Artificial Bee Colony (ABC) algorithm is a Nature
Inspired Algorithm (NIA) which based on intelligent food
foraging behaviour of honey bee swarm. This paper introduces
a local search strategy that enhances exploration competence
of ABC and avoids the problem of stagnation. The proposed
strategy introduces two new local search phases in original
ABC. One just after onlooker bee phase and one after scout
bee phase. The newly introduced phases are inspired by
modified Golden Section Search (GSS) strategy. The proposed
strategy named as new local search strategy in ABC
(NLSSABC). The proposed NLSSABC algorithm applied over
thirteen standard benchmark functions in order to prove its
efficiency.
Artificial Bee Colony (ABC) algorithm is a Nature Inspired Algorithm (NIA) which based in intelligent food foraging behaviour of honey bee swarm. ABC outperformed over other NIAs and other local search heuristics when tested for benchmark functions as well as factual world problems but occasionally it shows premature convergence and stagnation due to lack of balance between exploration and exploitation. This paper establishes a local search mechanism that enhances exploration capability of ABC and avoids the dilemma of stagnation. With help of recently introduces local search strategy it tries to balance intensification and diversification of search space. The anticipated algorithm named as Enhanced local search in ABC (EnABC) and tested over eleven benchmark functions. Results are evidence for its dominance over other competitive algorithms.
Artificial Bee Colony (ABC) is a swarm
optimization technique. This algorithm generally used to solve
nonlinear and complex problems. ABC is one of the simplest
and up to date population based probabilistic strategy for
global optimization. Analogous to other population based
algorithms, ABC also has some drawbacks computationally
pricey due to its sluggish temperament of search procedure.
The solution search equation of ABC is notably motivated by a
haphazard quantity which facilitates in exploration at the cost
of exploitation of the search space. Due to the large step size in
the solution search equation of ABC there are chances of
skipping the factual solution are higher. For that reason, this
paper introduces a new search strategy in order to balance the
diversity and convergence capability of the ABC. Both
employed bee phase and onlooker bee phase are improved
with help of a local search strategy stimulated by memetic
algorithm. This paper also proposes a new strategy for fitness
calculation and probability calculation. The proposed
algorithm is named as Improved Memetic Search in ABC
(IMeABC). It is tested over 13 impartial benchmark functions
of different complexities and two real word problems are also
considered to prove proposed algorithms superiority over
original ABC algorithm and its recent variants
The document discusses a proposed Randomized Memetic Artificial Bee Colony (RMABC) algorithm for optimization problems. RMABC incorporates local search techniques into the Artificial Bee Colony algorithm to improve exploitation of promising solutions. It randomizes the step size in the local search to balance diversification and intensification. Experimental results on benchmark problems show RMABC outperforms other ABC algorithm variants in finding optimal solutions. The document provides background on optimization problems, nature-inspired algorithms, Artificial Bee Colony algorithm, and Memetic algorithms.
Spider Monkey optimization (SMO) algorithm is newest addition in class of swarm intelligence. SMO is a population based stochastic meta-heuristic. It is motivated by intelligent foraging behaviour of fission fusion structured social creatures. SMO is a very good option for complex optimization problems. This paper proposed a modified strategy in order to enhance performance of original SMO. This paper introduces a position update strategy in SMO and modifies both local leader and global leader phase. The proposed strategy is named as Modified Position Update in Spider Monkey Optimization (MPU-SMO) algorithm. The proposed algorithm tested over benchmark problems and results show that it gives better results for considered unbiased problems.
Artificial Bee Colony (ABC) is a distinguished optimization strategy that can resolve nonlinear and multifaceted problems. It is comparatively a straightforward and modern population based probabilistic approach for comprehensive optimization. In the vein of the other population based algorithms, ABC is moreover computationally classy due to its slow nature of search procedure. The solution exploration equation of ABC is extensively influenced by a arbitrary quantity which helps in exploration at the cost of exploitation of the better search space. In the solution exploration equation of ABC due to the outsized step size the chance of skipping the factual solution is high. Therefore, here this paper improve onlooker bee phase with help of a local search strategy inspired by memetic algorithm to balance the diversity and convergence capability of the ABC. The proposed algorithm is named as Improved Onlooker Bee Phase in ABC (IoABC). It is tested over 12 well known un-biased test problems of diverse complexities and two engineering optimization problems; results show that the anticipated algorithm go one better than the basic ABC and its recent deviations in a good number of the experiments.
Artificial bee colony (ABC) algorithm has proved its importance in solving a number of problems including engineering optimization problems. ABC algorithm is one of the most popular and youngest member of the family of population based nature inspired meta-heuristic swarm intelligence method. ABC has been proved its superiority over some other Nature Inspired Algorithms (NIA) when applied for both benchmark functions and real world problems. The performance of search process of ABC depends on a random value which tries to balance exploration and exploitation phase. In order to increase the performance it is required to balance the exploration of search space and exploitation of optimal solution of the ABC. This paper outlines a new hybrid of ABC algorithm with Genetic Algorithm. The proposed method integrates crossover operation from Genetic Algorithm (GA) with original ABC algorithm. The proposed method is named as Crossover based ABC (CbABC). The CbABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space. The CbABC tested over four standard benchmark functions and a popular continuous optimization problem.
Artificial bee colony (ABC) algorithm is a well known and one of the latest swarm intelligence based techniques. This method is a population based meta-heuristic algorithm used for numerical optimization. It is based on the intelligent behavior of honey bees. Artificial Bee Colony algorithm is one of the most popular techniques that are used in optimization problems. Artificial Bee Colony algorithm has some major advantages over other heuristic methods. To utilize its good feature a number of researchers combined ABC algorithm with other methods, and generate some new hybrid methods. This paper provides comparative analysis of hybrid differential Artificial Bee Colony algorithm with hybrid ABC – SPSO, Genetic algorithm and Independent rough set approach based on some parameters like technique, dimension, methodology etc. KEYWORDS
A Novel Approach of Image Ranking based on Enhanced Artificial Bee Colony Alg...ijsrd.com
In recent years researchers have provided novel problem solving techniques based on swarm intelligence for solving difficult real world problems such as traffic, routing, networking, games, industries and economics. Artificial bee colony algorithm (ABC) was first developed by Dervis Karaboga [1]. When the robust performance is desired by means of searching something, the swarms does it better; by adaptation of greedy selection and random search. The ABC algorithm simulates the foraging behavior of honey bees. The local search in two stages in each step and global search are responsible for making this algorithm a robust search technique. The details of this algorithm are discussed here. Because of its very strong search process, computational simplicity and ease of modification according to the problem, the ABC algorithm is now finding more widespread applications in business, scientific and engineering circles. In this paper, we provide a thorough and extensive overview of most research work focusing on the application of ABC, with the expectation that it would serve as a reference material to both old and new, incoming researchers to the field, to support their understanding of current trends and assist their future research prospects and directions. Also new proposed architecture of Enhanced ABC algorithm for image ranking is also given here.
Artificial Bee Colony (ABC) algorithm is a Nature Inspired Algorithm (NIA) which based in intelligent food foraging behaviour of honey bee swarm. ABC outperformed over other NIAs and other local search heuristics when tested for benchmark functions as well as factual world problems but occasionally it shows premature convergence and stagnation due to lack of balance between exploration and exploitation. This paper establishes a local search mechanism that enhances exploration capability of ABC and avoids the dilemma of stagnation. With help of recently introduces local search strategy it tries to balance intensification and diversification of search space. The anticipated algorithm named as Enhanced local search in ABC (EnABC) and tested over eleven benchmark functions. Results are evidence for its dominance over other competitive algorithms.
Artificial Bee Colony (ABC) is a swarm
optimization technique. This algorithm generally used to solve
nonlinear and complex problems. ABC is one of the simplest
and up to date population based probabilistic strategy for
global optimization. Analogous to other population based
algorithms, ABC also has some drawbacks computationally
pricey due to its sluggish temperament of search procedure.
The solution search equation of ABC is notably motivated by a
haphazard quantity which facilitates in exploration at the cost
of exploitation of the search space. Due to the large step size in
the solution search equation of ABC there are chances of
skipping the factual solution are higher. For that reason, this
paper introduces a new search strategy in order to balance the
diversity and convergence capability of the ABC. Both
employed bee phase and onlooker bee phase are improved
with help of a local search strategy stimulated by memetic
algorithm. This paper also proposes a new strategy for fitness
calculation and probability calculation. The proposed
algorithm is named as Improved Memetic Search in ABC
(IMeABC). It is tested over 13 impartial benchmark functions
of different complexities and two real word problems are also
considered to prove proposed algorithms superiority over
original ABC algorithm and its recent variants
The document discusses a proposed Randomized Memetic Artificial Bee Colony (RMABC) algorithm for optimization problems. RMABC incorporates local search techniques into the Artificial Bee Colony algorithm to improve exploitation of promising solutions. It randomizes the step size in the local search to balance diversification and intensification. Experimental results on benchmark problems show RMABC outperforms other ABC algorithm variants in finding optimal solutions. The document provides background on optimization problems, nature-inspired algorithms, Artificial Bee Colony algorithm, and Memetic algorithms.
Spider Monkey optimization (SMO) algorithm is newest addition in class of swarm intelligence. SMO is a population based stochastic meta-heuristic. It is motivated by intelligent foraging behaviour of fission fusion structured social creatures. SMO is a very good option for complex optimization problems. This paper proposed a modified strategy in order to enhance performance of original SMO. This paper introduces a position update strategy in SMO and modifies both local leader and global leader phase. The proposed strategy is named as Modified Position Update in Spider Monkey Optimization (MPU-SMO) algorithm. The proposed algorithm tested over benchmark problems and results show that it gives better results for considered unbiased problems.
Artificial Bee Colony (ABC) is a distinguished optimization strategy that can resolve nonlinear and multifaceted problems. It is comparatively a straightforward and modern population based probabilistic approach for comprehensive optimization. In the vein of the other population based algorithms, ABC is moreover computationally classy due to its slow nature of search procedure. The solution exploration equation of ABC is extensively influenced by a arbitrary quantity which helps in exploration at the cost of exploitation of the better search space. In the solution exploration equation of ABC due to the outsized step size the chance of skipping the factual solution is high. Therefore, here this paper improve onlooker bee phase with help of a local search strategy inspired by memetic algorithm to balance the diversity and convergence capability of the ABC. The proposed algorithm is named as Improved Onlooker Bee Phase in ABC (IoABC). It is tested over 12 well known un-biased test problems of diverse complexities and two engineering optimization problems; results show that the anticipated algorithm go one better than the basic ABC and its recent deviations in a good number of the experiments.
Artificial bee colony (ABC) algorithm has proved its importance in solving a number of problems including engineering optimization problems. ABC algorithm is one of the most popular and youngest member of the family of population based nature inspired meta-heuristic swarm intelligence method. ABC has been proved its superiority over some other Nature Inspired Algorithms (NIA) when applied for both benchmark functions and real world problems. The performance of search process of ABC depends on a random value which tries to balance exploration and exploitation phase. In order to increase the performance it is required to balance the exploration of search space and exploitation of optimal solution of the ABC. This paper outlines a new hybrid of ABC algorithm with Genetic Algorithm. The proposed method integrates crossover operation from Genetic Algorithm (GA) with original ABC algorithm. The proposed method is named as Crossover based ABC (CbABC). The CbABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space. The CbABC tested over four standard benchmark functions and a popular continuous optimization problem.
Artificial bee colony (ABC) algorithm is a well known and one of the latest swarm intelligence based techniques. This method is a population based meta-heuristic algorithm used for numerical optimization. It is based on the intelligent behavior of honey bees. Artificial Bee Colony algorithm is one of the most popular techniques that are used in optimization problems. Artificial Bee Colony algorithm has some major advantages over other heuristic methods. To utilize its good feature a number of researchers combined ABC algorithm with other methods, and generate some new hybrid methods. This paper provides comparative analysis of hybrid differential Artificial Bee Colony algorithm with hybrid ABC – SPSO, Genetic algorithm and Independent rough set approach based on some parameters like technique, dimension, methodology etc. KEYWORDS
A Novel Approach of Image Ranking based on Enhanced Artificial Bee Colony Alg...ijsrd.com
In recent years researchers have provided novel problem solving techniques based on swarm intelligence for solving difficult real world problems such as traffic, routing, networking, games, industries and economics. Artificial bee colony algorithm (ABC) was first developed by Dervis Karaboga [1]. When the robust performance is desired by means of searching something, the swarms does it better; by adaptation of greedy selection and random search. The ABC algorithm simulates the foraging behavior of honey bees. The local search in two stages in each step and global search are responsible for making this algorithm a robust search technique. The details of this algorithm are discussed here. Because of its very strong search process, computational simplicity and ease of modification according to the problem, the ABC algorithm is now finding more widespread applications in business, scientific and engineering circles. In this paper, we provide a thorough and extensive overview of most research work focusing on the application of ABC, with the expectation that it would serve as a reference material to both old and new, incoming researchers to the field, to support their understanding of current trends and assist their future research prospects and directions. Also new proposed architecture of Enhanced ABC algorithm for image ranking is also given here.
An efficient and powerful advanced algorithm for solving real coded numerica...IOSR Journals
This document discusses an artificial bee colony algorithm with crossover operators for solving numerical optimization problems. The algorithm is based on the intelligent behavior of honey bee swarms. It introduces crossover operations between individual food source positions to generate new offspring. The offspring replace parents if they have better fitness. The algorithm is tested on standard benchmark functions like Griewank and Rosenbrock and results compared to an X-ABC algorithm. Results show the ABC with crossover performs better with fewer parameters.
AUTOMATED TEST CASE GENERATION AND OPTIMIZATION: A COMPARATIVE REVIEWijcsit
Software testing is the primary phase, which is performed during software development and it is carried by a sequence of instructions of test inputs followed by expected output. Evolutionary algorithms are most popular in the computational field based on population. The test case generation process is used to identify
test cases with resources and also identifies critical domain requirements. The behavior of bees is based on
population and evolutionary method. Bee Colony algorithm (BCA) has gained superiority in comparison to other algorithms in the field of computation. The Harmony Search (HS) algorithm is based on the enhancement process of music. When musicians compose the harmony through different possible combinations of the music, at that time the pitches are stored in the harmony memory and the optimization
can be done by adjusting the input pitches and generate the perfect harmony. Particle Swarm Optimization (PSO) is an intelligence based meta-heuristic algorithm where each particle can locate their source of food at different position.. In this algorithm, the particles will search for a better food source position in the hope of getting a better result. In this paper, the role of Artificial Bee Colony, particle swarm optimization
and harmony search algorithms are analyzed in generating random test data and optimized those test data.
Test case generation and optimization through bee colony, PSO and harmony search (HS) algorithms which are applied through a case study, i.e., withdrawal operation in Bank ATM and it is observed that these algorithms are able to generate suitable automated test cases or test data in a client manner. This
section further gives the brief details and compares between HS, PSO, and Bee Colony (BC) Optimization
methods which are used for test case or test data generation and optimization.
Articial bee Colony algorithm (ABC) is a population based
heuristic search technique used for optimization problems. ABC
is a very eective optimization technique for continuous opti-
mization problem. Crossover operators have a better exploration
property so crossover operators are added to the ABC. This pa-
per presents ABC with dierent types of real coded crossover op-
erator and its application to Travelling Salesman Problem (TSP).
Each crossover operator is applied to two randomly selected par-
ents from current swarm. Two o-springs generated from crossover
and worst parent is replaced by best ospring, other parent remains
same. ABC with real coded crossover operator applied to travelling
salesman problem. The experimental result shows that our proposed
algorithm performs better than the ABC without crossover in terms
of eciency and accuracy.
The document proposes a hybrid algorithm combining genetic algorithm and cuckoo search optimization to solve job shop scheduling problems. It aims to minimize makespan (completion time of all jobs) by scheduling jobs on machines. The genetic algorithm is used to explore the search space but can get trapped in local optima. Cuckoo search optimization performs local search faster than genetic algorithm and helps avoid local optima. Experimental results on benchmark problems show the hybrid algorithm yields better solutions in terms of makespan and runtime compared to genetic algorithm and ant colony optimization algorithms.
MOCANAR: A Multi-Objective Cuckoo Search Algorithm for Numeric Association Ru...csandit
Extracting association rules from numeric features
involves searching a very large search space. To
deal with this problem, in this paper a meta-heuris
tic algorithm is used that we have called
MOCANAR. The MOCANAR is a Pareto based multi-object
ive cuckoo search algorithm which
extracts high quality association rules from numeri
c datasets. The support, confidence,
interestingness and comprehensibility are the objec
tives that have been considered in the
MOCANAR. The MOCANAR extracts rules incrementally,
in which, in each run of the algorithm, a
small number of high quality rules are made. In thi
s paper, a comprehensive taxonomy of meta-
heuristic algorithm have been presented. Using this
taxonomy, we have decided to use a Cuckoo
Search algorithm because this algorithm is one of t
he most matured algorithms and also, it is simple
to use and easy to comprehend. In addition, until n
ow, to our knowledge this method has not been
used as a multi-objective algorithm and has not bee
n used in the association rule mining area. To
demonstrate the merit and associated benefits of th
e proposed methodology, the methodology has
been applied to a number of datasets and high quali
ty results in terms of the objectives were
extracted
Solving np hard problem using artificial bee colony algorithmIAEME Publication
The document presents an artificial bee colony (ABC) algorithm to solve the NP-hard shortest common supersequence problem. The ABC algorithm is inspired by the foraging behavior of honey bees. It represents solutions as food sources and uses employed, onlooker, and scout bees to explore the search space. The algorithm calculates character frequencies in input strings to guide random supersequence generation. Fitness is evaluated by comparing sequences using a modified merge algorithm. Results show the ABC approach finds near-optimal solutions compared to other algorithms for solving shortest common supersequences.
Predicting of Hosting Animal Centre Outcome Based on Supervised Machine Learn...sushantparte
This document is a research project submission for a MSc in Data Analytics at the National College of Ireland. The project aims to use supervised machine learning models to predict animal shelter outcomes using a dataset from the Austin Animal Center. Four classification models - logistic regression, neural network, XGboost, and random forest - are implemented and evaluated based on metrics like accuracy, logarithmic loss, sensitivity and specificity. The best performing model is found to be XGboost, which achieves an accuracy of 65.33% on the Austin animal shelter outcomes dataset.
EVOLUTIONARY COMPUTING TECHNIQUES FOR SOFTWARE EFFORT ESTIMATIONijcsit
Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
The document discusses using a genetic algorithm to solve the complex problem of university course scheduling. It describes the problem which involves assigning courses, professors, classrooms and time slots while satisfying various hard and soft constraints. A phased approach is proposed which first assigns professors to subjects, then labs to courses, followed by assigning lectures to time slots and labs/tutorials to days and time slots. The genetic algorithm representation and fitness function are defined based on the scheduling problem. The approach is demonstrated on the course scheduling problem at Christ University and is able to generate a timetable that satisfies the hard constraints, though some soft constraints remain unsatisfied.
This paper describes using a genetic algorithm to teach a simulated three-legged creature to walk. The creature, called a Tripod, lives in a 3D physics simulation. Its goal is to travel as far as possible within 30 seconds. A genetic algorithm modifies the Tripod's joint movements over generations to maximize its fitness score of distance traveled. However, accurately analyzing the genetic algorithm's convergence properties is challenging due to the complex simulation and lack of mathematical models describing the Tripod's movement patterns. The paper discusses various challenges in applying standard genetic algorithm performance analysis methods to this problem.
A Non-Revisiting Genetic Algorithm for Optimizing Numeric Multi-Dimensional F...ijcsa
The document describes a non-revisiting genetic algorithm with adaptive mutation for optimizing multi-dimensional numeric functions. The proposed algorithm avoids revisiting previously evaluated solutions by replacing any duplicates with a mutated version of either the best or random individual. The mutation rate adapts over generations, starting with more exploration and ending with more exploitation. Experimental results on 9 benchmark functions in 2 and 4 dimensions show the proposed algorithm achieves better best and average fitness than a standard genetic algorithm, with improvements ranging from 10-98% depending on the function.
Artificial bee colony with fcm for data clusteringAlie Banyuripan
This document summarizes a research paper that proposes a modified artificial bee colony (ABC) algorithm for data clustering. The algorithm integrates a fuzzy C-means (FCM) operator into the ABC algorithm. Specifically:
- The ABC algorithm is modified to incorporate an FCM operator, which introduces scout bees based on a fuzzy function rather than randomly.
- In each cycle, after the employed bee and onlooker bee phases, the FCM operator generates a new solution for the scout bees based on previous solutions to improve optimization.
- The algorithm was tested on datasets from a machine learning repository and showed improved clustering quality compared to existing algorithms.
A hybrid optimization algorithm based on genetic algorithm and ant colony opt...ijaia
In optimization problem, Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) have
been known as good alternative techniques. GA is designed by adopting the natural evolution process,
while ACO is inspired by the foraging behaviour of ant species. This paper presents a hybrid GA-ACO for
Travelling Salesman Problem (TSP), called Genetic Ant Colony Optimization (GACO). In this method, GA
will observe and preserve the fittest ant in each cycle in every generation and only unvisited cities will be
assessed by ACO. From experimental result, GACO performance is significantly improved and its time
complexity is fairly equal compared to the GA and ACO.
COMPARISON BETWEEN ARTIFICIAL BEE COLONY ALGORITHM, SHUFFLED FROG LEAPING ALG...csandit
In this paper three optimum approaches to design PID controller for a Gryphon Robot are
presented. The three applied approaches are Artificial Bee Colony, Shuffled Frog Leaping
algorithms and nero-fuzzy system. The design goal is to minimize the integral absolute error
and reduce transient response by minimizing overshoot, settling time and rise time of step
response. An Objective function of these indexes is defined and minimized applying Shuffled
Frog Leaping (SFL) algorithm, Artificial Bee Colony (ABC) algorithm and Nero-Fuzzy System
(FNN). After optimization of the objective function, the optimal parameters for the PID
controller are adjusted. Simulation results show that FNN has a remarkable effect on
decreasing the amount of settling time and rise-time and eliminating of steady-state error while
the SFL algorithm performs better on steady-state error and the ABC algorithm is better on
decreasing of overshoot. In steady state manner, all of the methods react robustly to the
disturbance, but FNN shows more stability in transient response.
The document describes the Bees Algorithm, which is an optimization algorithm inspired by the foraging behavior of honey bees. It begins with randomly placed scout bees that evaluate potential solutions. The best sites are selected and more bees are recruited to explore near those locations. The fittest bees are kept and the process repeats until termination criteria is met. The algorithm aims to efficiently locate good solutions like honey bees locating food sources. It can be applied to problems with multiple optimal solutions.
The document summarizes furniture designed for Indian classrooms with input from experienced Japanese designers. It focuses on comfort, safety, and durability. Key features include rounded edges for safety, imported plywood and laminate tops for easy cleaning, and Japanese rivets to join seat and back frames. The furniture undergoes rigorous product testing and is manufactured in a fully integrated facility.
This document discusses the bee algorithm, which is an optimization technique inspired by the foraging behavior of honey bees. It begins with an introduction and overview of concepts like nature of bees, hill climbing, swarm intelligence, and bee colony optimization. It then describes the key steps of the proposed bee algorithm, including initializing a population of solutions, evaluating their fitness, selecting sites for neighborhood search, recruiting bees to search those sites, and iterating until an optimal solution is found. An example application to a traveling salesperson problem is provided. The document concludes that bee algorithm can help provide an optimal solution for problems with many possible solutions, such as in artificial intelligence applications.
This document provides an overview of the Artificial Bee Colony (ABC) algorithm. It describes how ABC was inspired by the foraging behavior of honey bees. The core components of the ABC algorithm are introduced, including the initialization phase, employed bee phase, onlooker bee phase, and scout bee phase. Pseudocode and a flowchart depicting the steps of the ABC algorithm are presented. Applications of ABC in areas such as optimization, bioinformatics, scheduling, clustering, and engineering are discussed. Finally, the advantages of ABC like simplicity and flexibility are contrasted with limitations such as high computational cost.
The document summarizes the artificial bee colony (ABC) algorithm, which was introduced in 2005 and is inspired by the foraging behavior of honeybee swarms. The ABC algorithm simulates three groups of bees - employed bees, onlookers, and scouts - to solve optimization problems. It involves phases of employed bee search, onlooker bee choice, and scout bee recruitment to balance exploration and exploitation. The ABC algorithm has few parameters and fast convergence but is limited by its initial solutions. Variations include multi-objective ABC algorithms and parameter studies on swarm size, limit, and dimension.
The document describes the Social Spider Optimization (SSO) algorithm, which is inspired by the behaviors of social spiders. The SSO algorithm initializes a population of solutions represented as spiders (both male and female). It then evaluates the fitness of each solution and models vibrations transmitted through a communal web. Operators like female cooperation, male cooperation, and mating are applied to update solution positions. The algorithm iterates until termination criteria are met, with the goal of finding high-quality solutions represented by heavier/fitter spiders.
This document discusses the bee algorithm, which is an optimization algorithm inspired by the decision-making process of honey bees. It begins with an introduction and outline, then describes how bees communicate through waggle dances to share information about food sources. The bee algorithm mimics this process by initializing solutions, evaluating fitness, selecting sites for neighborhood search, and recruiting bees to explore solutions. An example application to mathematical function optimization is provided to illustrate the algorithm. Potential applications discussed include training neural networks, load balancing in cloud computing, and more.
An efficient and powerful advanced algorithm for solving real coded numerica...IOSR Journals
This document discusses an artificial bee colony algorithm with crossover operators for solving numerical optimization problems. The algorithm is based on the intelligent behavior of honey bee swarms. It introduces crossover operations between individual food source positions to generate new offspring. The offspring replace parents if they have better fitness. The algorithm is tested on standard benchmark functions like Griewank and Rosenbrock and results compared to an X-ABC algorithm. Results show the ABC with crossover performs better with fewer parameters.
AUTOMATED TEST CASE GENERATION AND OPTIMIZATION: A COMPARATIVE REVIEWijcsit
Software testing is the primary phase, which is performed during software development and it is carried by a sequence of instructions of test inputs followed by expected output. Evolutionary algorithms are most popular in the computational field based on population. The test case generation process is used to identify
test cases with resources and also identifies critical domain requirements. The behavior of bees is based on
population and evolutionary method. Bee Colony algorithm (BCA) has gained superiority in comparison to other algorithms in the field of computation. The Harmony Search (HS) algorithm is based on the enhancement process of music. When musicians compose the harmony through different possible combinations of the music, at that time the pitches are stored in the harmony memory and the optimization
can be done by adjusting the input pitches and generate the perfect harmony. Particle Swarm Optimization (PSO) is an intelligence based meta-heuristic algorithm where each particle can locate their source of food at different position.. In this algorithm, the particles will search for a better food source position in the hope of getting a better result. In this paper, the role of Artificial Bee Colony, particle swarm optimization
and harmony search algorithms are analyzed in generating random test data and optimized those test data.
Test case generation and optimization through bee colony, PSO and harmony search (HS) algorithms which are applied through a case study, i.e., withdrawal operation in Bank ATM and it is observed that these algorithms are able to generate suitable automated test cases or test data in a client manner. This
section further gives the brief details and compares between HS, PSO, and Bee Colony (BC) Optimization
methods which are used for test case or test data generation and optimization.
Articial bee Colony algorithm (ABC) is a population based
heuristic search technique used for optimization problems. ABC
is a very eective optimization technique for continuous opti-
mization problem. Crossover operators have a better exploration
property so crossover operators are added to the ABC. This pa-
per presents ABC with dierent types of real coded crossover op-
erator and its application to Travelling Salesman Problem (TSP).
Each crossover operator is applied to two randomly selected par-
ents from current swarm. Two o-springs generated from crossover
and worst parent is replaced by best ospring, other parent remains
same. ABC with real coded crossover operator applied to travelling
salesman problem. The experimental result shows that our proposed
algorithm performs better than the ABC without crossover in terms
of eciency and accuracy.
The document proposes a hybrid algorithm combining genetic algorithm and cuckoo search optimization to solve job shop scheduling problems. It aims to minimize makespan (completion time of all jobs) by scheduling jobs on machines. The genetic algorithm is used to explore the search space but can get trapped in local optima. Cuckoo search optimization performs local search faster than genetic algorithm and helps avoid local optima. Experimental results on benchmark problems show the hybrid algorithm yields better solutions in terms of makespan and runtime compared to genetic algorithm and ant colony optimization algorithms.
MOCANAR: A Multi-Objective Cuckoo Search Algorithm for Numeric Association Ru...csandit
Extracting association rules from numeric features
involves searching a very large search space. To
deal with this problem, in this paper a meta-heuris
tic algorithm is used that we have called
MOCANAR. The MOCANAR is a Pareto based multi-object
ive cuckoo search algorithm which
extracts high quality association rules from numeri
c datasets. The support, confidence,
interestingness and comprehensibility are the objec
tives that have been considered in the
MOCANAR. The MOCANAR extracts rules incrementally,
in which, in each run of the algorithm, a
small number of high quality rules are made. In thi
s paper, a comprehensive taxonomy of meta-
heuristic algorithm have been presented. Using this
taxonomy, we have decided to use a Cuckoo
Search algorithm because this algorithm is one of t
he most matured algorithms and also, it is simple
to use and easy to comprehend. In addition, until n
ow, to our knowledge this method has not been
used as a multi-objective algorithm and has not bee
n used in the association rule mining area. To
demonstrate the merit and associated benefits of th
e proposed methodology, the methodology has
been applied to a number of datasets and high quali
ty results in terms of the objectives were
extracted
Solving np hard problem using artificial bee colony algorithmIAEME Publication
The document presents an artificial bee colony (ABC) algorithm to solve the NP-hard shortest common supersequence problem. The ABC algorithm is inspired by the foraging behavior of honey bees. It represents solutions as food sources and uses employed, onlooker, and scout bees to explore the search space. The algorithm calculates character frequencies in input strings to guide random supersequence generation. Fitness is evaluated by comparing sequences using a modified merge algorithm. Results show the ABC approach finds near-optimal solutions compared to other algorithms for solving shortest common supersequences.
Predicting of Hosting Animal Centre Outcome Based on Supervised Machine Learn...sushantparte
This document is a research project submission for a MSc in Data Analytics at the National College of Ireland. The project aims to use supervised machine learning models to predict animal shelter outcomes using a dataset from the Austin Animal Center. Four classification models - logistic regression, neural network, XGboost, and random forest - are implemented and evaluated based on metrics like accuracy, logarithmic loss, sensitivity and specificity. The best performing model is found to be XGboost, which achieves an accuracy of 65.33% on the Austin animal shelter outcomes dataset.
EVOLUTIONARY COMPUTING TECHNIQUES FOR SOFTWARE EFFORT ESTIMATIONijcsit
Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
The document discusses using a genetic algorithm to solve the complex problem of university course scheduling. It describes the problem which involves assigning courses, professors, classrooms and time slots while satisfying various hard and soft constraints. A phased approach is proposed which first assigns professors to subjects, then labs to courses, followed by assigning lectures to time slots and labs/tutorials to days and time slots. The genetic algorithm representation and fitness function are defined based on the scheduling problem. The approach is demonstrated on the course scheduling problem at Christ University and is able to generate a timetable that satisfies the hard constraints, though some soft constraints remain unsatisfied.
This paper describes using a genetic algorithm to teach a simulated three-legged creature to walk. The creature, called a Tripod, lives in a 3D physics simulation. Its goal is to travel as far as possible within 30 seconds. A genetic algorithm modifies the Tripod's joint movements over generations to maximize its fitness score of distance traveled. However, accurately analyzing the genetic algorithm's convergence properties is challenging due to the complex simulation and lack of mathematical models describing the Tripod's movement patterns. The paper discusses various challenges in applying standard genetic algorithm performance analysis methods to this problem.
A Non-Revisiting Genetic Algorithm for Optimizing Numeric Multi-Dimensional F...ijcsa
The document describes a non-revisiting genetic algorithm with adaptive mutation for optimizing multi-dimensional numeric functions. The proposed algorithm avoids revisiting previously evaluated solutions by replacing any duplicates with a mutated version of either the best or random individual. The mutation rate adapts over generations, starting with more exploration and ending with more exploitation. Experimental results on 9 benchmark functions in 2 and 4 dimensions show the proposed algorithm achieves better best and average fitness than a standard genetic algorithm, with improvements ranging from 10-98% depending on the function.
Artificial bee colony with fcm for data clusteringAlie Banyuripan
This document summarizes a research paper that proposes a modified artificial bee colony (ABC) algorithm for data clustering. The algorithm integrates a fuzzy C-means (FCM) operator into the ABC algorithm. Specifically:
- The ABC algorithm is modified to incorporate an FCM operator, which introduces scout bees based on a fuzzy function rather than randomly.
- In each cycle, after the employed bee and onlooker bee phases, the FCM operator generates a new solution for the scout bees based on previous solutions to improve optimization.
- The algorithm was tested on datasets from a machine learning repository and showed improved clustering quality compared to existing algorithms.
A hybrid optimization algorithm based on genetic algorithm and ant colony opt...ijaia
In optimization problem, Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) have
been known as good alternative techniques. GA is designed by adopting the natural evolution process,
while ACO is inspired by the foraging behaviour of ant species. This paper presents a hybrid GA-ACO for
Travelling Salesman Problem (TSP), called Genetic Ant Colony Optimization (GACO). In this method, GA
will observe and preserve the fittest ant in each cycle in every generation and only unvisited cities will be
assessed by ACO. From experimental result, GACO performance is significantly improved and its time
complexity is fairly equal compared to the GA and ACO.
COMPARISON BETWEEN ARTIFICIAL BEE COLONY ALGORITHM, SHUFFLED FROG LEAPING ALG...csandit
In this paper three optimum approaches to design PID controller for a Gryphon Robot are
presented. The three applied approaches are Artificial Bee Colony, Shuffled Frog Leaping
algorithms and nero-fuzzy system. The design goal is to minimize the integral absolute error
and reduce transient response by minimizing overshoot, settling time and rise time of step
response. An Objective function of these indexes is defined and minimized applying Shuffled
Frog Leaping (SFL) algorithm, Artificial Bee Colony (ABC) algorithm and Nero-Fuzzy System
(FNN). After optimization of the objective function, the optimal parameters for the PID
controller are adjusted. Simulation results show that FNN has a remarkable effect on
decreasing the amount of settling time and rise-time and eliminating of steady-state error while
the SFL algorithm performs better on steady-state error and the ABC algorithm is better on
decreasing of overshoot. In steady state manner, all of the methods react robustly to the
disturbance, but FNN shows more stability in transient response.
The document describes the Bees Algorithm, which is an optimization algorithm inspired by the foraging behavior of honey bees. It begins with randomly placed scout bees that evaluate potential solutions. The best sites are selected and more bees are recruited to explore near those locations. The fittest bees are kept and the process repeats until termination criteria is met. The algorithm aims to efficiently locate good solutions like honey bees locating food sources. It can be applied to problems with multiple optimal solutions.
The document summarizes furniture designed for Indian classrooms with input from experienced Japanese designers. It focuses on comfort, safety, and durability. Key features include rounded edges for safety, imported plywood and laminate tops for easy cleaning, and Japanese rivets to join seat and back frames. The furniture undergoes rigorous product testing and is manufactured in a fully integrated facility.
This document discusses the bee algorithm, which is an optimization technique inspired by the foraging behavior of honey bees. It begins with an introduction and overview of concepts like nature of bees, hill climbing, swarm intelligence, and bee colony optimization. It then describes the key steps of the proposed bee algorithm, including initializing a population of solutions, evaluating their fitness, selecting sites for neighborhood search, recruiting bees to search those sites, and iterating until an optimal solution is found. An example application to a traveling salesperson problem is provided. The document concludes that bee algorithm can help provide an optimal solution for problems with many possible solutions, such as in artificial intelligence applications.
This document provides an overview of the Artificial Bee Colony (ABC) algorithm. It describes how ABC was inspired by the foraging behavior of honey bees. The core components of the ABC algorithm are introduced, including the initialization phase, employed bee phase, onlooker bee phase, and scout bee phase. Pseudocode and a flowchart depicting the steps of the ABC algorithm are presented. Applications of ABC in areas such as optimization, bioinformatics, scheduling, clustering, and engineering are discussed. Finally, the advantages of ABC like simplicity and flexibility are contrasted with limitations such as high computational cost.
The document summarizes the artificial bee colony (ABC) algorithm, which was introduced in 2005 and is inspired by the foraging behavior of honeybee swarms. The ABC algorithm simulates three groups of bees - employed bees, onlookers, and scouts - to solve optimization problems. It involves phases of employed bee search, onlooker bee choice, and scout bee recruitment to balance exploration and exploitation. The ABC algorithm has few parameters and fast convergence but is limited by its initial solutions. Variations include multi-objective ABC algorithms and parameter studies on swarm size, limit, and dimension.
The document describes the Social Spider Optimization (SSO) algorithm, which is inspired by the behaviors of social spiders. The SSO algorithm initializes a population of solutions represented as spiders (both male and female). It then evaluates the fitness of each solution and models vibrations transmitted through a communal web. Operators like female cooperation, male cooperation, and mating are applied to update solution positions. The algorithm iterates until termination criteria are met, with the goal of finding high-quality solutions represented by heavier/fitter spiders.
This document discusses the bee algorithm, which is an optimization algorithm inspired by the decision-making process of honey bees. It begins with an introduction and outline, then describes how bees communicate through waggle dances to share information about food sources. The bee algorithm mimics this process by initializing solutions, evaluating fitness, selecting sites for neighborhood search, and recruiting bees to explore solutions. An example application to mathematical function optimization is provided to illustrate the algorithm. Potential applications discussed include training neural networks, load balancing in cloud computing, and more.
This document provides information about genetic algorithms including:
1. Definitions of genetic algorithms from Grefenstette and Goldberg that describe genetic algorithms as search algorithms based on biological evolution and natural selection.
2. An overview of genetic algorithms including the basic concepts of populations, chromosomes, genes, fitness functions, selection, crossover, and mutation.
3. Examples of genetic representations like binary encoding and permutation encoding.
4. Descriptions of genetic operators like selection, crossover, and mutation that maintain genetic diversity between generations.
List the problems that can be efficiently solved by Evolutionary P.pdfinfomalad
List the problems that can be efficiently solved by Evolutionary Programming?
List the problems that can be efficiently solved by Evolutionary Programming?
Solution
Evolutionary Programming is a Global Optimization algorithm and is an instance of an
Evolutionary Algorithm from the field of Evolutionary Computation. The approach is a sibling
of other Evolutionary Algorithms such as the Genetic Algorithm, and Learning Classifier
Systems. It is sometimes confused with Genetic Programming given the similarity in name, and
more recently it shows a strong functional similarity to Evolution Strategies.
Inspiration
Evolutionary Programming is inspired by the theory of evolution by means of natural selection.
Specifically, the technique is inspired by macro-level or the species-level process of evolution
(phenotype, hereditary, variation) and is not concerned with the genetic mechanisms of evolution
(genome, chromosomes, genes, alleles).
Metaphor
A population of a species reproduce, creating progeny with small phenotypical variation. The
progeny and the parents compete based on their suitability to the environment, where the
generally more fit members constitute the subsequent generation and are provided with the
opportunity to reproduce themselves. This process repeats, improving the adaptive fit between
the species and the environment.
Strategy
The objective of the Evolutionary Programming algorithm is to maximize the suitability of a
collection of candidate solutions in the context of an objective function from the domain. This
objective is pursued by using an adaptive model with surrogates for the processes of evolution,
specifically hereditary (reproduction with variation) under competition. The representation used
for candidate solutions is directly assessable by a cost or objective function from the domain.
Problems for evolutionary algorithms
Evolutionary programming are useful for problems for which no mechanistic method is available
– or when unreasonable assumptions need to be made. Problems for which you cannot calculate
or derive a solution. Problems for which you have to find a solution.
Aspects of problems that lend them to Evolutionary Programming solution include:
Assignment of individuals into groups, wherever simple ranking and truncation will not work.
This usually involves interactions, whereby whether an individual should be in a group depends
on what other individuals will be in that group. Example: developing multiplex groupings for
genotyping.
Problems where thresholds are involved, for example when the value of a solution depends on
whether certain thresholds have been passed, or the fate of an individual depends on passing one
or more thresholds. Example: Supply chain optimizing to target multiple product end-points
and/or turnoff dates.
Combinatorially tedious problems, where many combinations of components can exist. Example:
The setting up of animal matings. Below are the few applications:
Learning classifier systems, or .
Prediction of Euro 50 Using Back Propagation Neural Network (BPNN) and Geneti...AI Publications
Modeling time series is often associated with the process forecasts certain characteristics in the next period. One of the methods forecasts that developed nowadays is using artificial neural network or more popularly known as a neural network. Use neural network in forecasts time series can be a good solution, but the problem is network architecture and the training method in the right direction. One of the choices that might be using a genetic algorithm. A genetic algorithm is a search algorithm stochastic resonance based on how it works by the mechanisms of natural selection and genetic variation that aims to find a solution to a problem. This algorithm can be used as teaching methods in train models are sent back propagation neural network. The application genetic algorithm and neural network for divination time series aim to get the weight optimum. From the training and testing on the data index share price euro 50 obtained by the RMSE testing 27.8744 and 39.2852 RMSE training. The weight or parameters that produced by has reached an optimum level in second-generation 1000 with the best fitness and the average 0.027771 the fitness of 0.0027847.Model is good to be used to give a prediction that is quite accurate information that is shown by the close target with the output.
MOCANAR: A MULTI-OBJECTIVE CUCKOO SEARCH ALGORITHM FOR NUMERIC ASSOCIATION RU...cscpconf
Extracting association rules from numeric features involves searching a very large search space. To
deal with this problem, in this paper a meta-heuristic algorithm is used that we have called
MOCANAR. The MOCANAR is a Pareto based multi-objective cuckoo search algorithm which
extracts high quality association rules from numeric datasets. The support, confidence,
interestingness and comprehensibility are the objectives that have been considered in the
MOCANAR. The MOCANAR extracts rules incrementally, in which, in each run of the algorithm, a
small number of high quality rules are made. In this paper, a comprehensive taxonomy of metaheuristic
algorithm have been presented. Using this taxonomy, we have decided to use a Cuckoo
Search algorithm because this algorithm is one of the most matured algorithms and also, it is simple
to use and easy to comprehend. In addition, until now, to our knowledge this method has not been
used as a multi-objective algorithm and has not been used in the association rule mining area. To
demonstrate the merit and associated benefits of the proposed methodology, the methodology has
been applied to a number of datasets and high quality results in terms of the objectives were
extracted
Soft computing is an approach to construct intelligent systems that employs techniques like fuzzy logic, neural networks, evolutionary algorithms, and swarm intelligence. It aims to model human-like expertise by being adaptive, learning, and operating with uncertainty. Soft computing techniques are used together in a complementary rather than competitive way in hybrid systems. Genetic algorithms are a popular type of evolutionary algorithm used for optimization that is inspired by biological evolution, using techniques like selection, crossover, and mutation to evolve solutions over generations.
An Hybrid Learning Approach using Particle Intelligence Dynamics and Bacteri...IJMER
The foraging behavior of E. Coli is used for optimization problems. This paper is based on a
hybrid method that combines particle swarm optimization and bacterial foraging (BF) algorithm for
solution of optimization results. We applied this proposed algorithm on different closed loop transfer
functions and the performance of the system using time response for the optimum value of PID
parameters is studied with incorporating PSO method on mutation, crossover, step sizes, and chemotactic
of the bacteria during the foraging. The bacterial foraging particle swarm optimization (BFPSO)
algorithm is applied to tune the PID controller of type 2, 3 and 4 systems with consideration of minimum
peak overshoot and steady state error objective function. The performance of the time response is
evaluated for the designed PID controller as the integral of time weighted squared error. The results
illustrate that the proposed approach is more efficient and provides better results as compared to the
conventional PSO algorithm.
This document discusses how natural computation techniques can be applied to web usage mining. It begins by introducing web usage mining and its importance. It then provides an overview of various natural computation approaches, including artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, bacterial foraging, DNA computation, and hybrid approaches. The document explains how each of these natural computation techniques can inspire computational methods for analyzing web usage data.
This document discusses nature-inspired optimization techniques and their applications. It provides an overview of problems in real-world optimization that involve multiple conflicting objectives. Nature provides inspiration for algorithms that can solve complex problems with simple rules, as seen in animals. Examples of nature-inspired algorithms discussed include firefly algorithm, particle swarm optimization, ant colony optimization, cuckoo search, and others. These algorithms have applications in fields like engineering, cheminformatics, bioinformatics, and more.
Soft computing is an umbrella term used to describe types of algorithms that produce approximate solutions to unsolvable high-level problems in computer science.
GA is a search technique that depends on the natural selection and genetics principles and which determines a optimal solution for even a hard issue.genetic algorithm crossover and genetic algorithm for optimization
Machine learning techniques can be used to enable computers to learn from data and perform tasks. Some key techniques discussed in the document include decision tree learning, artificial neural networks, Bayesian learning, support vector machines, genetic algorithms, graph-based learning, reinforcement learning, and pattern recognition. Each technique has its own strengths and applications.
Survey on evolutionary computation tech techniques and its application in dif...ijitjournal
In computer science, 'evolutionary computation' is an algorithmic tool based on evolution. It implements
random variation, reproduction and selection by altering and moving data within a computer. It helps in
building, applying and studying algorithms based on the Darwinian principles of natural selection. In this
paper, studies about different evolutionary computation techniques used in some applications specifically
image processing, cloud computing and grid computing is carried out briefly. This work is an effort to help
researchers from different fields to have knowledge on the techniques of evolutionary computation
applicable in the above mentioned areas.
Jawad Ali discusses heuristic algorithms and search methods. A heuristic algorithm sacrifices optimality, accuracy, or completeness for speed in solving problems like NP-complete decision problems. Heuristic search iteratively improves solutions based on a heuristic function or cost measure, finding a good solution quickly without guaranteeing an optimal one. Examples given are swarm intelligence inspired by swarm movements in nature, tabu search preventing repetitive movements, and artificial neural networks inspired by the brain for pattern recognition. Heuristic algorithms have low time complexity and are applied to complex problems.
Nature-Inspired Mateheuristic Algorithms: Success and New Challenges Xin-She Yang
Nature-inspired metaheuristic algorithms mimic characteristics of natural systems to solve optimization problems. They have become popular due to their simplicity, ease of implementation, and ability to balance solution diversity and speed. However, there are still challenges in developing a theoretical framework and applying these algorithms to large-scale problems. Future work is needed to address these challenges and close the gap between theory and applications of metaheuristics.
This document discusses nature-inspired metaheuristic algorithms. It begins by classifying these algorithms into four categories: evolution-based, physics-based, swarm-based, and human-based. Evolution-based algorithms mimic natural evolution, physics-based algorithms mimic physical rules, swarm-based algorithms mimic group behaviors of animals, and human-based algorithms mimic human behaviors like teaching and learning. The document then provides examples of algorithms that fall into each category and describes their mechanisms. It notes that metaheuristic algorithms balance intensification to find local optima and diversification to find global optima. The document concludes by stating that metaheuristics are effective tools for optimization problems and their applications continue growing.
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
A comprehensive review of the firefly algorithmsXin-She Yang
This document provides a comprehensive review of firefly algorithms. It begins with background on swarm intelligence and how firefly algorithms were inspired by the flashing lights of fireflies. It then describes the basic structure of firefly algorithms, including initializing a population of fireflies, evaluating their fitness, sorting by fitness, selecting the best solution, and moving fireflies toward more attractive solutions over generations. The document reviews applications of firefly algorithms in areas like continuous, combinatorial, and multi-objective optimization as well as engineering problems. It concludes by discussing exploration vs exploitation in firefly algorithms and directions for further development.
This paper proposes a parallel evolutionary algorithm to solve single variable optimization problems. Specifically:
- It presents a genetic algorithm approach that runs in parallel using a master-slave model, where the master performs genetic operations and distributes individuals to slaves for evaluation.
- The algorithm is tested on single variable optimization problems to find minimum/maximum values.
- Experimental results show the parallel genetic algorithm is effective at finding optimal solutions to these problems and represents an efficient parallel approach for optimization.
A Binary Bat Inspired Algorithm for the Classification of Breast Cancer Data ijscai
This document summarizes a research paper that proposes using a binary bat algorithm to classify breast cancer data. The researchers developed a hybrid model combining a binary bat algorithm and feedforward neural network. The binary bat algorithm was used to generate a activation function for training the neural network and minimize error. Testing of the model on three breast cancer datasets produced an accuracy of 92.61% for training data and 89.95% for testing data, showing potential for classifying breast cancer as malignant or benign.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Similar to New Local Search Strategy in Artificial Bee Colony Algorithm (20)
This document outlines the syllabus for an MTCSCS302 course on Soft Computing taught by Dr. Sandeep Kumar Poonia. The course covers topics including neural networks, fuzzy logic, probabilistic reasoning, and genetic algorithms. It is divided into five units: (1) neural networks, (2) fuzzy logic, (3) fuzzy arithmetic and logic, (4) neuro-fuzzy systems and applications of fuzzy logic, and (5) genetic algorithms and their applications. The goal of the course is to provide students with knowledge of soft computing fundamentals and approaches for solving complex real-world problems.
Differential Evolution (DE) is a renowned optimization stratagem that can easily solve nonlinear and comprehensive problems. DE is a well known and uncomplicated population based probabilistic approach for comprehensive optimization. It has apparently outperformed a number of Evolutionary Algorithms and further search heuristics in the vein of Particle Swarm Optimization at what time of testing over both yardstick and actual world problems. Nevertheless, DE, like other probabilistic optimization algorithms, from time to time exhibits precipitate convergence and stagnates at suboptimal position. In order to stay away from stagnation behavior while maintaining an excellent convergence speed, an innovative search strategy is introduced, named memetic search in DE. In the planned strategy, positions update equation customized as per a memetic search stratagem. In this strategy a better solution participates more times in the position modernize procedure. The position update equation is inspired from the memetic search in artificial bee colony algorithm. The proposed strategy is named as Memetic Search in Differential Evolution (MSDE). To prove efficiency and efficacy of MSDE, it is tested over 8 benchmark optimization problems and three real world optimization problems. A comparative analysis has also been carried out among proposed MSDE and original DE. Results show that the anticipated algorithm go one better than the basic DE and its recent deviations in a good number of the experiments.
Multiplication of two 3 d sparse matrices using 1d arrays and linked listsDr Sandeep Kumar Poonia
A basic algorithm of 3D sparse matrix multiplication (BASMM) is presented using one dimensional (1D) arrays which is used further for multiplying two 3D sparse matrices using Linked Lists. In this algorithm, a general concept is derived in which we enter non- zeros elements in 1st and 2nd sparse matrices (3D) but store that values in 1D arrays and linked lists so that zeros could be removed or ignored to store in memory. The positions of that non-zero value are also stored in memory like row and column position. In this way space complexity is decreased. There are two ways to store the sparse matrix in memory. First is row major order and another is column major order. But, in this algorithm, row major order is used. Now multiplying those two matrices with the help of BASMM algorithm, time complexity also decreased. For the implementation of this, simple c programming and concepts of data structures are used which are very easy to understand for everyone.
This document summarizes a tool called Sunzip that uses the Huffman algorithm for data compression. It discusses how Huffman encoding works by assigning shorter bit codes to more common symbols to reduce file size. The tool analyzes files to determine symbol frequencies and builds a Huffman tree to assign variable-length codes. It allows compressing different data types like text, images, audio and video. Adaptive Huffman coding is also described, which dynamically updates the code tree as more data is processed. Benefits of Huffman compression include being fast, simple to implement and achieving close to optimal compression. Sample screenshots of the Sunzip tool are also provided showing file details before and after compression.
This document presents a new approach called mixed S-D slicing that combines static and dynamic program slicing using object-oriented concepts in C++. Static slicing analyzes the entire program code but produces larger slices, while dynamic slicing produces smaller slices based on a specific execution but is more difficult to compute. The mixed S-D slicing aims to generate dynamic slices faster by leveraging object-oriented features like classes. An example C++ program is provided to demonstrate the S-D slicing approach using concepts like classes, inheritance, and polymorphism. The approach is intended to reduce complexity and aid in debugging object-oriented programs by combining static and dynamic slicing techniques.
Performance evaluation of different routing protocols in wsn using different ...Dr Sandeep Kumar Poonia
This document evaluates the performance of different routing protocols in wireless sensor networks using various network parameters. It simulates the Dynamic Source Routing (DSR) and Adhoc On-Demand Distance Vector (AODV) routing protocols in a 1000m x 1000m terrain area with 100 sensor nodes. The packet delivery fraction, average throughput, and normalized routing load are analyzed at different node speeds ranging from 20-100m/s. The results show that AODV performs better than DSR in terms of packet delivery fraction and normalized routing load, while DSR has better average throughput performance. In conclusion, AODV is more optimal for small terrain areas when considering packet delivery and routing overhead, while DSR provides higher data rates.
This document describes a simulator for database aggregation using metadata. The simulator sits between an end-user application and a database management system (DBMS) to intercept SQL queries and transform them to take advantage of available aggregates using metadata describing the data warehouse schema. The simulator provides performance gains by optimizing queries to use appropriate aggregate tables. It was found to improve performance over previous aggregate navigators by making fewer calls to system tables through the use of metadata mappings. Experimental results showed the simulator solved queries faster than alternative approaches by transforming queries to leverage aggregate tables.
Performance evaluation of diff routing protocols in wsn using difft network p...Dr Sandeep Kumar Poonia
In the recent past, wireless sensor networks have been introduced to use in many applications. To design the networks, the factors needed to be considered are the coverage area, mobility, power consumption, communication capabilities etc. The challenging goal of our project is to create a simulator to support the wireless sensor network simulation. The network simulator (NS-2) which supports both wire and wireless networks is implemented to be used with the wireless sensor network.
The Traveling Salesman Problem (TSP) involves finding the minimum cost tour that visits each customer exactly once and returns to the starting depot. Key heuristics to solve the TSP include nearest neighbor, insertion methods, and 2-opt exchanges. The Vehicle Routing Problem (VRP) extends the TSP by routing multiple vehicles of limited capacity from a central depot to serve customer demands. Common heuristics for the VRP include savings algorithms and sweep methods.
This document provides an overview of linear programming, including:
- It describes the linear programming model which involves maximizing a linear objective function subject to linear constraints.
- It provides examples of linear programming problems like product mix, blending, transportation, and network flow problems.
- It explains how to develop a linear programming model by defining decision variables, the objective function, and constraints.
- It discusses solutions methods like the graphical and simplex methods. The simplex method involves iteratively moving to adjacent extreme points to maximize the objective function.
This document discusses approximation algorithms and introduces several combinatorial optimization problems. It begins by explaining that approximation algorithms are needed to find near-optimal solutions for problems that cannot be solved in polynomial time, such as set cover and bin packing. It then provides examples of problems that are in P, NP, and NP-complete. Several techniques for designing approximation algorithms are outlined, including greedy algorithms, linear programming, and semidefinite programming. Specific NP-complete problems like vertex cover, set cover, and independent set are introduced and approximations algorithms with performance guarantees are provided for set cover and vertex cover.
The document describes Voronoi diagrams and an algorithm for constructing them efficiently. Voronoi diagrams partition space into regions based on distance to points called sites. The algorithm uses a sweep line approach, maintaining the current state of the diagram. It handles events where the sweep line encounters a site or potential empty circle. The key data structures are a balanced binary search tree to represent the beach line, a doubly linked list to represent the constructed diagram, and a priority queue of events. The algorithm runs in O(n log n) time.
This lecture covers several topics in geometric algorithms:
- An optimal parallel algorithm for computing the 2D convex hull problem in O(n log n) time using O(n) work.
- Applications of the 2D convex hull algorithm to problems like range searching and geometric intersection finding.
- The use of techniques like sweep lines and space partitioning trees to solve higher dimensional geometric problems efficiently.
- A parallel search algorithm finds two elements in a sorted array that bracket a query element in logarithmic time using p processors (paragraph 1).
- A parallel merging algorithm uses the parallel search to rank and merge two sorted arrays in optimal O(log log n) time and O(n log log n) work (paragraph 2).
- An efficient sorting algorithm uses the optimal parallel merging algorithm in a merge sort approach to sort n elements in O(n log n) work and O(log n log log n) time (paragraph 3).
This document discusses parallel algorithms for tree problems. It introduces the Euler tour technique for representing trees as lists to allow parallel processing. The technique converts trees to Eulerian graphs by adding edges. It also discusses parallel depth-first search using Euler tours. Tree contraction is presented as a method to evaluate arithmetic expressions represented as binary trees in parallel by successively merging nodes.
This document discusses parallel algorithms for merging sorted arrays and ranking elements in a linked list. It presents:
1. A parallel merging algorithm that partitions the arrays into blocks, ranks the elements within matching blocks in parallel, and sequentially merges the smaller blocks. This takes O(log n) time using O(n) processors.
2. Two list ranking algorithms - one in O(log n) time and O(n log n) work, and an optimal one in O(log n log log n) time and O(n) work using independent sets and pointer jumping.
This document describes a parallel algorithm for computing prefix sums and merging sorted arrays. It presents:
1) A parallel prefix algorithm that computes prefix sums in O(log n) time using O(n) processors by performing prefix computations in parallel across a binary tree representation.
2) A parallel merging algorithm that partitions input arrays into blocks of size O(log n), ranks block elements in parallel, and sequentially merges matching blocks, achieving O(log n) time and O(n) processors.
3) An analysis showing the parallel prefix algorithm runs in O(log n) time using O(n) processors, and two sorted arrays can be merged in O(log n) time using O(n
The document discusses parallel algorithms and their analysis. It introduces a simple parallel algorithm for adding n numbers using log n steps. Parallel algorithms are analyzed based on their time complexity, processor complexity, and work complexity. For adding n numbers in parallel, the time complexity is O(log n), processor complexity is O(n), and work complexity is O(n log n). The document also discusses models of parallel computation like PRAM and designs of parallel architectures like meshes and hypercubes.
The document summarizes network flow problems and the Ford-Fulkerson algorithm for finding the maximum flow in a network. It introduces network representations using graphs, the concepts of capacity, flow, and defines the maximum flow problem. It then describes the Ford-Fulkerson method using residual networks and augmenting paths to iteratively increase the flow. It proves that this process results in a valid flow and terminates when no augmenting path remains, at which point the maximum flow has been found.
The document discusses algorithms for finding shortest paths in graphs. It describes Dijkstra's algorithm and Bellman-Ford algorithm for solving the single-source shortest paths problem and Floyd-Warshall algorithm for solving the all-pairs shortest paths problem. Dijkstra's algorithm uses a priority queue to efficiently find shortest paths from a single source node to all others, assuming non-negative edge weights. Bellman-Ford handles graphs with negative edge weights but is slower. Floyd-Warshall finds shortest paths between all pairs of nodes in a graph.
MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...NelTorrente
In this research, it concludes that while the readiness of teachers in Caloocan City to implement the MATATAG Curriculum is generally positive, targeted efforts in professional development, resource distribution, support networks, and comprehensive preparation can address the existing gaps and ensure successful curriculum implementation.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
New Local Search Strategy in Artificial Bee Colony Algorithm
1. New Local Search Strategy in Artificial Bee
Colony Algorithm
Sandeep Kumar , Pawan Bhambu , Vivek Kumar Sharma
Faculty of Engineering and Technology
Jagannath University, Jaipur, India
Abstract— Artificial Bee Colony (ABC) algorithm is a Nature
Inspired Algorithm (NIA) which based on intelligent food
foraging behaviour of honey bee swarm. This paper introduces
a local search strategy that enhances exploration competence
of ABC and avoids the problem of stagnation. The proposed
strategy introduces two new local search phases in original
ABC. One just after onlooker bee phase and one after scout
bee phase. The newly introduced phases are inspired by
modified Golden Section Search (GSS) strategy. The proposed
strategy named as new local search strategy in ABC
(NLSSABC). The proposed NLSSABC algorithm applied over
thirteen standard benchmark functions in order to prove its
efficiency.
Keywords— artificial bee colony algorithm; nature inspired
algorithm; local search; memetic computing; swarm
intelligence.
I. INTRODUCTION
Artificial bee colony Optimization techniques is one of
the fashionable swam intelligence optimization technique
anticipated by D. Karaboga [1]. These modus operandi are
used to discover a set of values for the independent
variables that optimizes the value of one or more dependent
variables. There are number of trifling multivariable
optimization problems with capriciously high
dimensionality which cannot be solved by precise search
methods in stirred time. So search algorithms capable of
searching near-optimal or good solutions within adequate
computation time are very realistic in real life. In few years,
the technical community has noticed the importance of a
large number of nature-inspired metaheuristics and hybrids
of these nature-inspired optimization methods.
Metaheuristics may be measured a widespread algorithmic
skeleton that can be applied to poles apart optimization
problems with comparative a small number of
modifications to get a feel for them to a specific problem.
Metaheuristics are anticipated to make bigger the
capabilities of heuristics by hybridizing one or more
heuristic strategies using a higher-level methodologies
(hence ‘meta’). Metaheuristics are strategies that provide
guidance to the search process. Hyperheuristics are up till
now an additional extension that focuses on heuristics that
adapt their parameters in order to get better efficacy or
result, or the effectiveness of the computation progression.
Hyperheuristics endow with high-level methodologies that
possibly will make use of machine learning and get a feel
for their search behaviour by modifying the application of
the sub-procedures or even which procedures are used [2].
Algorithms on or after the meadow of computational
intelligence, biologically inspired intelligent computing,
and metaheuristics are applied to troublesome problems, to
which more classical approaches may not be significant.
Michalewicz and Fogel says that these tribulations are
difficult [3] as: they has large number of feasible solutions
in the search space due to which they not able to exploit the
best results; The problem is so intricate, that just to
facilitate any reply at all, we have to make use of such
beginner's models of the problem that any consequence is in
essence a waste of time; the appraisal function that
describes the quality of whichever proposed explanation is
noisy or varies with time, by this means requiring not just a
solitary solution but an entire series of solutions; the
promising elucidations are so deliberately constrained that
constructing even single feasible answer is incredibly easier
said than done, let alone searching for an most
advantageous solution; the human being solving the
problem is inadequately composed or imagines some
psychological fencing that prevents them from discovering
exact solution.
Nature inspired algorithms are encouraged by some
natural happening, can be categorized as per their source of
encouragement. Major classes of NIA are: Evolutionary
Algorithms, Immune Algorithms, Neural Algorithms,
Physical Algorithms, Probabilistic Algorithms, Stochastic
Algorithms and Swarm Algorithms [2].
Evolutionary Algorithms are motivated by advancement
of natural selection strategy. Evolutionary Algorithms fit
into the Evolutionary Computation field of learning
concerned with computational methods encouraged by the
itinerary of action and mechanisms of biological
progression. Examples of evolutionary algorithm are
Differential Evolution (EA), Evolutionary Programming
(EP), Evolution Strategies (ES), Gene Expression
Programming, Genetic Algorithm (GA), Genetic
Programming (GP), Grammatical Evolution, Learning
Classifier structure, Non-dominated Sorting Genetic
Algorithm, and Strength Pareto Evolutionary Algorithm [2].
Immune Algorithms are aggravated by the adaptive
immune system of vertebrates. A simplified narration of the
immune organization is an appendage system anticipated to
care for the host organism from the intimidation posed to it
from pathogens and noxious substances. Pathogens include
an assortment of microorganisms such as bacteria, viruses,
parasites and pollen. The conventional viewpoint regarding
the responsibility of the immune system is alienated into
two most important tasks: the detection and elimination of
pathogen. This activity is classically referred to as the
delineation of self (molecules and cells that are in the right
Sandeep Kumar et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) , 2014, 2559-2565
www.ijcsit.com 2559
2. place to the host organisms) from potentially destructive
non-self. Like Clonal Selection Algorithm (CSA), Negative
Selection Algorithm, Artificial Immune Recognition
System, Immune Network Algorithm and Dendritic Cell
Algorithm [2].
Neural Algorithms are encouraged by the flexibility and
learning individuality of the human nervous coordination.
Some well known neural algorithms are Perceptron, Back-
propagation, Hopfield Network, Learning Vector
Quantization and Self-Organizing Map [2].
Physical Algorithms are motivated by corporal and
communal systems Physical algorithms are those
algorithms motivated by a physical process. Most of the
physical algorithm in general belong to the fields of
metaheustics and Computational cleverness; even though
do not fit neatly into the obtainable categories of the
biological motivated techniques. In this vein, they could
immediately as by far be referred to as nature inspired
algorithms. Like Simulated Annealing, Extremal
Optimization, Harmony Search, Cultural Algorithm, and
Memetic Algorithm [2].
Probabilistic Algorithms are strategies that concentrate
on methods that put together models and guesstimate
distributions in search domains. Probabilistic Algorithms
are those algorithms that sculpt a dilemma or explore a
problem space using a probabilistic model of entrant
solutions. Examples of probabilistic algorithms are
Population-Based Incremental Learning, Univariate
Marginal Distribution Algorithm, Compact Genetic
Algorithm, Bayesian Optimization Algorithm and Cross-
Entropy Method [2].
Stochastic Algorithms are algorithms that focus on the
prologue of unpredictability into heuristic methods.
Examples of stochastic algorithms are Random Search,
Adaptive Random Search, Stochastic Hill Climbing,
Iterated Local Search, Guided Local Search, Variable
Neighbourhood Search, Greedy Randomized Adaptive
Search, Scatter Search, Tabu Search, and Reactive Tabu
Search [2].
Swarm Algorithms focus on strategies that make use of
the properties of cooperative intelligence. Swarm
intelligence is the study of computational systems
motivated by the ‘collective intelligence’. Collective
Intelligence emerges all the way through the cooperation of
large numbers of uniform agents in the surroundings.
Examples consist of schools of fish, group of birds, and
colonies of ants. Such intelligence is decentralized, self-
organizing and scattered throughout surroundings. In nature
such systems are usually used to solve problems such as
successful foraging for food, prey escaping, or colony
repositioning. The information is classically stored all the
way through the participating uniform agents. Some
examples of swarm intelligence are Particle Swarm
Optimization, Ant System, Ant Colony System, Bees
Algorithm, Spider Monkey Optimization Algorithm and
Bacterial Foraging Optimization Algorithm.
Real-world optimization problems and generalizations
thereof know how to haggard from the majority fields of
science, management, engineering, and information
technology. Prominently, function optimization problems
have had a long institution in the fields of Artificial
Intelligence in encouraging basic research into new
dilemma solving strategies, and for finding and verifying
systemic behaviour against benchmark problem instances
[1]. The enhanced local search strategy proposed in this
paper is based on one of the youngest member of NIA
family the Artificial Bee Colony (ABC) algorithm [1],
which mimics the extra ordinary food foraging behaviour of
most intelligent insect that is honey bees swarm. ABC is a
swarm-based intelligent stochastic optimization algorithm
that has shows significant performance in solving
continuous [4]-[6], combinatorial [7]-[10] and many more
complex optimization problems. Stochastic optimization
algorithms are those so as to use unpredictability to induce
non-deterministic characteristics, contrasted to entirely
deterministic strategies. Most strategies from the fields of
biologically motivated Computation, Computational
Intelligence and Metaheuristics may be evaluated to relate
the field of Stochastic Optimization [2].
The organization of rest of paper is as follow: In segment
2, it introduce Artificial Bee Colony Algorithm in detailed
manner, one of the newest swarm based method introduces
by D. Karaboga [1]. Next section discusses some recent and
important improvement and modifications in ABC. Section
4 establishes the proposed New Local Search Strategy in
ABC algorithm. Section 5 contains experimental setup and
results followed by conclusion and reference sections.
II. ARTIFICIAL BEE COLONY ALGORITHM
The ABC algorithm impersonates the food foraging
behaviour of the honey bees with three groups of bees:
employed bees, onlookers and scouts. A honey bee working
to forage a food source (i.e. solution) previously visited by
itself and searching only around its vicinity is called an
employed bee. Employed bees perform waggle dance upon
returning to the hive to propagate the information of its
food source to the rest of the colony. A bee waiting around
the dance floor to choose any of the employed bees to
follow is called an onlooker. A bee indiscriminately
searching a search space for finding a food source is called
a scout. For every food source, there is only one employed
bee and a number of adherent bees. The scout bee, after
finding some food source better than some threshold value,
also performs a waggle dance to share this information. In
the ABC algorithm accomplishment, half of the colony
consists of employed bees and the other half constitutes the
onlookers. The number of food sources (i.e., solutions being
exploited) is equal to the number of employed bees in the
colony. The employed bee whose food source (i.e. solution)
is fatigued (i.e. the solution has not been improved after
quite a few attempts) becomes a scout. The meticulous
algorithm is given below:
Algorithm 1: Artificial Bee Colony Algorithm
Step 1. Engender a preliminary population of N uniformly distributed
individuals. Each individual xij is a food source (i.e. solution) and
has D attributes. D is the dimensionality of the problem. xij
represent ith
solution in jth
dimension. Where j ∈ {1, 2, …, D}
min max min[0,1]( )ij j j jx x rand x x= + −
Sandeep Kumar et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) , 2014, 2559-2565
www.ijcsit.com 2560
3. Step 2. Guesstimate the fitness of each individual solution using the
subsequent method,
if (solution value >= 0)
then ( 1)2*
1
solution valueifit +
=
else
1(1 ( ))solutioni value
fit fabs= +
Step 3. Each employed bee, sited at a food source that is poles apart
from others, search in the propinquity of its current situation to
find a better food source. For each employed bee, engender a new
solution, vi around its current location, xi using the subsequent
formula.
( )ij ij ij kjijv x x xφ= + −
Here, k ∈ {1, 2,…, N} and j ∈ {1, 2, …, D} are randomly
preferred index. N is number of employed bees. ɸij is a uniform
random number from [-1, 1].
Step 4. Compute the fitness of both xi and vi. Apply insatiable selection
strategy to pick better one of them.
Step 5. Determine and stabilize the likelihood values, Pi for each
solution xi using the subsequent formula.
1
SN
i
i
ij
i
fit
P
fit
=
=
Step 6. Allocate each onlooker bee to a solution, xi at haphazard with
probability comparative to Pi.
Step 7. Engender new food positions (i.e. solutions), vi for each
onlooker bee.
Step 8. Calculate the fitness of every onlooker bee, xi and the
innovative solution, vi. Apply insatiable selection procedure to
keep the fitter one and throw out other.
Step 9. if a fastidious solution xi has not been enhanced over a
predefined number of cycles, then select it for denunciation.
Replace the result by insertion a scout bee at a food source
generated in an even way at random within the search space by
means of subsequent formula.
min max min[0,1]( )ij j j jx x rand x x= + −
for j = 1, 2,……,D
Step 10. Maintain pathway of the best food sources (solution)
found as far as this.
Step 11. Check termination criteria. If the best solution found
is acceptable or reached the maximum iterations, stop and return
the best solution found so far. Otherwise go back to step 2 and
repeat again.
III. MODIFICATIONS IN ARTIFICIAL BEE COLONY
ALGORITHM
Often real world provides some complex optimization
problems that cannot be easily dealt with available
mathematical optimization methods. If the user is not very
aware about the exact solution of the problem in hand then
intelligence emerged from social behaviour of social colony
members may be used to solve these kinds of problems.
Honey bees are in the category of social insects. The
foraging behaviour of honey bees produces an intelligent
social behaviour, called as swarm intelligence. This swarm
intelligence is simulated and an intelligent search algorithm
namely, Artificial Bee Colony (ABC) algorithm is
established by Karaboga in 2005 [1]. Since its
commencement, a lot of research has been conceded to
make ABC more and more efficient and to apply ABC for
different types of problems.
In order to liberate the drawbacks of original ABC,
researchers and scientists have adapted ABC in numerous
ways. The potential where ABC can be enhanced are
improvement of ABC control parameters named SN, ɸij and
limit (maximum cycle number). Hybridization of ABC with
new population based probabilistic or deterministic
algorithms. Some new control parameters are also
introduced in different phases of ABC algorithm. D.
Karaboga [1] has recommended that the value of ɸij should
be in the range of [-1, 1]. The value of limit (maximum
cycle number) should be SN × D, where, SN is the number
of possible solutions and D is the dimension of the problem.
Wei-feng Gao et al. [11] proposed an improved solution
search method in ABC, which depends on the fact that bee
searches around the best solution of the preceding iteration
to increase the exploitation. A. Banharnsakun et al. [12]
introduced a new variant of ABC namely the best-so-far
selection in artificial bee colony algorithm. To enhance the
exploitation and exploration processes, they propose to
make three major changes by introducing the best-so-far
method, an adjustable search radius, and an objective-value-
based comparison method in DE. J.C. Bansal et al. [13]
anticipated balanced ABC; they added a new control
parameter, Cognitive Learning Factor and also tailored
range of ɸ in Artificial Bee Colony algorithm. Qingxian
and Haijun [14] anticipated a change in the initialization
scheme by making the initial group symmetrical, and the
Boltzmann selection mechanism was employed instead of
roulette wheel selection for humanizing the convergence
ability of the ABC algorithm.
In order to take full advantage of the exploitation power
of the onlooker stage, Tsai et al. anticipated the Newtonian
law of universal gravitation in the onlooker segment of the
basic ABC algorithm in which onlookers are elected based
on a roulette wheel [15]. Baykasoglu et al. integrated the
ABC algorithm with swing vicinity searches and greedy
randomized adaptive search heuristic and applied it to the
widespread assignment problem [16]. In addition, tailored
versions of the Artificial Bee Colony algorithm are
introduced and applied for efficiently solving real-
parameter optimization problems by Bahriye Akay and
Dervis Karaboga [17]. To adjust ABC behaviour for
constrained search space Mezura et al. [18] anticipated four
modifications related with the selection strategy, the scout
bee machinist, and the equality and border line constraints.
As an alternative of fitness relative selection, tournament
selection is performed to utilize employed bee food sources
by onlooker bees. Second, they employed self-motivated
tolerance for equality constraints. In 2010, Zhu and Kwong
[19] expected an enhanced version of ABC algorithm called
gbest-guided ABC (GABC) algorithm by including the
information of global best (gbest) explanation into the
solution search equation to get better the exploitation.
GABC is encouraged by PSO [20], which, in order to
improve the exploitation capability, takes advantage of the
information of the global best (gbest) solution to guide the
search by candidate solutions. J.C. Bansal et al. [25]
introduced memetic search in ABC algorithm with the
intention of balancing exploitation and exploration. S.
Kumar et al.[30]-[36] modified memetic search with new
parameters and applied modified golden section search
process in ABC algorithm, DE and Spider Monkey
Sandeep Kumar et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) , 2014, 2559-2565
www.ijcsit.com 2561
4. Optimization algorithm to speed rate of convergence. In
2010, Derelia and Das [21] anticipated a hybrid bee(s)
algorithm for solving container loading problems. In the
wished-for algorithm, a bee(s) algorithm is hybridized with
the heuristic filling modus operandi for the solution of
container loading problems. In 2010, Huang and Lin [22]
anticipated a new bee colony optimization algorithm with
idle-time-based filtering scheme and its application for
open shop-scheduling problems. In 2011, Nambiraj Suguna
et al. [23] anticipated a self-regulating rough set approach
hybrid with artificial bee colony algorithm for
dimensionality diminution. In the wished-for work, effects
of the perturbation rate, the scaling factor, and the limit are
examined on real-parameter optimization. ABC algorithm
hybridized with genetic algorithm to poise exploration and
exploitation of search space [26], [27]. In 2012, Bin Wu et
al. [24] anticipated perfection of Global swarm optimization
(GSO) hybrid with ABC and PSO. They use neighbourhood
solution generation scheme of ABC and accept new
solution only when it is better than preceding one to
advance GSO performance.
IV.NEW LOCAL SEARCH STRATEGY IN ABC
The proposed new local search strategy in ABC has two
supplementary phases to original ABC [1]. The proposed
algorithm introduces two new strategies; it modified the
range of GSS process and add memetic search phase. The
detailed modified ABC algorithm is given below:
Algorithm 2: New Local Search Strategy in ABC
Step 1. Generate an initial population of N uniformly distributed
individuals. Each individual xij is a food source (i.e. solution) and
has D attributes. D is the dimensionality of the problem. xij
represent ith
solution in jth
dimension. Where j ∈ {1, 2, …, D}
min max min[0,1]( )ij j j jx x rand x x= + −
Step 2. Estimate the fitness of each individual solution using the
following method,
if (solution_value >= 0)
then
*(1/(2* +1))+
(1- )*(1+fabs(1/ ))
i solution value
solution val
t
e
fi
u
φ
φ
=
else
(1 )*(1/(2* +1))+
*(1+fabs(1/ ))
i solution value
solution val
f
u
t
e
i φ
φ
= −
Step 3. Each employed bee, placed at a food source that is different
from others, search in the proximity of its current position to find
a better food source. For each employed bee, generate a new
solution, vij around its current position, xij using the following
formula.
( )ij ij ij kjijv x x xφ= + −
Here, k ∈ {1, 2,…, N} and j ∈ {1, 2, …, D} are randomly chosen
indices. N is number of employed bees. ɸij is a uniform random
number from [-1, 1].
Step 4. Compute the fitness of both xij and vij. Apply greedy selection
strategy to select better one of them.
Step 5. Calculate and normalize the probability values, Pij for each
solution xi using the following formula.
1
_
(1 ) N
i
i i
ij
Maximum fitness
i
fit fit
P
fit
φ φ
=
+= −
Step 6. Assign each onlooker bee to a solution, xi at random with
probability proportional to Pij.
Step 7. Generate new food positions (i.e. solutions), vij for each
onlooker bee.
Step 8. Compute the fitness of each onlooker bee, xij and the new
solution, vij. Apply greedy selection process to keep the fitter one
and abandon other.
Step 9. First memetic search phase inspired by GSS process.
Repeat while termination criteria meet
Compute 1 ( ) ,f b b a ψ= − − ×
and
2 ( ) ,f a b a ψ= + − ×
Calculate
( )1f f
and
( )2f f
If
( )1f f
<
( )2f f
then
b = f2 and the solution lies in the range [a, b]
else
a = f1 and the solution lies in the range [a, b]
End of if
End of while
Step 10. If a particular solution xij has not been improved
over a predefined number of cycles, then select it for rejection.
Replace the solution by placing a scout bee at a food source
generated evenly at random within the search space using
min max min[0,1]( )ij j j jx x rand x x= + −
for j = 1, 2,……,D
Step 11. Second memetic search phase inspired by GSS
process.
Repeat while termination criteria meet
Compute 1 ( ) ,f b b a ψ= − − ×
and 2 ( ) ,f a b a ψ= + − ×
Calculate
( )1f f
and
( )2f f
If
( )1f f
<
( )2f f
then
b = f2 and the solution lies in the range [a, b]
else
a = f1 and the solution lies in the range [a, b]
End of if
End of while
Step 12. Keep track of the best food sources (solution) found
so far.
Step 13. Check termination criteria. If the best solution found
is acceptable or reached the maximum iterations, stop and return
the best solution found so far. Otherwise go back to step 2 and
repeat again.
V. EXPERIMENTAL RESULTS
A. Test problems under consideration
In order to analyse the performance of NLSSABC
diverse comprehensive optimization problems (f1 to f13) are
chosen. These are continuous optimization problems and
have different degrees of complexity, search range and
multimodality. Test problems are taken from [28], [29] with
the associated counterbalance values.
B. Experimental Setup
To prove the competence of NLSSABC, it is compared
with original ABC and MeABC algorithms. To test
NLSSABC over considered problems, subsequent
experimental setting is adopted:
Sandeep Kumar et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) , 2014, 2559-2565
www.ijcsit.com 2562
5. TABLE I: TEST PROBLEMS
Test Problem Objective Function Search
Range
Optimum Value D Acceptable
Error
Griewank
2
1 1 1
1
( ) ( ) cos 1
4000
DD i
ii i
x
f x x
i= =
= − +
∏ [-600, 600] f(0) = 0 30 1.0E-05
Zakharov
2 41
2 1 1
1
2
( ) ( ) ( )
2 2
D Di
ii
i
D
i
ix ix
xf x
= =
=
= + + [-5.12,
5.12]
f(0) = 0 30 1.0E-02
Salomon
Problem
2 2
3 1 1
( ) 1 cos(2 ) 0.1( )
D D
i ii i
f x x xπ
= =
= − + [-100, 100] f(0) = 0 30 1.0E-01
Colville
function
2 2 2 2 2 2
4 2 1 1 4 3 3
2 2
2 4 2 4
( ) 100( ) (1 ) 90( ) (1 )
10.1[( 1) ( 1) ] 19.8( 1)( 1)
f x x x x x x x
x x x x
= − + − + − + −
+ − + − + − −
[-10, 10] f(1) = 0 4 1.0E-05
Kowalik
function
2
11 21 2
5 21
3 4
( )
( ) ( )i i
ii
i i
x b b x
f x a
b b x x=
+
= −
+ +
[-5, 5]
f(0.1928, 0.1908,
0.1231, 0.1357) =
3.07E-04
4
1.0E-05
Shifted
Rosenbrock
1 2 2 2
6 11
1, 2 1 2
( ) (100( ) ( 1) ,
1, [ ,... ], [ , ,....... ]
D
i i i biasi
D D
f x z z z f
z x o x x x x o o o o
−
+=
= − + − +
= − + = =
[-100, 100] f(o)=fbias=390 10 1.0E-01
Six-hump
camel back
2 4 2 2 2
7 1 1 1 1 2 2 2
1
( ) (4 2.1 ) ( 4 4 )
3
f x x x x x x x x= − + + + − + [-5, 5]
f(-0.0898, 0.7126) =
-1.0316
2 1.0E-05
Easom’s
function
2 2
1 2( ( ) ( ) )
8 1 2( ) cos cos x x
f x x x e π π− − − −
= − [-10, 10] f(π, π) = -1 2 1.0E-13
Meyer and
Roth Problem
5 21 3
9 1
1 2
( ) ( )
1
i
ii
i i
x x t
f x y
x t x v=
= −
+ + [-10, 10]
f(3.13, 15.16,0.78) =
0.4E-04
3 1.0E-03
Braninss
Function
2 2
10 2 1 1 1( ) ( ) (1 )cosf x a x bx cx d e f x e= − + − + − +
1
2
[ 5,10],
[0,15]
x
x
∈ −
∈ f(-π, 12.275) =
0.3979
2 1.0E-05
Alpine 11 1
( ) sin 0.1
n
i i ii
f x x x x
=
= + [-10, 10] f(0) =0 30 1.0E-05
McCormick 2
12 1 2 1 2 1 2
3 5
( ) sin( ) ( ) 1
2 2
f x x x x x x x= + + − − + +
1
2
1.5 4,
3 3
x
x
− ≤ ≤
− ≤ ≤ f(-0.547, -1.547) = -
1.9133
30 1.0E-04
Shubert
5 5
13 1 21 1
( ) cos(( 1) 1) cos(( 1) 1)
t i
f x i i x i i x
= =
= − + + + + [-10, 10]
f(7.0835, 4.8580)= -
186.7309
2 1.0E-05
Colony size N = 60,
Number of food sources SN = N/2,
ɸij = rand[−1, 1],
limit = 1500,
The stopping hallmark is either maximum
number of function assessment (which is set to
be 200000) is reached or the tolerable error
(outlined in Table I) has been achieved,
The number of imitations/run =100,
Parameter settings for the ABC and MeABC
algorithm are identical to NLSSABC.
C. Result Comparison
Statistical results of NLSSABC with experimental
setting as per last subsection are outlined in Table II. Table
II show the comparison of results based on mean function
value (MFV), standard deviation (SD), mean error (ME),
average function evaluations (AFE) and success rate (SR)
are accounted. Table II shows that most of the time
NLSSABC do better than other considered algorithms in
terms of competence (with less number of function
appraisals) and correctness. Table III shows upshots of
table II between NLSSABC, MeABC and original ABC
algorithm. The proposed algorithm at all times gets better
AFE and most of the time it also improve SD and ME. It is
due to newly initiated local search phase and modified GSS
process.
Sandeep Kumar et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) , 2014, 2559-2565
www.ijcsit.com 2563
6. TABLE II: COMPARISON OF RESULTS FOR TEST PROBLEMS
Test Problem AlgorithmMeasure MFV SD ME AFE SR
f1 ABC 4.61E-03 9.10E-03 4.61E-03 81988.8 61
MeABC 2.04E-04 1.42E-03 2.04E-04 37417.2 98
NLSSABC 1.04E-03 2.98E-03 1.04E-03 77758.02 89
f2 ABC 9.92E+01 1.49E+01 9.92E+01 100020 0
MeABC 1.88E-02 9.79E-03 1.88E-02 98848.74 18
NLSSABC 9.59E-03 4.43E-04 9.59E-03 58718.52 100
f3 ABC 1.67E+00 2.30E-01 1.67E+00 100020.1 0
MeABC 9.22E-01 3.58E-02 9.22E-01 21210.76 100
NLSSABC 9.22E-01 2.84E-02 9.22E-01 16846.92 100
f4 ABC 1.87E-01 1.38E-01 1.87E-01 100041.9 0
MeABC 7.14E-03 2.92E-03 7.14E-03 28743.11 98
NLSSABC 7.68E-03 2.33E-03 7.68E-03 21845.4 100
f5 ABC 4.73E-04 6.99E-05 1.66E-04 87420.15 26
MeABC 4.10E-04 5.28E-05 1.02E-04 62047.63 88
NLSSABC 3.98E-04 1.32E-05 9.01E-05 30936.77 100
f6 ABC 3.96E+02 9.68E+00 6.43E+00 95518.76 10
MeABC 3.91E+02 3.63E+00 1.34E+00 74898.53 48
NLSSABC 3.90E+02 1.03E+00 4.13E-01 104029.3 84
f7 ABC 3.00E+00 2.07E-06 5.03E-07 73563.42 35
MeABC 3.00E+00 4.40E-15 4.33E-15 5664.01 100
NLSSABC 3.00E+00 4.19E-15 4.16E-15 4401.02 100
f8 ABC -1.03E+00 4.39E-03 4.60E-03 100043.5 0
MeABC -1.03E+00 1.32E-05 1.69E-05 61663.03 43
NLSSABC -1.03E+00 1.40E-05 1.91E-05 120735.1 40
f9 ABC -1.87E+00 4.18E-02 3.91E-02 99637.14 1
MeABC -1.91E+00 9.39E-06 9.15E-05 41575.84 87
NLSSABC -1.91E+00 6.34E-06 8.81E-05 5036.13 100
f10 ABC 3.98E-01 6.65E-06 5.53E-06 10056.23 92
MeABC 3.98E-01 6.56E-06 5.73E-06 9080.57 92
NLSSABC 3.98E-01 7.53E-06 6.99E-06 42614.76 79
f11 ABC 3.06E-02 2.13E-02 3.06E-02 100020 0
MeABC 1.22E-05 2.38E-05 1.22E-05 75174.72 90
NLSSABC 9.25E-06 1.06E-06 9.25E-06 60865.44 100
f12 ABC -2.31E+00 4.25E-02 3.72E-02 100031.6 0
MeABC -2.35E+00 1.23E-05 8.48E-06 48699.6 81
NLSSABC -2.35E+00 6.18E-06 5.88E-06 20200.95 92
f13 ABC 1.91E-03 5.19E-06 1.95E-03 30838.67 91
MeABC 1.91E-03 2.94E-06 1.95E-03 3577.26 100
NLSSABC 1.91E-03 2.96E-06 1.95E-03 3575.05 100
TABLE III: SUMMARY OF TABLE II OUTCOMES
Test Problem NLSSABC vs ABC NLSSABC vs MeABC
f1 + -
f2 + +
f3 + +
f4 + +
f5 + +
f6 + +
f7 + +
f8 + -
f9 + +
f10 - -
f11 + +
f12 + +
f13 + +
Total Number of + sign 12 10
VI.CONCLUSION
At this point in this paper, two new phases are initiated
in original ABC. Newly added steps are inspired by
memetic ABC and position update achieved on the basis of
appropriateness of individual in order to balance
intensification and diversification of local search breathing
space. Additional, the advanced strategy is applied to get to
the bottom of 13 well-known benchmark functions. With
the help of experiments over test problems, it is shown that
the addition of the proposed strategy in the original ABC
improves the trustworthiness, competence and accurateness
as weigh against to their original adaptation. Table II and
III show that the anticipated NLSSABC is competent to
solve largest part the considered problems with smaller
amount of pains.
Sandeep Kumar et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) , 2014, 2559-2565
www.ijcsit.com 2564
7. REFERENCES
[1] D. Karaboga, “An idea based on honey bee swarm for numerical
optimization”, Technical Report-TR06, Erciyes Univ., Computer
Engg. Department, 2005.
[2] Brownlee, Clever algorithms: nature-inspired programming recipes.
Jason Brownlee, 2011.
[3] Z. Michalewicz and DB Fogel. How to Solve It: Modern Heuristics.
Springer, 2004.
[4] D. Karaboga, B. Basturk, Artificial Bee Colony (ABC)
Optimization Algorithm for Solving Constrained Optimization
Problems, LNCS: Advances in Soft Computing: Foundations of
Fuzzy Logic and Soft Computing, CA, Vol: 4529/2007, pp: 789-798,
Springer- Verlag, 2007, IFSA 2007.
[5] D. Karaboga, B. Basturk, “An artificial bee colony (ABC) algorithm
for numeric function optimization”, in Proceedings of the IEEE
Swarm Intelligence Symposium 2006, Indianapolis, Indiana, USA,
12–14 May 2006.
[6] XS Yang, “Engineering Optimizations via Nature-Inspired Virtual
Bee Algo.”, IWINAC2005, LNCS 3562, J. M. Yang and J.R.
Alvarez (Eds.), Springer-Verlag, Berlin Heidelberg, pp. 317-323,
2005.
[7] P. Lucic, D. Teodorovic, “Computing with Bees: Attacking
Complex Transportation Engineering Problems”, in International
Journal on Artificial Intelligence Tools, vol. 12, no. 3, pp. 375-394,
2003.
[8] CS Chong, M. Y. H. Low, A. I. Sivakumar, K. L. Gay, “A Bee
Colony Optimization Algorithm to Job Shop Scheduling”, Proc. of
the 37th Winter Simulation, California, pp. 1954-1961, 2006.
[9] GZ Markovic, D. Teodorovic, V. S. Acimovic-Raspopovic,
“Routing and Wavelength Assignment in All-Optical Networks
Based on the Bee Colony Optimization”, AI Communications, v.20
no.4, p.273-285, December 2007.
[10] N Quijano, KM Passino, “Honey Bee Social Foraging Algorithms
for Resource Allocation Theory and Application”, American
Control Conference, New York City, USA, 2007.
[11] W Gao and S Liu. A modified artificial bee colony algorithm.
Computers & Operations Research, 2011.
[12] A Banharnsakun, T Achalakul, and B Sirinaovakul. The best-so-far
selection in artificial bee colony algorithm. Applied Soft Computing,
2010.
[13] JC Bansal, H Sharma, A Nagar and KV Arya. "Balanced artificial
bee colony algorithm." International Journal of Artificial
Intelligence and Soft Computing 3.3 (2013): 222-243.
[14] D Haijun, F Qingxian. Bee colony algorithm for the function
optimization. Science Paper Online, August 2008.
[15] P.W. Tsai, J.S. Pan, B.Y. Liao, and S.C. Chu. Enhanced artificial
bee colony optimization. International Journal of Innovative
Computing, Information and Control, 5(12), 2009.
[16] Baykasoglu, L. Ozbakir, and P. Tapkan. Artificial bee colony
algorithm and its application to generalized assignment problem.
Swarm Intelligence: Focus on Ant and Particle Swarm Optimization,
pages 113–144, 2007.
[17] B. Akay and D. Karaboga. A modified artificial bee colony
algorithm for real-parameter optimization. Information Sciences,
2010.
[18] E. Mezura-Montes and O. Cetina-Dom´ınguez. Empirical analysis
of a modified artificial bee colony for constrained numerical
optimization. Applied Mathematics and Computation, 2012.
[19] G. Zhu and S. Kwong. Gbest-guided artificial bee colony algorithm
for numerical function optimization. Applied Mathematics and
Computation, 2010.
[20] J Kennedy and RC Eberhart. Particle swarm optimization. In
Proceedings IEEE int’l conf. on neural networks Vol. IV, pages
1942–1948, 1995.
[21] T. Derelia and GS Dasb. A hybrid’bee (s) algorithm’for solving
container loading problems. Applied Soft Computing, 2010.
[22] Y.M. Huang and J.C. Lin. A new bee colony optimization algorithm
with idle-time-based filtering scheme for open shop-scheduling
problems. Expert Systems with Applications, 2010.
[23] N. Suguna and K.G. Thanushkodi. An independent rough set
approach hybrid with artificial bee colony algorithm for
dimensionality reduction. American Journal of Applied Sciences, 8,
2011.
[24] B. Wu, C. Qian, W. Ni, and S. Fan. The improvement of glowworm
swarm optimization for continuous optimization problems. Expert
Systems with Applications, 2012.
[25] J.C. Bansal, H. Sharma, K.V. Arya and A. Nagar, “Memetic search
in artificial bee colony algorithm.” Soft Computing (2013): 1-18.
[26] S. Kumar, V.K. Sharma, and R. Kumari. "A Novel Hybrid
Crossover based Artificial Bee Colony Algorithm for Optimization
Problem." International Journal of Computer Applications 82, 2013.
[27] S. Pandey and S. Kumar "Enhanced Artificial Bee Colony
Algorithm and It’s Application to Travelling Salesman Problem."
HCTL Open International Journal of Technology Innovations and
Research, Volume 2, 2013:137-146.
[28] MM Ali, C Khompatraporn, and ZB Zabinsky. “A numerical
evaluation of several stochastic algorithms on selected continuous
global optimization test problems.” J. of Global Optimization,
31(4):635–672, 2005.
[29] P.N. Suganthan, N. Hansen, J.J. Liang, K. Deb, YP Chen, A. Auger,
and S. Tiwari. “Problem definitions and evaluation criteria for the
CEC 2005 special session on real-parameter optimization.” In CEC
2005, 2005.
[30] S Kumar, VK Sharma and R Kumari, “Randomized Memetic
Artificial Bee Colony Algorithm”. International Journal of
Emerging Trends & Technology in Computer 3(1): 52-62, March
2014.
[31] S Kumar, VK Sharma and R Kumari, “An Improved Memetic
Search in Artificial Bee Colony Algorithm”, International Journal of
Computer Science and Information Technology (0975 – 9646), 5(2):
1237-1247, 2014
[32] S Kumar, VK Sharma and R Kumari, “Modified Position Update in
Spider Monkey Optimization Algorithm”, International Journal of
Emerging Technologies in Computational and Applied Sciences
(IJETCAS), 7(2): 198-204, March 2014
[33] S Kumar, VK Sharma and R Kumari, “Enhanced Local Search in
Artificial Bee Colony Algorithm”, International Journal of
Emerging Technologies in Computational and Applied Sciences
(IJETCAS), 7(2): 177-184, March 2014
[34] S Kumar, VK Sharma and R Kumari, “Memetic Search in
Differential Evolution Algorithm”, International Journal of
Computer Applications (0975 – 8887) 90(6):40-47, March 2014.
Published by Foundation of Computer Science, New York, USA.
DOI: 10.5120/15582-4406
[35] S Kumar, VK Sharma and R Kumari, “Improved Onlooker Bee
Phase in Artificial Bee Colony Algorithm”, International Journal of
Computer Applications (0975 – 8887) 90(6):31-39, March 2014.
Published by Foundation of Computer Science, New York, USA.
DOI: 10.5120/15579-4304
[36] S Kumar, VK Sharma and R Kumari, “Comparative study of
Hybrids of Artificial Bee Colony Algorithm”, International Journal
of Information, Communication and Computing Technology
(IJICCT) Volume 1, Issue 2, pages: 20-28. Feb 2014.
Sandeep Kumar et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) , 2014, 2559-2565
www.ijcsit.com 2565