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
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.
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) 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
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) 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.
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 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.
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
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.
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) 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
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) 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.
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 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.
This document discusses several metaheuristic optimization algorithms, including Ant Colony Optimization (ACO), Firefly Algorithm, Modified Firefly Algorithm, BAT Algorithm, and Artificial Bee Colony (ABC) algorithm. It provides brief overviews of each algorithm, describing how they are inspired by natural behaviors and processes and outlining their main rules and procedures. The document is presented by Dr. C.Gokul and discusses these algorithms for optimization and problem solving.
The document summarizes the Whale Optimization Algorithm (WOA), which is a meta-heuristic optimization algorithm inspired by the hunting behavior of humpback whales. It describes how WOA simulates the bubble-net feeding mechanism of humpback whales to optimize problem solutions. The algorithm includes steps of encircling prey to find the best solution, then exploiting and exploring further to update positions and potentially find an even better solution. WOA iterates through these steps until a termination criterion is met, at which point it outputs the best found solution.
Optimal Motion Planning For a Robot Arm by Using Artificial Bee Colony (ABC) ...IJMER
The document describes using the artificial bee colony (ABC) algorithm to optimize the trajectory of a 2R robotic arm. The trajectory is divided into segments connected by intermediate points, with the goal of minimizing travel time and space while not exceeding torque limits. The ABC algorithm is applied to optimize six parameters - intermediate joint angles, velocities, and execution times - to minimize a fitness function evaluating excessive torque, joint travel distance, Cartesian length, and total time. The algorithm is tested for trajectories in free space and with circular obstacles.
This is about Comparative Analysis of Artificial Bee Colony and Improve Cuckoo Search algorithm, a thesis work done by us. Finally it is published on February-10-2015 on IJARAI. Here you will find the basic of ABC algorithm, ICS algorithm and the comparison between them.
The document discusses various metaheuristic algorithms for optimization problems including particle swarm optimization, bee colony optimization, ant colony optimization, and cuckoo search. It explains the components and mechanisms of these algorithms, provides pseudocode examples, and evaluates them in comparison to other metaheuristics like genetic algorithms and simulated annealing. The metaheuristics aim to efficiently search large solution spaces by mimicking natural processes like swarming behavior.
A Modified Bee Colony Optimization Algorithm for Nurse Rostering ProblemAM Publications
Scheduling shifts to the nurses in the hospital is highly a complex problem. The Nurse Scheduling
Problem (NRP) is considered to be a NP-Hard. It can be solved by combinatorial optimization problem. This paper
proposes a modified BCO algorithm for solving the problem. The modified Bee Colony Optimization algorithm
modifies the forward pass phases by introducing a pipelined constructive move followed by local search and
discarding move for solving Nurse Rostering Problem.
The document discusses the artificial bee colony (ABC) algorithm, which is an optimization algorithm inspired by the foraging behavior of honey bee swarms. It summarizes the ABC algorithm's main components: employed bees, onlooker bees, scout bees, and how they work together to iteratively improve potential solutions. An example application of the ABC algorithm to minimize a 2D function is provided to demonstrate how the algorithm progresses through cycles of the bee phases and updates potential solutions based on their "fitness".
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
Solving np hard problem artificial bee colony algorithmIAEME Publication
The document discusses using an artificial bee colony (ABC) algorithm to solve the NP-hard shortest common supersequence problem. It begins by introducing ABC algorithms and their inspiration from honey bee behavior. It then discusses previous approaches to solving the shortest common supersequence problem and outlines the proposed ABC approach. The ABC approach generates candidate solutions, calculates their fitness by comparing them to input strings, and iteratively improves solutions until termination criteria are met. Experimental results show the ABC approach finds solutions close to optimal.
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.
Solving np hard problem artificial bee colony algorithmIAEME Publication
The document discusses using an artificial bee colony (ABC) algorithm to solve the NP-hard shortest common supersequence problem. It begins by introducing ABC algorithms and their inspiration from honey bee behavior. It then discusses previous approaches to solving the shortest common supersequence problem and outlines the proposed ABC approach. The ABC approach calculates character frequencies, generates candidate solutions randomly based on frequencies, evaluates candidates against input strings, and iteratively improves candidates until termination criteria are met. Experimental results show the ABC approach finds solutions close to optimal.
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.
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 summarizes two nature-inspired metaheuristic algorithms: the Cuckoo Search algorithm and the Firefly algorithm.
The Cuckoo Search algorithm is based on the brood parasitism of some cuckoo species. It lays its eggs in the nests of other host birds. The algorithm uses Lévy flights for generating new solutions and considers the best solutions for the next generation.
The Firefly algorithm is based on the flashing patterns of fireflies to attract mates. It considers attractiveness that decreases with distance and movement of fireflies towards more attractive ones. The pseudo codes of both algorithms are provided along with some example applications.
Optimal k-means clustering using artificial bee colony algorithm with variab...IJECEIAES
Clustering is a robust machine learning task that involves dividing data points into a set of groups with similar traits. One of the widely used methods in this regard is the k-means clustering algorithm due to its simplicity and effectiveness. However, this algorithm suffers from the problem of predicting the number and coordinates of the initial clustering centers. In this paper, a method based on the first artificial bee colony algorithm with variable-length individuals is proposed to overcome the limitations of the k-means algorithm. Therefore, the proposed technique will automatically predict the clusters number (the value of k) and determine the most suitable coordinates for the initial centers of clustering instead of manually presetting them. The results were encouraging compared with the traditional k-means algorithm on three real-life clustering datasets. The proposed algorithm outperforms the traditional k-means algorithm for all tested real-life datasets.
COMPARISON BETWEEN ARTIFICIAL BEE COLONY ALGORITHM, SHUFFLED FROG LEAPING ALG...cscpconf
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.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
This document discusses swarm intelligence algorithms including particle swarm optimization (PSO) and ant colony optimization (ACO). It begins with an overview of swarm intelligence as the collective behavior of decentralized and self-organized systems, with characteristics like simple local rules and no centralized control. It then covers metaheuristics, PSO, ACO, and a case study applying PSO to data clustering. PSO is presented as optimizing a problem by updating particles based on personal and global best positions. ACO draws inspiration from how ants find food via pheromone trails, with the algorithm constructing solutions probabilistically based on pheromone levels. The case study shows PSO clustering outperforming K-means by avoiding local optima
Volume 14 issue 03 march 2014_ijcsms_march14_10_14_rahulDeepak Agarwal
1) The document presents a hybrid approach for feature subset selection that combines artificial bee colony and particle swarm optimization algorithms.
2) It applies this approach to three datasets from a public repository to select optimal feature subsets and compares the classification accuracy to other algorithms.
3) The results show the proposed hybrid approach achieves better classification accuracy on all three datasets compared to using artificial bee colony or random selection alone.
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This document discusses several metaheuristic optimization algorithms, including Ant Colony Optimization (ACO), Firefly Algorithm, Modified Firefly Algorithm, BAT Algorithm, and Artificial Bee Colony (ABC) algorithm. It provides brief overviews of each algorithm, describing how they are inspired by natural behaviors and processes and outlining their main rules and procedures. The document is presented by Dr. C.Gokul and discusses these algorithms for optimization and problem solving.
The document summarizes the Whale Optimization Algorithm (WOA), which is a meta-heuristic optimization algorithm inspired by the hunting behavior of humpback whales. It describes how WOA simulates the bubble-net feeding mechanism of humpback whales to optimize problem solutions. The algorithm includes steps of encircling prey to find the best solution, then exploiting and exploring further to update positions and potentially find an even better solution. WOA iterates through these steps until a termination criterion is met, at which point it outputs the best found solution.
Optimal Motion Planning For a Robot Arm by Using Artificial Bee Colony (ABC) ...IJMER
The document describes using the artificial bee colony (ABC) algorithm to optimize the trajectory of a 2R robotic arm. The trajectory is divided into segments connected by intermediate points, with the goal of minimizing travel time and space while not exceeding torque limits. The ABC algorithm is applied to optimize six parameters - intermediate joint angles, velocities, and execution times - to minimize a fitness function evaluating excessive torque, joint travel distance, Cartesian length, and total time. The algorithm is tested for trajectories in free space and with circular obstacles.
This is about Comparative Analysis of Artificial Bee Colony and Improve Cuckoo Search algorithm, a thesis work done by us. Finally it is published on February-10-2015 on IJARAI. Here you will find the basic of ABC algorithm, ICS algorithm and the comparison between them.
The document discusses various metaheuristic algorithms for optimization problems including particle swarm optimization, bee colony optimization, ant colony optimization, and cuckoo search. It explains the components and mechanisms of these algorithms, provides pseudocode examples, and evaluates them in comparison to other metaheuristics like genetic algorithms and simulated annealing. The metaheuristics aim to efficiently search large solution spaces by mimicking natural processes like swarming behavior.
A Modified Bee Colony Optimization Algorithm for Nurse Rostering ProblemAM Publications
Scheduling shifts to the nurses in the hospital is highly a complex problem. The Nurse Scheduling
Problem (NRP) is considered to be a NP-Hard. It can be solved by combinatorial optimization problem. This paper
proposes a modified BCO algorithm for solving the problem. The modified Bee Colony Optimization algorithm
modifies the forward pass phases by introducing a pipelined constructive move followed by local search and
discarding move for solving Nurse Rostering Problem.
The document discusses the artificial bee colony (ABC) algorithm, which is an optimization algorithm inspired by the foraging behavior of honey bee swarms. It summarizes the ABC algorithm's main components: employed bees, onlooker bees, scout bees, and how they work together to iteratively improve potential solutions. An example application of the ABC algorithm to minimize a 2D function is provided to demonstrate how the algorithm progresses through cycles of the bee phases and updates potential solutions based on their "fitness".
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
Solving np hard problem artificial bee colony algorithmIAEME Publication
The document discusses using an artificial bee colony (ABC) algorithm to solve the NP-hard shortest common supersequence problem. It begins by introducing ABC algorithms and their inspiration from honey bee behavior. It then discusses previous approaches to solving the shortest common supersequence problem and outlines the proposed ABC approach. The ABC approach generates candidate solutions, calculates their fitness by comparing them to input strings, and iteratively improves solutions until termination criteria are met. Experimental results show the ABC approach finds solutions close to optimal.
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.
Solving np hard problem artificial bee colony algorithmIAEME Publication
The document discusses using an artificial bee colony (ABC) algorithm to solve the NP-hard shortest common supersequence problem. It begins by introducing ABC algorithms and their inspiration from honey bee behavior. It then discusses previous approaches to solving the shortest common supersequence problem and outlines the proposed ABC approach. The ABC approach calculates character frequencies, generates candidate solutions randomly based on frequencies, evaluates candidates against input strings, and iteratively improves candidates until termination criteria are met. Experimental results show the ABC approach finds solutions close to optimal.
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.
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 summarizes two nature-inspired metaheuristic algorithms: the Cuckoo Search algorithm and the Firefly algorithm.
The Cuckoo Search algorithm is based on the brood parasitism of some cuckoo species. It lays its eggs in the nests of other host birds. The algorithm uses Lévy flights for generating new solutions and considers the best solutions for the next generation.
The Firefly algorithm is based on the flashing patterns of fireflies to attract mates. It considers attractiveness that decreases with distance and movement of fireflies towards more attractive ones. The pseudo codes of both algorithms are provided along with some example applications.
Optimal k-means clustering using artificial bee colony algorithm with variab...IJECEIAES
Clustering is a robust machine learning task that involves dividing data points into a set of groups with similar traits. One of the widely used methods in this regard is the k-means clustering algorithm due to its simplicity and effectiveness. However, this algorithm suffers from the problem of predicting the number and coordinates of the initial clustering centers. In this paper, a method based on the first artificial bee colony algorithm with variable-length individuals is proposed to overcome the limitations of the k-means algorithm. Therefore, the proposed technique will automatically predict the clusters number (the value of k) and determine the most suitable coordinates for the initial centers of clustering instead of manually presetting them. The results were encouraging compared with the traditional k-means algorithm on three real-life clustering datasets. The proposed algorithm outperforms the traditional k-means algorithm for all tested real-life datasets.
COMPARISON BETWEEN ARTIFICIAL BEE COLONY ALGORITHM, SHUFFLED FROG LEAPING ALG...cscpconf
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.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
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This document discusses swarm intelligence algorithms including particle swarm optimization (PSO) and ant colony optimization (ACO). It begins with an overview of swarm intelligence as the collective behavior of decentralized and self-organized systems, with characteristics like simple local rules and no centralized control. It then covers metaheuristics, PSO, ACO, and a case study applying PSO to data clustering. PSO is presented as optimizing a problem by updating particles based on personal and global best positions. ACO draws inspiration from how ants find food via pheromone trails, with the algorithm constructing solutions probabilistically based on pheromone levels. The case study shows PSO clustering outperforming K-means by avoiding local optima
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1) The document presents a hybrid approach for feature subset selection that combines artificial bee colony and particle swarm optimization algorithms.
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3) The results show the proposed hybrid approach achieves better classification accuracy on all three datasets compared to using artificial bee colony or random selection alone.
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Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
1. Introduction
Behaviour of Honey Bee Swarm
Position update process in NIAs
Artificial Bee Colony (ABC) algorithm
Artificial Bee Colony Algorithm
By:
Dr. Harish Sharma
Associate Professor
Department of Computer Engineering
Rajasthan Technical University, Kota
Email: hsharma@rtu.ac.in
Mob. No. 9461174365
2. Introduction
Behaviour of Honey Bee Swarm
Position update process in NIAs
Artificial Bee Colony (ABC) algorithm
Engineering Optimization Problems
Nature-Inspired Algorithms
Swarm Intelligence
Nature-Inspired Algorithms I
Nature-Inspired Algorithms can be defined as population based stochastic
metaheuristic which imitate some natural phenomenon to find an optimum
solution of a problem.
The main idea of these algorithms is: individuals updates itself using:
1 The knowledge of the environment (its fitness value)
2 The individual’s previous history of states (its memory)
3 The previous history of states of the individual’s neighborhood
Often real world provides some complex optimization problems that can not be
easily dealt with available mathematical optimization methods. If the user is not
very conscious about the exact solution of the problem in hand then
nature-inspired algorithms may be used to solve these kind of problems.
3. Introduction
When We Use NIAs ??
If any deterministic method is available for the optimization problems, we do not
need to switch for NIAs.
But sometime complex optimization problems can not be easily deal with
available mathematical optimization methods.
So when the user is not very conscious about the exact solution of the problem
in hand, then nature-inspired algorithms may be used to solve these kind of
problems.
4. Introduction
Why We Use NIAs ??
Applicable to wider set of problems i.e. mathematical formation of function is not
required.
Use the stochastic or probabilistic approach i.e. random approach.
Gives near optimal solution to these problems.
Works on the basis of fitness evaluation.
5. Introduction
Optimization
An art of selecting the best alternative(s) amongst a given set of options or finding the
values of the variable that maximize or minimize the objective function while satisfying the
constraints.
F(X) = X2
1 + X2
2
Main components of an optimization problem
F(X) = Objective Function
X1 and X2 = Decision Variables
limit = −5 ≤ X1, X2 ≤ 5
Dimensions = 2
The main objective is to find the values of X1 and X2 Such that objective function F(X)
should be minimized.
10. Introduction
Some Basic Terminologies in NIA I
Exploration
Exploration is the process of visiting entirely new regions of a search space.
Exploitation
Exploitation is the process of visiting those regions of a search space within the neighborhood of
previously visited points.
Stagnation
Stagnation refers to a situation in which the optimum seeking process stagnates before finding a
globally optimal solution.
Premature Convergence
A population for an optimization problem converged too early, resulting in being suboptimal.
11. Introduction
Some Basic Terminologies in NIA
Stochastic Nature
Intelligence + Randomness + Previous Experience
Heuristic and Meta-heuristics
Heuristics are problem-dependent techniques and Meta-heuristics problem-independent
techniques.
12. Introduction
Behaviour of Honey Bee Swarm
Position update process in NIAs
Artificial Bee Colony (ABC) algorithm
Engineering Optimization Problems
Nature-Inspired Algorithms
Swarm Intelligence
Nature-Inspired Algorithms
Important Classes ofPopulationbased
StochasticOptimizationAlgorithms
Evolutionary
Algorithms
Swarm-Intelligence
based Algorithms
Evolutionary algorithm (EA).
Swarm Intelligence: The definition given by Bonabeau is
Any attempt to design algorithms or distributed problem-solving devices inspired by the
collective behaviour of social insect colonies and other animal societies
13. Introduction
Behaviour of Honey Bee Swarm
Position update process in NIAs
Artificial Bee Colony (ABC) algorithm
Engineering Optimization Problems
Bee Behaviour
Dance
Artificial Bee Colony algorithm (ABC), introduced by D.
Karaboga in 2005, is a swarm-intelligence based optimization
algorithm.
The minimal model of forage selection that leads to the
emergence of collective intelligence of honey bee swarms
consists of three essential components:
1 Food sources,
2 Employed foragers
3 Unemployed foragers
i Scouts
ii Onlookers
14. Introduction
Behaviour of Honey Bee Swarm
Position update process in NIAs
Artificial Bee Colony (ABC) algorithm
Engineering Optimization Problems
Bee Behaviour
Dance
(a) (b) (c)
Food Sources: The value of a food source depends on its proximity to the nest, its richness,
and the ease of extracting nectar.
Employed foragers: They are associated with a particular food source which they are
currently exploiting or are “employed at. They carry with them information about this
particular source, its distance and direction from the nest and share this information with a
certain probability.
Unemployed foragers: They are continually at look out for a food source to exploit. There
are two types of unemployed foragers:
1 Scouts: searching the environment surrounding the nest for new food sources and
2 Onlookers: waiting in the nest and establishing a food source through the information shared by
employed foragers
15. Introduction
Behaviour of Honey Bee Swarm
Position update process in NIAs
Artificial Bee Colony (ABC) algorithm
Engineering Optimization Problems
Bee Behaviour
Dance
After unloading the nectar, the forager bee which has found a rich source performs
special movements called dance on the area of the comb in order to share her
information about the food source such as how plentiful it is, its direction and distance
and recruits the other bees for exploiting that rich source.
While dancing, other bees touch her with their antenna and learn the scent and the
taste of the source she is exploiting. She dances on different areas of the comb in
order to recruit more bees and goes on to collect nectar from her source.
1 Round dance: If the distance of the source to the hive is less than 100 meters.
Round dance does not give direction information.
2 waggle dance: When the source is far away. In case of waggle dance, direction
of the source according to the sun is transferred to other bees. Longer distances
cause quicker dances.
3 Tremble dance: When the foraging bee perceives a long delay in unloading its
nectar.
16. Introduction
Behaviour of Honey Bee Swarm
Position update process in NIAs
Artificial Bee Colony (ABC) algorithm
Engineering Optimization Problems
Bee Behaviour
Dance
Social behaviour of Honey Bees
Figure: Social behaviour of Honey Bees
17. Introduction
Behaviour of Honey Bee Swarm
Position update process in NIAs
Artificial Bee Colony (ABC) algorithm
Engineering Optimization Problems
Basic Step of position update of an individual
Position updated procedure is an important and crucial part of the nature inspired algorithms as it is
the step where members learn from society and update itself accordingly.
New Position = Persistence + Social Influence
The social learning/Social Influence of ABC is based on difference vectors i.e. variation component.
The generalized position updated equation of the algorithms is as follows:
xnext = xcurrent + B ×
Variation Component
z }| {
(x1 − x2)
| {z }
Step size
.
18. Introduction
Behaviour of Honey Bee Swarm
Position update process in NIAs
Artificial Bee Colony (ABC) algorithm
Engineering Optimization Problems
ABC I
Initialization of the population
Each food source is generated as follows:.
xij = xminj + rand[0, 1](xmaxj − xminj ) (1)
where xminj and xmaxj are bounds of xi in jth direction and rand[0, 1] is a uniformly
distributed random number in the range [0, 1].
Employed bee phase
The position update equation for ith candidate in this phase is
vij = xij + φij (xij − xkj ) (2)
where k ∈ {1, 2, ..., SN} and j ∈ {1, 2, ..., D} are randomly chosen indices. k must be
different from i. φij is a random number between [-1, 1].
Slide 10/ 17
19. Introduction
Behaviour of Honey Bee Swarm
Position update process in NIAs
Artificial Bee Colony (ABC) algorithm
Engineering Optimization Problems
ABC II
(a)
x1
x2
xij
vij
vij
'
vij
'
'
vij
'
'
'
(b)
Figure: (a) Illustrating a simple position update equation execution,
(b) Different possible new vectors formed in neighborhood of xij due
to position update equation in 2-D search space.
20. Introduction
Behaviour of Honey Bee Swarm
Position update process in NIAs
Artificial Bee Colony (ABC) algorithm
Engineering Optimization Problems
ABC III
Onlooker bees phase
The probability pi may be calculated using following expression:
pi =
fiti
PSN
i=1 fiti
(3)
where fiti is the fitness value of the solution i.
Scout bees phase
If the position of a food source is not updated up to predetermined number of cycles,
then the food source is assumed to be abandoned and scout bees phase starts.
Harish Sharma
21. Introduction
Behaviour of Honey Bee Swarm
Position update process in NIAs
Artificial Bee Colony (ABC) algorithm
Artificial Bee Colony Algorithm
Initialize the population of solutions, xi (i = 1, 2, ...; SN);
cycle = 1;
while cycle <> MCN do
Produce new solutions vi for the employed bees and evaluate them;
Apply the greedy selection process for the employed bees;
Calculate the probability values pi for the solutions xi ;
Produce the new solutions vi for the onlookers from the solutions xi selected
depending on pi and evaluate them;
Apply the greedy selection process for the onlookers;
Determine the abandoned solution for the scout, if exists, and replace it with a
new randomly produced solution xi ;
Memorize the best solution achieved so far;
cycle = cycle + 1;
end while
ABC Step by Step Example