In this paper a new evolutionary algorithm, for continuous nonlinear optimization problems, is surveyed.
This method is inspired by the life of a bird, called Cuckoo.
The Cuckoo Optimization Algorithm (COA) is evaluated by using the Rastrigin function. The problem is a
non-linear continuous function which is used for evaluating optimization algorithms. The efficiency of the
COA has been studied by obtaining optimal solution of various dimensions Rastrigin function in this paper.
The mentioned function also was solved by FA and ABC algorithms. Comparing the results shows the COA
has better performance than other algorithms.
Application of algorithm to test function has proven its capability to deal with difficult optimization
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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
The document summarizes the flower pollination algorithm, a nature-inspired optimization technique. It describes how flower pollination occurs in nature through both biotic cross-pollination involving pollinators, and abiotic self-pollination involving wind and water. The algorithm mimics these processes to update solution populations towards optimization. It initializes a population randomly, then iteratively explores through global pollination modeled by Levy flights, and exploits through local pollination. Applications include engineering problems, neural networks, and scheduling.
Comparison between pid controllers for gryphon robot optimized with neuro fuz...ijctcm
In this paper three intelligent evolutionary optimization approaches to design PID controller for a
Gryphon Robot are presented and compared to the results of a neuro-fuzzy system applied. The three
applied approaches are artificial bee colony, shuffled frog leaping algorithm and particle swarm
optimization. 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 the four optimization methods mentioned above. 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. On the other hand PSO sounds to perform well on steady-state error
only. In steady state manner all of the methods react robustly to the disturbance, but FNN shows more
stability in transient response.
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.
Multi-objective Flower Algorithm for OptimizationXin-She Yang
The document proposes a multi-objective flower algorithm (MOFPA) for optimization. MOFPA extends the single-objective flower pollination algorithm (FPA) to solve multi-objective problems. MOFPA uses a weighted sum approach to combine multiple objectives into a single objective function. Random weights are used to find an accurate Pareto front with uniformly distributed solutions. MOFPA is tested on standard benchmark functions and shown to converge quickly, finding Pareto fronts accurately.
Recent Advances in Flower Pollination AlgorithmEditor IJCATR
Flower Pollination Algorithm (FPA) is a nature inspired algorithm based on pollination process of plants. Recently, FPA
has become a popular algorithm in the evolutionary computation field due to its superiority to many other algorithms. As a
consequence, in this paper, FPA, its improvements, its hybridization and applications in many fields, such as operations research,
engineering and computer science, are discussed and analyzed. Based on its applications in the field of optimization it was seemed that
this algorithm has a better convergence speed compared to other algorithms. The survey investigates the difference between FPA
versions as well as its applications. To add to this, several future improvements are suggested.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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
The document summarizes the flower pollination algorithm, a nature-inspired optimization technique. It describes how flower pollination occurs in nature through both biotic cross-pollination involving pollinators, and abiotic self-pollination involving wind and water. The algorithm mimics these processes to update solution populations towards optimization. It initializes a population randomly, then iteratively explores through global pollination modeled by Levy flights, and exploits through local pollination. Applications include engineering problems, neural networks, and scheduling.
Comparison between pid controllers for gryphon robot optimized with neuro fuz...ijctcm
In this paper three intelligent evolutionary optimization approaches to design PID controller for a
Gryphon Robot are presented and compared to the results of a neuro-fuzzy system applied. The three
applied approaches are artificial bee colony, shuffled frog leaping algorithm and particle swarm
optimization. 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 the four optimization methods mentioned above. 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. On the other hand PSO sounds to perform well on steady-state error
only. In steady state manner all of the methods react robustly to the disturbance, but FNN shows more
stability in transient response.
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.
Multi-objective Flower Algorithm for OptimizationXin-She Yang
The document proposes a multi-objective flower algorithm (MOFPA) for optimization. MOFPA extends the single-objective flower pollination algorithm (FPA) to solve multi-objective problems. MOFPA uses a weighted sum approach to combine multiple objectives into a single objective function. Random weights are used to find an accurate Pareto front with uniformly distributed solutions. MOFPA is tested on standard benchmark functions and shown to converge quickly, finding Pareto fronts accurately.
Recent Advances in Flower Pollination AlgorithmEditor IJCATR
Flower Pollination Algorithm (FPA) is a nature inspired algorithm based on pollination process of plants. Recently, FPA
has become a popular algorithm in the evolutionary computation field due to its superiority to many other algorithms. As a
consequence, in this paper, FPA, its improvements, its hybridization and applications in many fields, such as operations research,
engineering and computer science, are discussed and analyzed. Based on its applications in the field of optimization it was seemed that
this algorithm has a better convergence speed compared to other algorithms. The survey investigates the difference between FPA
versions as well as its applications. To add to this, several future improvements are suggested.
Grid computing or network computing is developed to make the available electric power in the similar way
as it is available for the grid. For that we just plug in the power and whoever needs power, may use it. In
grid computing if a system needs more power than available it can share the computing with other
machines connected in a grid. In this way we can use the power of a super computer without a huge cost
and the CPU cycles that were wasted previously can also be utilized. For performing grid computation in
joined computers through the internet, the software must be installed which supports grid computation on
each computer inside the VO. The software handles information queries, storage management, processing
scheduling, authentication and data encryption to ensure information security.
MOVEMENT ASSISTED COMPONENT BASED SCALABLE FRAMEWORK FOR DISTRIBUTED WIRELESS...ijcsa
Intelligent networks are becoming more enveloping and dwelling a new generation of applications are
deployed over the peer-to-peer networks. Intelligent networks are very attractive because of their role in
improving the scalability and enhancing performance by enabling direct and real-time communication
among the participating network stations. A suitable solution for resource management in distributed wireless systems is required which should support fault-tolerant operations, requested resources (at shortest path), minimize overhead generation during network management, balancing the load distribution between the participating stations and high probability of lookup success and many more. This article
presents a Movement Assisted Component Based Scalable Framework (MAC-SF) for the distributed
network which manages the distributed wireless resources and applications; monitors the behavior of the
distributed wireless applications transparently and attains accurate resource projections, manages the
connections between the participating network stations and distributes the active objects in response to the
user requests and changing processing and network conditions. This system is also compared with some
exiting systems. Results shows that MAC-SF is a better system and can be used in any wireless network.
The proposed technique is capable of segmenting the lung’s air and its soft tissues followed by estimating
the lung’s air volume and its variations throughout the image sequence. The work presents a methodology
that consists of three steps. In the first step, CT image sequence are to be given as input where histograms
of all images within the sequence are calculated and they are overlaid in order to form the sequence’s
combined histogram to extract lung air area. In the second step, segment the lung air area by using
optimum threshold based method. In the third step, voxels in the rendered volume will be counted and the
percentage of air consumption will be estimated. The result will indicate a very good ability of the
proposed method for estimating the lung’s air volume and its variations in a respiratory image sequence.
Quality estimation of machine translation outputs through stemmingijcsa
Machine Translation is the challenging problem for Indian languages. Every day we can see some machine
translators being developed , but getting a high quality automatic translation is still a very distant dream .
The correct translated sentence for Hindi language is rarely found. In this paper, we are emphasizing on
English-Hindi language pair, so in order to preserve the correct MT output we present a ranking system,
which employs some machine learning techniques and morphological features. In ranking no human
intervention is required. We have also validated our results by comparing it with human ranking.
Logic coverage criteria are central aspect of programs and specifications in the testing of software. A
Boolean expression with n variables in expression 2n distinct test cases is required for exhaustive testing
.This is expensive even when n is modestly large. The possible solution is to select a small subset of all
possible test cases which can effectively detect most of the common faults. Test case prioritization is one of
the key techniques for making testing cost effective. In present study performance index of test suite is
calculated for two Boolean specification testing techniques MUMCUT and Minimal-MUMCUT.
Performance index helps to measure the efficiency and determine when testing can be stopped in case of
limited resources. This paper evaluates the testability of generated single faults according to the number of
test cases used to detect them. Test cases are generated from logic expressions in irredundant normal form
(IDNF) derived from specifications or source code. The efficiency of prioritization techniques has been
validated by an empirical study done on bench mark expressions using Performance Index (PI) metric.
Algeria engages with determination on the path renewable energies to bring global and long-lasting
solutions to the environmental challenges and to the problems of conservation of the energy resources of
fossil origin. Our study is interested on the wind spinneret which seems one of the most promising with a
very high global growth rate. The object of this article is to estimate the wind deposit of the region of Oran
(Es Senia), important stage in any planning and realization of wind plant. In our work, we began with the
processing of schedules data relative to the wind collected over a period of more than 50 years, to evaluate
the wind potential while determining its frequencies. Then, we calculated the total electrical energy
produced at various heights with three types of wind turbines.The analysis of the results shows that the
wind turbines of major powers allow producing important quantities of energy when we increase the height
of their hubs to take advantage of stronger speeds of wind.
Effect of mesh grid structure in reducing hot carrier effect of nmos device s...ijcsa
This paper presents the critical effect of mesh grid that should be considered during process and device
simulation using modern TCAD tools in order to develop and optimize their accurate electrical
characteristics. Here, the computational modelling process of developing the NMOS device structure is
performed in Athena and Atlas. The effect of Mesh grid on net doping profile, n++, and LDD sheet
resistance that could link to unwanted “Hot Carrier Effect” were investigated by varying the device grid
resolution in both directions. It is found that y-grid give more profound effect in the doping concentration,
the junction depth formation and the value of threshold voltage during simulation. Optimized mesh grid is
obtained and tested for more accurate and faster simulation. Process parameter (such as oxide thicknesses
and Sheet resistance) as well as Device Parameter (such as linear gain “beta” and SPICE level 3 mobility
roll-off parameter “ Theta”) are extracted and investigated for further different applications.
Hybrid hmmdtw based speech recognition with kernel adaptive filtering methodijcsa
This document summarizes a research paper that proposes a new approach for speech recognition using kernel adaptive filtering for speech enhancement and a hybrid HMM/DTW method for recognition. It first discusses adaptive filters and the LMS algorithm, then introduces kernel adaptive filters using the KLMS algorithm to transform input data into a high-dimensional feature space. Finally, it describes using HMM to train speech features and DTW for classification and recognition. The experimental results showed an improvement in recognition rates compared to traditional methods.
REAL TIME SPECIAL EFFECTS GENERATION AND NOISE FILTRATION OF AUDIO SIGNAL USI...ijcsa
Digital signal processing is being increasingly used for audio processing applications. Digital audio effects
refer to all those algorithms that are used for enhancing sound in any of the steps of a processing chain of
music production. Real time audio effects generation is a highly challenging task in the field of signal
processing. Now a day, almost every high end multimedia audio device does digital signal processing in
one form or another. For years musicians have used different techniques to give their music a unique
sound. Earlier, these techniques were implemented after a lot of work and experimentation. However, now
with the emergence of digital signal processing this task is simplified to a great extent. In this article, the
generations of special effects like echo, flanging, reverberation, stereo, karaoke, noise filtering etc are
successfully implemented using MATLAB and an attractive GUI has been designed for the same.
Graph coloring is the assignment of colors to the graph vertices and edges in the graph theory. We can
divide the graph coloring in two types. The first is vertex coloring and the second is edge coloring. The
condition which we follow in graph coloring is that the incident vertices/edges have not the same color.
There are some algorithms which solve the problem of graph coloring. Some are offline algorithm and
others are online algorithm. Where offline means the graph is known in advance and the online means that
the edges of the graph are arrive one by one as an input, and We need to color each edge as soon as it is
added to the graph and the main issue is that we want to minimize the number of colors. We cannot change
the color of an edge after colored in an online algorithm. In this paper, we improve the online algorithm
for edge coloring. There is also a theorem which proves that if the maximum degree of a graph is Δ, then it
is possible to color its edges, in polynomial time, using at most Δ+ 1 color. The algorithm provided by
Vizing is offline, i.e., it assumes the whole graph is known in advance. In online algorithm edges arrive one
by one in a random permutation. This online algorithm is inspired by a distributed offline algorithm of
Panconesi and Srinivasan, referred as PS algorithm, works on 2-rounds which we extend by reusing colors
online in multiple rounds.
Security & privacy issues of cloud & grid computing networksijcsa
Cloud computing is a new field in Internet computing that provides novel perspectives in internetworking
technologies. Cloud computing has become a significant technology in field of information technology.
Security of confidential data is a very important area of concern as it can make way for very big problems
if unauthorized users get access to it. Cloud computing should have proper techniques where data is
segregated properly for data security and confidentiality. This paper strives to compare and contrast cloud
computing with grid computing, along with the Tools and simulation environment & Tips to store data and
files safely in Cloud.
Augmented split –protocol; an ultimate d do s defenderijcsa
Distributed Denials of Service (DDoS) attacks have become the daunting problem for businesses, state
administrator and computer system users. Prevention and detection of a DDoS attack is a major research
topic for researchers throughout the world. As new remedies are developed to prevent or mitigate DDoS
attacks, invaders are continually evolving new methods to circumvent these new procedures. In this paper,
we describe various DDoS attack mechanisms, categories, scope of DDoS attacks and their existing
countermeasures. In response, we propose to introduce DDoS resistant Augmented Split-protocol (ASp).
The migratory nature and role changeover ability of servers in Split-protocol architecture will avoid
bottleneck at the server side. It also offers the unique ability to avoid server saturation and compromise
from DDoS attacks. The goal of this paper is to present the concept and performance of (ASp) as a
defensive tool against DDoS attacks.
STRICTLY CONFIDENTIAL - Carmine Di Blasio- SupervisorCarmine Di Blasio
Carmine Di Blasio is seeking a supervisory role where he can utilize his skills obtained over his career in construction and hospitality. He has over 20 years of experience in roles such as operator, supervisor, and general manager. His skills include strong communication, problem solving, relationship building, and the ability to manage teams and meet deadlines. He has managed projects ranging from $12.5M to $770M and overseen teams of 20-100 people.
This document provides a survey of ant colony optimization (ACO) approaches for routing and load balancing in computer networks. It begins with an overview of ACO, explaining how artificial ants emulate real ants to solve optimization problems. The document then compares ACO approaches to traditional routing algorithms in terms of routing information, overhead, and adaptivity. A major issue for ACO called stagnation is discussed, where the algorithm becomes stuck on suboptimal solutions. The document surveys approaches to mitigate stagnation, including pheromone evaporation, aging, and privileged pheromone laying. It also reviews three major areas of ACO research for routing/load balancing and discusses new directions and open problems.
The document provides observations from shadowing a pit crew instructor at an airport. It discusses key lessons learned around non-technical skills like crew resource management. Specifically, it highlights the importance of flexibility given changing flight schedules, constant communication between team members to coordinate workload, and thinking ahead to manage fatigue and prioritize tasks. It also emphasizes stress management, rotating roles to share workloads, and maintaining morale particularly during challenging situations like flight delays.
1. The document outlines SKJ Engineering & Grading's Heat Illness Prevention Plan, including procedures for water, shade, weather monitoring, high heat procedures, acclimatization, emergency response, handling sick employees, and employee training.
2. Key personnel responsible for implementing the plan are identified. Procedures describe how water and shade will be provided to workers, how weather will be monitored, and measures that will be taken during high heat or a heat wave.
3. The plan also includes procedures for acclimatizing new employees, responding to emergencies and heat illness, and training supervisors and employees on the company's prevention measures.
This document contains a resume for Jason S. Gagen. It summarizes his career objective of obtaining a transportation construction supervisor or manager position. It then lists his certifications and work experience in transportation construction and inspection roles over nearly 20 years, primarily with Hill International and Dick Corporation. It provides details on his duties and supervisors in these roles. Earlier in his career, Gagen worked as an EEG technician before transitioning into the transportation field. The resume concludes with his education history and special interests.
AN ENHANCED EDGE ADAPTIVE STEGANOGRAPHY APPROACH USING THRESHOLD VALUE FOR RE...ijcsa
The document summarizes an enhanced edge adaptive steganography approach using a threshold value for region selection. It aims to improve the quality and modification rate of a stego image compared to Sobel and Canny edge detection techniques. The proposed approach uses a threshold value to select high frequency pixels from the cover image for data embedding using LSBMR. Experimental results on 100 images show about a 0.2-0.6% improvement in image quality measured by PSNR and a 4-10% improvement in modification rate measured by MSE compared to Sobel and Canny edge detection.
Mining sequential patterns for interval basedijcsa
Sequential pattern mining finds the frequent subsequences or patterns from the given sequences.
TPrefixSpan algorithm finds the relevant frequent patterns from the given sequential patterns formed using
interval based events. In our proposed work, we add multiple constraints like item, length and aggregate to
the interval based TPrefixSpan algorithm. By adding these constraints the efficiency and effectiveness of
the algorithm improves. The proposed constraint based algorithm CTPrefixSpan has been applied to
synthetic medical dataset. The algorithm can be applied for stock market analysis, DNA sequences analysis
etc.
KEYWORDS
Sequential patterns, temporal patterns, Constraints, Interval based events.
Flix/Slim is a dual-personality club divided into two rooms, Flix playing Thai pop and live music and Slim playing hip-hop. It can get overcrowded on weekends. There is seating outside and stylish decor inside. The Flix side has three live bands nightly playing Thai covers along with monthly concerts and surprise appearances. The club is open Wednesdays and weekends, with free admission but ID required.
In this modern era a great deal of metamorphism is observed around us which eventuate due to some
minute modifications and innovations in the area of Science and Technology. This paper deals with the
application of a meta heuristic optimization algorithm namely the Cuckoo Search Algorithm in the design
of an optimized planar antenna array which ensures high gain ,directivity, suppression of side lobes,
increased efficiency and improves other antenna parameters as well[1], [2] and [3].
PORTFOLIO SELECTION BY THE MEANS OF CUCKOO OPTIMIZATION ALGORITHMijcsa
Portfolio selection is one of the most important and vital decisions that a real or legal person, who invests in stock market should make. The main purpose of this paper is the determination of the optimal portfolio with regard to stock returns of companies, which are active in Tehran’s stock market. For achieving this purpose, annual statistics of companies’ stocks since Farvardin 1387 until Esfand 1392 have been used. For analyzing statistics, information of companies’ stocks, the Cuckoo Optimization Algorithm (COA) and Knapsack Problem have been used with the aim of increasing the total return, in order to form a financial portfolio. At last, results merits of choosing the optimal portfolio using the COA rather than Genetic
Algorithm are given.
Grid computing or network computing is developed to make the available electric power in the similar way
as it is available for the grid. For that we just plug in the power and whoever needs power, may use it. In
grid computing if a system needs more power than available it can share the computing with other
machines connected in a grid. In this way we can use the power of a super computer without a huge cost
and the CPU cycles that were wasted previously can also be utilized. For performing grid computation in
joined computers through the internet, the software must be installed which supports grid computation on
each computer inside the VO. The software handles information queries, storage management, processing
scheduling, authentication and data encryption to ensure information security.
MOVEMENT ASSISTED COMPONENT BASED SCALABLE FRAMEWORK FOR DISTRIBUTED WIRELESS...ijcsa
Intelligent networks are becoming more enveloping and dwelling a new generation of applications are
deployed over the peer-to-peer networks. Intelligent networks are very attractive because of their role in
improving the scalability and enhancing performance by enabling direct and real-time communication
among the participating network stations. A suitable solution for resource management in distributed wireless systems is required which should support fault-tolerant operations, requested resources (at shortest path), minimize overhead generation during network management, balancing the load distribution between the participating stations and high probability of lookup success and many more. This article
presents a Movement Assisted Component Based Scalable Framework (MAC-SF) for the distributed
network which manages the distributed wireless resources and applications; monitors the behavior of the
distributed wireless applications transparently and attains accurate resource projections, manages the
connections between the participating network stations and distributes the active objects in response to the
user requests and changing processing and network conditions. This system is also compared with some
exiting systems. Results shows that MAC-SF is a better system and can be used in any wireless network.
The proposed technique is capable of segmenting the lung’s air and its soft tissues followed by estimating
the lung’s air volume and its variations throughout the image sequence. The work presents a methodology
that consists of three steps. In the first step, CT image sequence are to be given as input where histograms
of all images within the sequence are calculated and they are overlaid in order to form the sequence’s
combined histogram to extract lung air area. In the second step, segment the lung air area by using
optimum threshold based method. In the third step, voxels in the rendered volume will be counted and the
percentage of air consumption will be estimated. The result will indicate a very good ability of the
proposed method for estimating the lung’s air volume and its variations in a respiratory image sequence.
Quality estimation of machine translation outputs through stemmingijcsa
Machine Translation is the challenging problem for Indian languages. Every day we can see some machine
translators being developed , but getting a high quality automatic translation is still a very distant dream .
The correct translated sentence for Hindi language is rarely found. In this paper, we are emphasizing on
English-Hindi language pair, so in order to preserve the correct MT output we present a ranking system,
which employs some machine learning techniques and morphological features. In ranking no human
intervention is required. We have also validated our results by comparing it with human ranking.
Logic coverage criteria are central aspect of programs and specifications in the testing of software. A
Boolean expression with n variables in expression 2n distinct test cases is required for exhaustive testing
.This is expensive even when n is modestly large. The possible solution is to select a small subset of all
possible test cases which can effectively detect most of the common faults. Test case prioritization is one of
the key techniques for making testing cost effective. In present study performance index of test suite is
calculated for two Boolean specification testing techniques MUMCUT and Minimal-MUMCUT.
Performance index helps to measure the efficiency and determine when testing can be stopped in case of
limited resources. This paper evaluates the testability of generated single faults according to the number of
test cases used to detect them. Test cases are generated from logic expressions in irredundant normal form
(IDNF) derived from specifications or source code. The efficiency of prioritization techniques has been
validated by an empirical study done on bench mark expressions using Performance Index (PI) metric.
Algeria engages with determination on the path renewable energies to bring global and long-lasting
solutions to the environmental challenges and to the problems of conservation of the energy resources of
fossil origin. Our study is interested on the wind spinneret which seems one of the most promising with a
very high global growth rate. The object of this article is to estimate the wind deposit of the region of Oran
(Es Senia), important stage in any planning and realization of wind plant. In our work, we began with the
processing of schedules data relative to the wind collected over a period of more than 50 years, to evaluate
the wind potential while determining its frequencies. Then, we calculated the total electrical energy
produced at various heights with three types of wind turbines.The analysis of the results shows that the
wind turbines of major powers allow producing important quantities of energy when we increase the height
of their hubs to take advantage of stronger speeds of wind.
Effect of mesh grid structure in reducing hot carrier effect of nmos device s...ijcsa
This paper presents the critical effect of mesh grid that should be considered during process and device
simulation using modern TCAD tools in order to develop and optimize their accurate electrical
characteristics. Here, the computational modelling process of developing the NMOS device structure is
performed in Athena and Atlas. The effect of Mesh grid on net doping profile, n++, and LDD sheet
resistance that could link to unwanted “Hot Carrier Effect” were investigated by varying the device grid
resolution in both directions. It is found that y-grid give more profound effect in the doping concentration,
the junction depth formation and the value of threshold voltage during simulation. Optimized mesh grid is
obtained and tested for more accurate and faster simulation. Process parameter (such as oxide thicknesses
and Sheet resistance) as well as Device Parameter (such as linear gain “beta” and SPICE level 3 mobility
roll-off parameter “ Theta”) are extracted and investigated for further different applications.
Hybrid hmmdtw based speech recognition with kernel adaptive filtering methodijcsa
This document summarizes a research paper that proposes a new approach for speech recognition using kernel adaptive filtering for speech enhancement and a hybrid HMM/DTW method for recognition. It first discusses adaptive filters and the LMS algorithm, then introduces kernel adaptive filters using the KLMS algorithm to transform input data into a high-dimensional feature space. Finally, it describes using HMM to train speech features and DTW for classification and recognition. The experimental results showed an improvement in recognition rates compared to traditional methods.
REAL TIME SPECIAL EFFECTS GENERATION AND NOISE FILTRATION OF AUDIO SIGNAL USI...ijcsa
Digital signal processing is being increasingly used for audio processing applications. Digital audio effects
refer to all those algorithms that are used for enhancing sound in any of the steps of a processing chain of
music production. Real time audio effects generation is a highly challenging task in the field of signal
processing. Now a day, almost every high end multimedia audio device does digital signal processing in
one form or another. For years musicians have used different techniques to give their music a unique
sound. Earlier, these techniques were implemented after a lot of work and experimentation. However, now
with the emergence of digital signal processing this task is simplified to a great extent. In this article, the
generations of special effects like echo, flanging, reverberation, stereo, karaoke, noise filtering etc are
successfully implemented using MATLAB and an attractive GUI has been designed for the same.
Graph coloring is the assignment of colors to the graph vertices and edges in the graph theory. We can
divide the graph coloring in two types. The first is vertex coloring and the second is edge coloring. The
condition which we follow in graph coloring is that the incident vertices/edges have not the same color.
There are some algorithms which solve the problem of graph coloring. Some are offline algorithm and
others are online algorithm. Where offline means the graph is known in advance and the online means that
the edges of the graph are arrive one by one as an input, and We need to color each edge as soon as it is
added to the graph and the main issue is that we want to minimize the number of colors. We cannot change
the color of an edge after colored in an online algorithm. In this paper, we improve the online algorithm
for edge coloring. There is also a theorem which proves that if the maximum degree of a graph is Δ, then it
is possible to color its edges, in polynomial time, using at most Δ+ 1 color. The algorithm provided by
Vizing is offline, i.e., it assumes the whole graph is known in advance. In online algorithm edges arrive one
by one in a random permutation. This online algorithm is inspired by a distributed offline algorithm of
Panconesi and Srinivasan, referred as PS algorithm, works on 2-rounds which we extend by reusing colors
online in multiple rounds.
Security & privacy issues of cloud & grid computing networksijcsa
Cloud computing is a new field in Internet computing that provides novel perspectives in internetworking
technologies. Cloud computing has become a significant technology in field of information technology.
Security of confidential data is a very important area of concern as it can make way for very big problems
if unauthorized users get access to it. Cloud computing should have proper techniques where data is
segregated properly for data security and confidentiality. This paper strives to compare and contrast cloud
computing with grid computing, along with the Tools and simulation environment & Tips to store data and
files safely in Cloud.
Augmented split –protocol; an ultimate d do s defenderijcsa
Distributed Denials of Service (DDoS) attacks have become the daunting problem for businesses, state
administrator and computer system users. Prevention and detection of a DDoS attack is a major research
topic for researchers throughout the world. As new remedies are developed to prevent or mitigate DDoS
attacks, invaders are continually evolving new methods to circumvent these new procedures. In this paper,
we describe various DDoS attack mechanisms, categories, scope of DDoS attacks and their existing
countermeasures. In response, we propose to introduce DDoS resistant Augmented Split-protocol (ASp).
The migratory nature and role changeover ability of servers in Split-protocol architecture will avoid
bottleneck at the server side. It also offers the unique ability to avoid server saturation and compromise
from DDoS attacks. The goal of this paper is to present the concept and performance of (ASp) as a
defensive tool against DDoS attacks.
STRICTLY CONFIDENTIAL - Carmine Di Blasio- SupervisorCarmine Di Blasio
Carmine Di Blasio is seeking a supervisory role where he can utilize his skills obtained over his career in construction and hospitality. He has over 20 years of experience in roles such as operator, supervisor, and general manager. His skills include strong communication, problem solving, relationship building, and the ability to manage teams and meet deadlines. He has managed projects ranging from $12.5M to $770M and overseen teams of 20-100 people.
This document provides a survey of ant colony optimization (ACO) approaches for routing and load balancing in computer networks. It begins with an overview of ACO, explaining how artificial ants emulate real ants to solve optimization problems. The document then compares ACO approaches to traditional routing algorithms in terms of routing information, overhead, and adaptivity. A major issue for ACO called stagnation is discussed, where the algorithm becomes stuck on suboptimal solutions. The document surveys approaches to mitigate stagnation, including pheromone evaporation, aging, and privileged pheromone laying. It also reviews three major areas of ACO research for routing/load balancing and discusses new directions and open problems.
The document provides observations from shadowing a pit crew instructor at an airport. It discusses key lessons learned around non-technical skills like crew resource management. Specifically, it highlights the importance of flexibility given changing flight schedules, constant communication between team members to coordinate workload, and thinking ahead to manage fatigue and prioritize tasks. It also emphasizes stress management, rotating roles to share workloads, and maintaining morale particularly during challenging situations like flight delays.
1. The document outlines SKJ Engineering & Grading's Heat Illness Prevention Plan, including procedures for water, shade, weather monitoring, high heat procedures, acclimatization, emergency response, handling sick employees, and employee training.
2. Key personnel responsible for implementing the plan are identified. Procedures describe how water and shade will be provided to workers, how weather will be monitored, and measures that will be taken during high heat or a heat wave.
3. The plan also includes procedures for acclimatizing new employees, responding to emergencies and heat illness, and training supervisors and employees on the company's prevention measures.
This document contains a resume for Jason S. Gagen. It summarizes his career objective of obtaining a transportation construction supervisor or manager position. It then lists his certifications and work experience in transportation construction and inspection roles over nearly 20 years, primarily with Hill International and Dick Corporation. It provides details on his duties and supervisors in these roles. Earlier in his career, Gagen worked as an EEG technician before transitioning into the transportation field. The resume concludes with his education history and special interests.
AN ENHANCED EDGE ADAPTIVE STEGANOGRAPHY APPROACH USING THRESHOLD VALUE FOR RE...ijcsa
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Mining sequential patterns for interval basedijcsa
Sequential pattern mining finds the frequent subsequences or patterns from the given sequences.
TPrefixSpan algorithm finds the relevant frequent patterns from the given sequential patterns formed using
interval based events. In our proposed work, we add multiple constraints like item, length and aggregate to
the interval based TPrefixSpan algorithm. By adding these constraints the efficiency and effectiveness of
the algorithm improves. The proposed constraint based algorithm CTPrefixSpan has been applied to
synthetic medical dataset. The algorithm can be applied for stock market analysis, DNA sequences analysis
etc.
KEYWORDS
Sequential patterns, temporal patterns, Constraints, Interval based events.
Flix/Slim is a dual-personality club divided into two rooms, Flix playing Thai pop and live music and Slim playing hip-hop. It can get overcrowded on weekends. There is seating outside and stylish decor inside. The Flix side has three live bands nightly playing Thai covers along with monthly concerts and surprise appearances. The club is open Wednesdays and weekends, with free admission but ID required.
In this modern era a great deal of metamorphism is observed around us which eventuate due to some
minute modifications and innovations in the area of Science and Technology. This paper deals with the
application of a meta heuristic optimization algorithm namely the Cuckoo Search Algorithm in the design
of an optimized planar antenna array which ensures high gain ,directivity, suppression of side lobes,
increased efficiency and improves other antenna parameters as well[1], [2] and [3].
PORTFOLIO SELECTION BY THE MEANS OF CUCKOO OPTIMIZATION ALGORITHMijcsa
Portfolio selection is one of the most important and vital decisions that a real or legal person, who invests in stock market should make. The main purpose of this paper is the determination of the optimal portfolio with regard to stock returns of companies, which are active in Tehran’s stock market. For achieving this purpose, annual statistics of companies’ stocks since Farvardin 1387 until Esfand 1392 have been used. For analyzing statistics, information of companies’ stocks, the Cuckoo Optimization Algorithm (COA) and Knapsack Problem have been used with the aim of increasing the total return, in order to form a financial portfolio. At last, results merits of choosing the optimal portfolio using the COA rather than Genetic
Algorithm are given.
This document presents a new meta-heuristic optimization algorithm called Cuckoo Search (CS) that is inspired by the brood parasitism of some cuckoo species and the Lévy flight behavior of some birds and insects. The CS algorithm is formulated based on three idealized rules: each cuckoo lays one egg in a randomly selected nest; the best nests with high-quality eggs are carried over to subsequent generations; and a portion of the worst nests are abandoned. New solutions in CS are generated through Lévy flights. The performance of CS is validated on benchmark test functions and compared to genetic algorithms and particle swarm optimization. Results show that CS can find global optima efficiently.
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.
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Chicken Swarm as a Multi Step Algorithm for Global Optimizationinventionjournals
A new modified of Chicken Swarm Optimization (CSO) algorithm called multi step CSO is proposed for global optimization. This modification is reducing the CSO algorithm’s steps by eliminates the parameter roosters, hens and chicks. Multi step CSO more efficient than CSO algorithm to solve optimization problems. Experiments on seven benchmark problems and a speed reducer design were conducted to compare the performance of Multi Step CSO with CSO algorithms and the other algorithms based population such as Cuckoo Search (CS),Particle Swarm Optimization (PSO), Differential Evolution (DE) and Genetic Algorithm (GA). Simulation results show that Multi step CSO algorithm performs better than those algorithms. Multi step CSO algorithm has the advantages of simple, high robustness, fast convergence, fewer control.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
This document discusses using the Cuckoo Optimization Algorithm (COA) to solve a production planning problem. It provides background on COA and how it was applied to optimize a mathematical model of production planning with the goal of minimizing costs. The COA approach found better solutions than Genetic Algorithm and Lingo software in less time. The authors conclude COA is an effective method for solving this type of constrained nonlinear optimization problem.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
This document discusses using the Cuckoo Optimization Algorithm (COA) to solve a production planning problem. It provides background on COA and how it works, modeling the production planning problem with objectives and constraints. The COA is implemented on a 3-product, 5-period example problem. Results show COA finds better solutions faster than Genetic Algorithm and provides answers close to commercial solver Lingo. COA is thus shown to be an effective method for solving this type of constrained nonlinear production planning problem.
Engineering Optimisation by Cuckoo SearchXin-She Yang
This document summarizes a research paper that proposes a new metaheuristic optimization algorithm called Cuckoo Search (CS). CS is inspired by the breeding behavior of some cuckoo species. The paper describes the rules and steps of the CS algorithm, compares its performance to other algorithms on standard test functions and engineering design problems, and discusses unique features of CS like Lévy flights that make it promising for further research.
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.
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 summarizes research using evolutionary computing techniques like particle swarm optimization, ant colony optimization, and bee colony optimization to improve software effort estimation compared to the COCOMO model. The techniques are applied to tune the parameters of the COCOMO model. The models are validated using a dataset of 48 interactive voice response projects, and the bee colony optimization technique produces the lowest mean magnitude of relative error (MMRE) of 0.11 and root mean squared error (RMSE) of 7.85, outperforming the other techniques and COCOMO model. Evolutionary computing techniques are found to provide more accurate effort estimates than existing estimation models.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution
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.
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.
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.
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Evaluation the efficiency of cuckoo
1. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.2, April 2014
DOI:10.5121/ijcsa.2014.4205 39
Evaluation The Efficiency Of Cuckoo
Optimization Algorithm
1
Elham Shadkam and 2
Mehdi Bijari
1, 2
Department of Industrial and Systems Engineering, Isfahan University of Technology,
Isfahan, Iran
ABSTRACT
In this paper a new evolutionary algorithm, for continuous nonlinear optimization problems, is surveyed.
This method is inspired by the life of a bird, called Cuckoo.
The Cuckoo Optimization Algorithm (COA) is evaluated by using the Rastrigin function. The problem is a
non-linear continuous function which is used for evaluating optimization algorithms. The efficiency of the
COA has been studied by obtaining optimal solution of various dimensions Rastrigin function in this paper.
The mentioned function also was solved by FA and ABC algorithms. Comparing the results shows the COA
has better performance than other algorithms.
Application of algorithm to test function has proven its capability to deal with difficult optimization
problems.
KEYWORDS
Meta-Heuristic Algorithm, Cuckoo Optimization Algorithm (COA), Continuous Optimization.
1. INTRODUCTION
Many problems are continuous in the real world and finding the global solutions is difficult.
Although the development in computer technologies is increasing the speed of computations, this
often is not adequate, particularly if the size of the problem's instance is large. Applying exact
algorithm on such problems necessitate their linearization. Heuristic methods have been tackling
the problems within reasonable computational time. Heuristic methods give an approximate
solution.
The late studies of the researchers have led to development of the algorithms which have been
based on the natural phenomenon. Several Meta-Heuristic algorithms have been developed during
recent decades. For solving the combined optimization problems, The Meta-Heuristic methods
have been efficient in finding the solution [1, 2, 3, 4].
Many Meta-Heuristic algorithms have been presented basing the nature of which the Particle
Swarm Optimization (PSO) [5], Artificial Bee Colony (ABC) [6], Firefly Algorithm (FA) [7],
Bee Colony Optimization (BCO) [8] and Ants Colony Optimization (ACO) [9] could be
mentioned. The ABC algorithm [6, 10] is based on the mining manner of the bee colony for
solving the continuous optimization problems. The FA [7] is one of the Meta-Heuristic
algorithms inspired by the swarm intelligence for continuous optimization problems.
Some presented methods apply clustering decision space idea. Like the novel approach which
used for scheduling problems [11]. To solve this problem the proposed heuristic based on finding
2. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.2, April 2014
40
approximate jobs’ positions in a sequence and then applying the induced knowledge to find more
precise jobs’ situation.
In this paper, we evaluate the ABC algorithm and FA to show the efficiency of COA algorithm
and solve some continuous optimization functions and answers will be compared.
Cuckoo Optimization Algorithm is based on the life of a bird called cuckoo. The basis of this
novel optimization algorithm is Specific breeding and egg laying of this bird. Adult cuckoos and
eggs used in this modeling. Adult cuckoos lay eggs in other birds’ habitat. Those eggs grow and
become a mature cuckoo if are not fiends and not removed by host birds. The immigration of
groups of cuckoos and environmental specifications hopefully lead them to converge and reach
the best place for reproduction and breeding. The global maximum of objective functions is in
this best palace.
The rest of the paper is organized as follows: in part 2, we've presented COA algorithm; in part 3,
the evaluation of COA, ABC and FA for resolving the continuous optimization problems has
been studied and we've considered the results. In part 4, conclusions are presented.
2. CUCKOO'S LIFESTYLE
Cuckoo optimization was developed by Yang and Deb in 2009 that inspired from the nature [12].
Cuckoo Optimization Algorithm was developed by Rajabioun in 2011 [13]. Cuckoo Optimization
Algorithm (COA) is really a new continuous over all aware search based on the life of a bird
called cuckoo. Similar other meta heuristic, COA begins with an primary population, a group of
cuckoos. These cuckoos lay some eggs in the habitat of other host birds. A random group of
potential solutions is generated that represent the habitat in COA. In the fitness function will be
evaluated the parameters of the candidate components. The steps to search the optimum solution
are given as follows. First, the algorithm begins with a primary population of birds and they have
some eggs to lie in some host bird’s habitats. Some of these eggs grow up and become adult birds
which are more like to the host bird’s eggs and other eggs are discovered by host birds and are
removed. The more profit is gained in that area, the more eggs that remain and hatch in the place.
So the place in which more eggs remain will be the term that COA is going to optimize. Each
cuckoo has a “distance” to the best habitat.
It is essential that the values of the problem be changed as an array, called “habitat” for solving a
problem with COA. A habitat is an array of 1 ⃰ Nvar, indicating the current living place of the
cuckoo in the instance of Nvar dimensional case,. It is defined as follows:
habitat=[x1 ,x2 ,...,xNvar ] (1)
The suitability of a habitat is yielded by assessment of profit function fb at a habitat of
(x1,x2,…,xNvar), where:
profit=f b(habitat)=f b(x1 ,x2 ,...,xNvar) (2)
To begin the COA, a primary habitat matrix of size Npop ⃰ Nvar is introduced. For each of these
primary cuckoo habitats, some randomly produced number of eggs is supposed. the cuckoos lay
eggs within a maximum interval from their position, this domain is called Egg Laying Radius.
Egg Laying Radius (ELR) is given as follows:
= × (var − var ) (3)
3. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.2, April 2014
41
α handles the maximum value of ELR and is an integer value. varhi and varlow are the upper and
the lower bound for variables respectively. After each egg laying, P% of all the eggs (usually
10%) that less similar to the eggs of the host bird are discovered and thrown out of the nest.
Therefore, the eggs that profit function value is smaller remove.
Remained young cuckoos are grown in the host nests. The adult cuckoos live for a while in their
groups and habitats when they grow up. Then they migrate to better places where the eggs have
higher survival chances. Cuckoo groups are shaped in all different place of the environment. For
migration, the group with the best situation is selected as the goal point for all other cuckoos. it is
a problem to find out to which group each cuckoo belongs.
Grouping of birds is done by K-means clustering method to solve this problem. Group’s relative
optimization of habitat is calculated based on the average objective function of the group. Other
groups migrate to the place with the greatest average profit. The cuckoos do not migrate the entire
path in travel to the goal point; they just pass part of the distance. This subject is clearly shown in
Figure1:
Fig 1. Immigration of a sample cuckoo toward goal habitat [13]
Each bird just traverse λ% of the whole path to the goal place and has a deviation of φ radians. λ
and φ help the birds search a larger area. φ is a random number between π/6 and –π/6 and λ is a
number between 0 and 1. Each cuckoo lays some eggs, once all the cuckoos have traveled to the
goal place and the entire new place are specified.
An ELR is determined for each cuckoo based on the number of eggs. The maximum number of
the bird can live in a place is finite (Nmax). After some iteration, all of the cuckoos achieve an
optimized place with the maximum likeness of the eggs to the eggs of the host bird. This position
the number of the eggs which removed in it will be minimal and will have the greatest objective
function. Takes algorithm to its end if convergence of more than 95% of all the cuckoos towards
a single point. Relationship (2), migration function in COA, is:
= + × ( − ) (4)
The cuckoo optimization algorithm (COA) is summarized as follows [14] and Fig. 2 shows a
flowchart of the proposed algorithm.
1. Preparation cuckoo habitats with some arbitrary solution on the objective function;
2. Assignment some eggs to each cuckoo;
3. Determination ELR for each cuckoo;
4. Allow cuckoo to lay eggs inside their equivalent ELR;
4. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.2, April 2014
42
5. Removed those eggs that are discovered by host birds;
6. Allow eggs hatch and chicks grow up;
7. Assess the habitat of each grown cuckoo;
8. Remove cuckoo live in worst habitats and confine maximum number of cuckoo in
environment;
9. Cluster cuckoos and detect best place and select goal point;
10. Allow new cuckoo population travel toward goal point;
11. If stop condition is satisfied, then stop. If not, go to 2.
Fig 2. Flowchart of cuckoo optimization algorithm [13]
3. EVALUATION AND RESULTS
For evaluation and finding out efficiency of the COA, the Rastrigin function is studied [15]. This
function has several maximum and minimum points which have caused it to be utilize as a test
function for assessment of the Meta-Heuristic Algorithms. So, the Rastrigin function is used for
comparison of the evaluation and utility of COA algorithm.
= 10 + ∑ ( − 10 cos(2 )), (0,0, … ,0) = 0, − 5 ≤ ≤ 5 (5)
Rastrigin function is one of the difficult test functions and has plenty of local minimal, even in 3-
dimensional case. Fig. 3 shows the 3-dimensional Rastrigin function. The Rastrigin function is a
really challenging optimization problem, as it is appear even in 3-dimensional case.
5. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.2, April 2014
43
Fig 3. 3D plot of Rastrigin function
The Meta-Heuristic algorithms are very sensitive for their parameters and the setting of the
parameters can affect their efficiency. The parameters settings cause more reliability and
flexibility of the algorithm. So, settings of the parameters are one of the crucial factors in gaining
the optimized solution in all optimization problems. Table 1 shows the selected parameters for
COA algorithm.
Table 1. Parameters settings for COA algorithm
Max number of eggs Min number of eggs
Number of
initial
population
Higher
limitation of
variable
Lower
limitation of
variable
4 2 5 30 -30
Population variance
that cuts the
optimization
Control parameter of
egg laying (Radius
Coeff)
Max Number of
cuckoos
Lambda variable
(Motion Coeff)
Number of
clusters
1e-13 5 10 9 2
The results of Table 2 demonstrate that using the COA, ABC and FA algorithm makes getting the
optimized solution possible. The number of initial population is set 50 For ABC and FA
algorithms and the number of iterations is set 100 for both algorithms. As can be seen, the COA
algorithm reaches the optimum value. The results of the ABC and FA algorithms are derived
from [16].
Table 2. Comparison of the Results
COAFAABCRange of search SpaceDimensional of Function
00.12870.0059±302
00.75160.0136±303
To show the efficiency of COA, the convergence diagram is used. As shown in figure 4, the
function of the algorithms in convergence toward the optimal solution in appropriate number of
repetitions.
6. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.2, April 2014
44
ABC for Solving the Two Dimensional Rastrigin [12] ABC for Solving the Three Dimensional Rastrigin[12]
FA for Solving the Two Dimensional Rastrigin [16] FA for Solving the Three Dimensional Rastrigin [16]
Fig 4. The Convergence Diagram of COA, ABC and FA for Solving the Two and Three Dimensional
Rastrigin Function
Table 3 shows the number of the repetition of the execution of COA, ABC and FA for two
dimensional Rastrigin function. The results of table 3 and fig 5, show the fact that COA algorithm
is more efficient in finding the global optimized points.
7. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.2, April 2014
45
Table 3. The result of comparison for two dimensional Rastrigin function
Iteration
Algorithm
10080604020
0.00590.01481.00561.16101.0372ABC
0.12871.02281.13121.36041.5273FA
00004.26E-14COA
Fig 5. The Comparison Diagram for two dimensional Rastrigin function
Table 4 shows the repetition number of execution of COA, ABC and FA for three dimensional
Rastrigin function. As it is seen, COA algorithm is more able in finding the global optimized
points and is more efficient in solving the continuous optimization functions of large dimensions.
Table 3. The result of comparison for three dimensional Rastrigin function
Iteration
Algorithm
10080604020
0.01360.08261.61651.16881.6723ABC
0.75161.17431.62282.12982.2512FA
00007.01E-12COA
Fig 6. The Comparison between tree algorithms for three dimensional Rastrigin function
COA algorithm is able to maintain the equilibrium of the local and global search of the problem
in spite of the increase in the dimensions of it in an optimized way, as it is seen from table 4.
COA algorithm reaches the optimal solution (zero value) for different dimension in Finite number
of iterations.
0
0.5
1
1.5
2
0 20 40 60 80 100 120
BestSolution
Number of Iteration
ABC FA COA
0
1
2
3
0 50 100 150
BestSolution
Number of Iteration
ABC FA COA
8. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.2, April 2014
46
Table 4. Number of Iteration to reach the optimal solution
1000010001005010532Dimensional of Function
3938363532302825Number of Iteration
Fig 7. Number of Iteration to reach the optimal solution
Table 5 shows variation and mean of solutions obtained from the COA algorithm. Results
indicate the robustness of COA algorithm is compared to the dimension of the problem (number
of iteration=20).
Table 5. variation and mean of solutions obtained from the COA algorithm
1000010001005010532
Dimensio
n of the
problem
0.06668
0.0140
5
5.66E-
05
3.97E-
05
4.38E-
06
3.81027
E-05
1.5485E
-05
3.70E-
07
Mean of
solution
0.00032
65
0.0002
83
4.82102
E-09
5.12347
E-09
5.63182
E-11
2.49416
E-08
7.40086
E-10
8.0542
4E-07
Variation
of solution
4. CONCLUSION
In this paper, a novel optimization algorithm was verified which was based on lifestyle of a bird
called Cuckoo. Egg laying and breeding are Special characteristics of cuckoos and had been the
basic motivation for development of this algorithm. The COA algorithm was evaluated on
benchmark cost functions. We have studied the COA, ABC and FA for solving the continuous
optimization problems of big area and the answers near to the optimized one. To evaluate the
efficiency of the algorithm, two types of comparison from answer reliability and accuracy in
convergence points have been studied. The comparison of COA with ABC and FA, showed the
superiority of COA in fast convergence and global optima achievement. In this test, other
methods have not found the global minimal but COA found it and COA has converged faster in
less iterations. In the test function (low dimensional Rastrigin function) methods have found the near
global minima but COA has converged faster in less iterations. But in test function with high
dimensional, ABC and FA could not converge to even a near value of global optimal. But COA had
found global minimum in just 35 iterations. COA algorithm is stronger from answering point and
more appropriate in solving such problems and is also when it is convergent; it is faster than other
algorithm.
30
32
34
36
38
40
0 500 1000 1500
NumberofIteration
Number of Variables
9. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.2, April 2014
47
REFERENCES
[1] F.S. Gharehchopogh, I. Maleki, S.R. Khaze, “A New Optimization Method for Dynamic Travelling
Salesman Problem with Hybrid Ant Colony Optimization Algorithm and Particle Swarm
Optimization”, International Journal of Advanced Research in Computer Engineering & Technology
(IJARCET), Vol. 2, Issue 2, pp. 352-358, February 2013.
[2] F.S. Gharehchopogh, I.Maleki, S.R.Khaze, “A New Approach in Dynamic Traveling Salesman
Solution: A Hybrid of Ant Colony Optimization and Descending Gradients”, International Journal of
Managing Public Sector Information and Communication Technologies (IJMPICT), Vol. 3, No. 2, pp.
1-9, December 2012.
[3] I. Maleki, S.R. Khaze, F.S. Gharehchopogh, “A New Solution for Dynamic Travelling Salesman
Problem with Hybrid Ant Colony Optimization Algorithm and Chaos Theory”, International Journal
of Advanced Research in Computer Science (IJARCS), Vol. 3, No. 7, pp. 39-44, Nov-Dec 2012.
[4] F.S. Gharehchopogh, I. Maleki, M. Farahmandian, "New Approach for Solving Dynamic Traveling
Salesman Problem with Hybrid Genetic Algorithms and Ant Colony Optimization", International
Journal of Computer Applications (IJCA), Vol. 53, No.1, pp. 39-44, September 2012.
[5] J. Kennedy, R.C. Eberhart, "Particle Swarm Optimization", In Proceedings of the IEEE International
Conference on Neural Networks, pp. 1942-1948, 1995.
[6] D. Karaboga, “An idea based on honeybee swarm for numerical optimization”, Technical Report
TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
[7] X.S. Yang, “Nature-Inspired Meta-heuristic Algorithms”, Luniver Press, 2008.
[8] P. Lucic, D. Teodorovic, “Bee system: Modeling Combinatorial Optimization Transportation
Engineering Problems by Swarm Intelligence”, Preprints of the TRISTAN IV Triennial Symposium
on Transportation Analysis, Sao Miguel, Azores Islands, pp. 441-445, 2001.
[9] M. Dorigo, V. Maniezzo, A. Colorni, “Ant system: optimization by a colony of cooperating agents”,
IEEE Trans. on Systems, Man and Cybernetics, Part B, Vol.26, No.1, pp.29-41, 1996.
[10] P. Shunmugapriya , S. Kanmani, “Artificial Bee Colony Approach for Optimizing Feature Selection”,
International Journal of Computer Science Issues, Vol. 9, Issue 3, No. 3, pp. 432-438, May 2012.
[11] S. Poursheikh Ali, M. Bijari, " Minimizing Maximum Earliness and Tardiness on a Single Machine
using a Novel Heuristic Approach", Proceedings of the International Conference on Industrial
Engineering and Operations Management, Istanbul, Turkey, July 3 – 6, 2012.
[12] X. S. Yang and S. Deb, "Cuckoo search via Lévy Flights," In: World Congress on Nature &
Biologically Inspired Computing (NaBIC2009). IEEE Publications, pp. 210–214, 2009.
[13] R. Rajabioun, "Cuckoo Optimization Algorithm," In: Applied Soft Computing journal, vol. 11, pp.
5508 - 5518, 2011.
[14] M. Arezki Mellal, s. Adjerid, E. Williams and D. Benazzouz, ''Optimal replacement policy for bsolete
components using cuckoo optimization algorithm based-approach: Dependability context,'' Journal of
Scientific & Industrial research, Vol. 71, pp. 715-721, 2012.
[15] M. Molga, C. Smutnicki, “Test functions for optimization needs”, 2005.
[16] R. Khaze, I. Maleki, S. Hojjatkhah and A. Bagherinia, ''evaluation the efficiency of artificial bee
colony and the firefly algorithm in solving the continues optimization problem'', International Journal
on Computational Sciences & Applications (IJCSA), Vol.3, No.4, pp.23-35, 2013.
Authors
Elham shadkam is a PhD student in the Department of Industrial Engineering at
Isfahan University of Technology in Isfahan, Iran. Her main research interests are
modeling and simulation, Optimization, Multi objective decision making.
Mehdi Bijari received his BSc in Industrial Engineering 1987, Msc in system
planning 1990, both from Isfahan University of Technology (IUT), and PhD in
Industrial Engineering 2002, Sharif University of Technology. He has lectured in the
Industrial Engineering Department at IUT from 1991. He is Associate professor in
Industrial Engineering. His interested research areas are in the Stochastic production
planning, Meta Heuristics Methods, Optimization and Artificial Intelligence. He has
published more than 50 papers in journals and conferences.
International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.2, April 2014
47
REFERENCES
[1] F.S. Gharehchopogh, I. Maleki, S.R. Khaze, “A New Optimization Method for Dynamic Travelling
Salesman Problem with Hybrid Ant Colony Optimization Algorithm and Particle Swarm
Optimization”, International Journal of Advanced Research in Computer Engineering & Technology
(IJARCET), Vol. 2, Issue 2, pp. 352-358, February 2013.
[2] F.S. Gharehchopogh, I.Maleki, S.R.Khaze, “A New Approach in Dynamic Traveling Salesman
Solution: A Hybrid of Ant Colony Optimization and Descending Gradients”, International Journal of
Managing Public Sector Information and Communication Technologies (IJMPICT), Vol. 3, No. 2, pp.
1-9, December 2012.
[3] I. Maleki, S.R. Khaze, F.S. Gharehchopogh, “A New Solution for Dynamic Travelling Salesman
Problem with Hybrid Ant Colony Optimization Algorithm and Chaos Theory”, International Journal
of Advanced Research in Computer Science (IJARCS), Vol. 3, No. 7, pp. 39-44, Nov-Dec 2012.
[4] F.S. Gharehchopogh, I. Maleki, M. Farahmandian, "New Approach for Solving Dynamic Traveling
Salesman Problem with Hybrid Genetic Algorithms and Ant Colony Optimization", International
Journal of Computer Applications (IJCA), Vol. 53, No.1, pp. 39-44, September 2012.
[5] J. Kennedy, R.C. Eberhart, "Particle Swarm Optimization", In Proceedings of the IEEE International
Conference on Neural Networks, pp. 1942-1948, 1995.
[6] D. Karaboga, “An idea based on honeybee swarm for numerical optimization”, Technical Report
TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
[7] X.S. Yang, “Nature-Inspired Meta-heuristic Algorithms”, Luniver Press, 2008.
[8] P. Lucic, D. Teodorovic, “Bee system: Modeling Combinatorial Optimization Transportation
Engineering Problems by Swarm Intelligence”, Preprints of the TRISTAN IV Triennial Symposium
on Transportation Analysis, Sao Miguel, Azores Islands, pp. 441-445, 2001.
[9] M. Dorigo, V. Maniezzo, A. Colorni, “Ant system: optimization by a colony of cooperating agents”,
IEEE Trans. on Systems, Man and Cybernetics, Part B, Vol.26, No.1, pp.29-41, 1996.
[10] P. Shunmugapriya , S. Kanmani, “Artificial Bee Colony Approach for Optimizing Feature Selection”,
International Journal of Computer Science Issues, Vol. 9, Issue 3, No. 3, pp. 432-438, May 2012.
[11] S. Poursheikh Ali, M. Bijari, " Minimizing Maximum Earliness and Tardiness on a Single Machine
using a Novel Heuristic Approach", Proceedings of the International Conference on Industrial
Engineering and Operations Management, Istanbul, Turkey, July 3 – 6, 2012.
[12] X. S. Yang and S. Deb, "Cuckoo search via Lévy Flights," In: World Congress on Nature &
Biologically Inspired Computing (NaBIC2009). IEEE Publications, pp. 210–214, 2009.
[13] R. Rajabioun, "Cuckoo Optimization Algorithm," In: Applied Soft Computing journal, vol. 11, pp.
5508 - 5518, 2011.
[14] M. Arezki Mellal, s. Adjerid, E. Williams and D. Benazzouz, ''Optimal replacement policy for bsolete
components using cuckoo optimization algorithm based-approach: Dependability context,'' Journal of
Scientific & Industrial research, Vol. 71, pp. 715-721, 2012.
[15] M. Molga, C. Smutnicki, “Test functions for optimization needs”, 2005.
[16] R. Khaze, I. Maleki, S. Hojjatkhah and A. Bagherinia, ''evaluation the efficiency of artificial bee
colony and the firefly algorithm in solving the continues optimization problem'', International Journal
on Computational Sciences & Applications (IJCSA), Vol.3, No.4, pp.23-35, 2013.
Authors
Elham shadkam is a PhD student in the Department of Industrial Engineering at
Isfahan University of Technology in Isfahan, Iran. Her main research interests are
modeling and simulation, Optimization, Multi objective decision making.
Mehdi Bijari received his BSc in Industrial Engineering 1987, Msc in system
planning 1990, both from Isfahan University of Technology (IUT), and PhD in
Industrial Engineering 2002, Sharif University of Technology. He has lectured in the
Industrial Engineering Department at IUT from 1991. He is Associate professor in
Industrial Engineering. His interested research areas are in the Stochastic production
planning, Meta Heuristics Methods, Optimization and Artificial Intelligence. He has
published more than 50 papers in journals and conferences.
International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.2, April 2014
47
REFERENCES
[1] F.S. Gharehchopogh, I. Maleki, S.R. Khaze, “A New Optimization Method for Dynamic Travelling
Salesman Problem with Hybrid Ant Colony Optimization Algorithm and Particle Swarm
Optimization”, International Journal of Advanced Research in Computer Engineering & Technology
(IJARCET), Vol. 2, Issue 2, pp. 352-358, February 2013.
[2] F.S. Gharehchopogh, I.Maleki, S.R.Khaze, “A New Approach in Dynamic Traveling Salesman
Solution: A Hybrid of Ant Colony Optimization and Descending Gradients”, International Journal of
Managing Public Sector Information and Communication Technologies (IJMPICT), Vol. 3, No. 2, pp.
1-9, December 2012.
[3] I. Maleki, S.R. Khaze, F.S. Gharehchopogh, “A New Solution for Dynamic Travelling Salesman
Problem with Hybrid Ant Colony Optimization Algorithm and Chaos Theory”, International Journal
of Advanced Research in Computer Science (IJARCS), Vol. 3, No. 7, pp. 39-44, Nov-Dec 2012.
[4] F.S. Gharehchopogh, I. Maleki, M. Farahmandian, "New Approach for Solving Dynamic Traveling
Salesman Problem with Hybrid Genetic Algorithms and Ant Colony Optimization", International
Journal of Computer Applications (IJCA), Vol. 53, No.1, pp. 39-44, September 2012.
[5] J. Kennedy, R.C. Eberhart, "Particle Swarm Optimization", In Proceedings of the IEEE International
Conference on Neural Networks, pp. 1942-1948, 1995.
[6] D. Karaboga, “An idea based on honeybee swarm for numerical optimization”, Technical Report
TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
[7] X.S. Yang, “Nature-Inspired Meta-heuristic Algorithms”, Luniver Press, 2008.
[8] P. Lucic, D. Teodorovic, “Bee system: Modeling Combinatorial Optimization Transportation
Engineering Problems by Swarm Intelligence”, Preprints of the TRISTAN IV Triennial Symposium
on Transportation Analysis, Sao Miguel, Azores Islands, pp. 441-445, 2001.
[9] M. Dorigo, V. Maniezzo, A. Colorni, “Ant system: optimization by a colony of cooperating agents”,
IEEE Trans. on Systems, Man and Cybernetics, Part B, Vol.26, No.1, pp.29-41, 1996.
[10] P. Shunmugapriya , S. Kanmani, “Artificial Bee Colony Approach for Optimizing Feature Selection”,
International Journal of Computer Science Issues, Vol. 9, Issue 3, No. 3, pp. 432-438, May 2012.
[11] S. Poursheikh Ali, M. Bijari, " Minimizing Maximum Earliness and Tardiness on a Single Machine
using a Novel Heuristic Approach", Proceedings of the International Conference on Industrial
Engineering and Operations Management, Istanbul, Turkey, July 3 – 6, 2012.
[12] X. S. Yang and S. Deb, "Cuckoo search via Lévy Flights," In: World Congress on Nature &
Biologically Inspired Computing (NaBIC2009). IEEE Publications, pp. 210–214, 2009.
[13] R. Rajabioun, "Cuckoo Optimization Algorithm," In: Applied Soft Computing journal, vol. 11, pp.
5508 - 5518, 2011.
[14] M. Arezki Mellal, s. Adjerid, E. Williams and D. Benazzouz, ''Optimal replacement policy for bsolete
components using cuckoo optimization algorithm based-approach: Dependability context,'' Journal of
Scientific & Industrial research, Vol. 71, pp. 715-721, 2012.
[15] M. Molga, C. Smutnicki, “Test functions for optimization needs”, 2005.
[16] R. Khaze, I. Maleki, S. Hojjatkhah and A. Bagherinia, ''evaluation the efficiency of artificial bee
colony and the firefly algorithm in solving the continues optimization problem'', International Journal
on Computational Sciences & Applications (IJCSA), Vol.3, No.4, pp.23-35, 2013.
Authors
Elham shadkam is a PhD student in the Department of Industrial Engineering at
Isfahan University of Technology in Isfahan, Iran. Her main research interests are
modeling and simulation, Optimization, Multi objective decision making.
Mehdi Bijari received his BSc in Industrial Engineering 1987, Msc in system
planning 1990, both from Isfahan University of Technology (IUT), and PhD in
Industrial Engineering 2002, Sharif University of Technology. He has lectured in the
Industrial Engineering Department at IUT from 1991. He is Associate professor in
Industrial Engineering. His interested research areas are in the Stochastic production
planning, Meta Heuristics Methods, Optimization and Artificial Intelligence. He has
published more than 50 papers in journals and conferences.