Dual Population Genetic Algorithm for Solving Constrained Optimization Problems

Umbarkar, A. J.; Joshi, Madhuri S.; Sheth, P. D.

doi:10.5815/ijisa.2015.02.05

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…Umbarkar et al [1] propose a genetic algorithm with the dual population to solve optimization problems. The application of dual population improves the performance of the genetic algorithm for solving these problems.…”

Section: Literature Surveymentioning

confidence: 99%

Applying greedy genetic algorithm on 0/1 multiple knapsack problem

Jain¹,

Prasad²

2018

IJATEE

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Knapsack problem is a well-known optimization problem in computer science. It has many application areas in science and engineering. Knapsack problem can be solved using genetic algorithm. Multiple knapsack problem (MKP) is a special form of knapsack problem in which items are to be placed in more than one knapsack. Many researchers solve MKP problem using different techniques such as ant colony optimization (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). The objective of this paper is to solve MKP problem using GA in an efficient manner. In thispaper MKP is solved using greedy genetic algorithm. The proposed genetic algorithm uses greedy approach in its selection and reproduction operations of GA. The proposed greedy genetic algorithm is implemented on a standard data set and results ensure that the proposed greedy algorithm performs better than the standard genetic algorithm.

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Section: Literature Surveymentioning

confidence: 99%

Applying greedy genetic algorithm on 0/1 multiple knapsack problem

Jain¹,

Prasad²

2018

IJATEE

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“…J. Umbarkar and Others used a new in the area of evolutionary algorithms for solving optimization problems called Dual Population Genetic Algorithm. It solves constraints optimization problems by applying Maximum Constrains Satisfaction method by using DPGA which is a novel technique that tries to satisfy maximum constrains first and then it attempts to optimize objective function.The results are close to optimum value but fails to obtain exact optimum solution [17].In 2016, ChunFeng Wang, and Yong-Hong Zhang used a chaotic system and an opposition-based method for initial population also, they adopted a chaotic search in the best solution of the current iteration to improve the exploitation of onlooker bees. Results demonstrate that proposed algorithm is better than other standard algorithms [18].In 2017, Pintu Pal presented Hybrid and Particle Swarm Optimization (HPSO).…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Artificial Bee Colony Algorithm for Linear Least Squares and Constrained Optimization Problems

Taher¹

2017

IJCA

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This paper proposes An Improved Artificial Bee Colony (Deb's-ABC) algorithm for solving constrained optimization problems and Nonnegative linear least squares problems. The proposed approach introduces different methods Based upon new search mechanism to balance exploration and exploitation abilities, generating initial population by using the orthogonal initialization method for achieving initial population that spread regularly over the feasible solution and to enhance the global convergence. In addition, we relax the Deb's rules by replacing the feasible solutions with the approximate feasible solutions in Deb's rules because some infeasible solutions with better objective function value and small violation may carry more important information than some feasible solutions .This algorithm is tested on several benchmark functions. Experimental results compared with a standard ABC and other algorithms show that the proposed algorithm is efficient and competitive algorithm for solving constrained optimization problems.

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“…Umbarkar et al [19] proposed Dual Population GA for solving Constrained Optimization Problems. It is based on maximum constraints satisfaction applied as a constraints handling technique and a Dual Population GA used as a metaheuristic.…”

Section: Previous Workmentioning

confidence: 99%

A Generic Adaptive Multi-Gene-Set Genetic Algorithm (AMGA)

Maaita¹,

Zraqou²,

Hamad³

et al. 2015

ijacsa

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Abstract-Genetic algorithms have been used extensively in solving complex solution-space search problems. However, certain problems can include multiple sub-problems in which multiple searches through distinct solution-spaces are required before the final solution combining all the sub-solutions is found. This paper presents a generic design of genetic algorithms which can be used for solving complex solution-space search problems that involve multiple sub-solutions. Such problems are very difficult to solve using basic genetic algorithm designs that utilize a single gene-set per chromosome. The suggested algorithm presents a generic solution which utilizes both multi-gene-set chromosomes, and an adaptive gene mutation rate scheme. The results presented from experiments done using an automatic graphical user interface generation case study, show that the suggested algorithm is capable of producing successful solutions where the common single-gene-set design fails.

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Dual Population Genetic Algorithm for Solving Constrained Optimization Problems

Cited by 10 publications

References 16 publications

Applying greedy genetic algorithm on 0/1 multiple knapsack problem

Applying greedy genetic algorithm on 0/1 multiple knapsack problem

An Improved Artificial Bee Colony Algorithm for Linear Least Squares and Constrained Optimization Problems

A Generic Adaptive Multi-Gene-Set Genetic Algorithm (AMGA)

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