Dual Population Genetic Algorithm (GA) versus OpenMP GA for Multimodal Function Optimization

Umbarkar, A. J.; Joshi, Madhuri S.

doi:10.5120/10744-5516

Cited by 12 publications

(10 citation statements)

References 32 publications

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“…Umbarkar, Joshi and Hong (2014) [6] improves the performance of DPGA by parallelizing it using multithreads. By using this technique they also solve the problem of population diversity and premature convergence.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Study of CEC’2013 Problem Using Dual Population Genetic Algorithm

Umbarkar¹,

Moon²,

Sheth³

2018

IJIEEB

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Evolutionary Algorithms (EAs) are found to be effective for solving a large variety of optimization problems. In this Paper Dual Population Genetic Algorithm (DPGA) is used for solving the test functions of Congress on Evolutionary Computation 2013 (CEC'2013), by using two different crossovers. Dual Population Genetic Algorithm is found to be better in performance than traditional Genetic Algorithm. It is also able to solve the problem of premature convergence and diversity of the population in genetic algorithm. This paper proposes Dual Population Genetic Algorithm for solving the problem regarding unconstrained optimization. Dual Population Genetic Algorithm is used as metaheuristic which is verified against 28 functions from Problem Definitions and Evaluation Criteria for the Congress on Evolutionary Computation 2013 on unconstrained set of benchmark functions using two different crossovers. The results of both the crossovers are compared with each other. The results of both the crossovers are also compared with the existing results of Standard Particle Swarm Optimization algorithm. The Experimental results showed that the algorithm found to be better for finding the solution of multimodal functions of the problem set.

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Section: Literature Reviewmentioning

confidence: 99%

Comparative Study of CEC’2013 Problem Using Dual Population Genetic Algorithm

Umbarkar¹,

Moon²,

Sheth³

2018

IJIEEB

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“…Park and Ruy (2010) [1] experimented DPGA on various classes of problems using binary, real-valued, and order-based representations. Umbarkar and Joshi (2013) [8] compared DPGA with OpenMP GA for Multimodal Function Optimization. The results show that the performance of OpenMP GA better than SGA on the basis of execution time and speed up.…”

Section: Literature Review Dual Population Genetic Algorithm For mentioning

confidence: 99%

OpenMP Dual Population Genetic Algorithm for Solving Constrained Optimization Problems

Umbarkar¹,

Joshi²,

Sheth³

2015

IJIEEB

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Dual Population Genetic Algorithm is an effective optimization algorithm that provides additional diversity to the main population. It deals with the premature convergence problem as well as the diversity problem associated with Genetic Algorithm. But dual population introduces additional search space that increases time required to find an optimal solution. This large scale search space problem can be easily solved using all available cores of current age multi-core processors. Experiments are conducted on the problem set of CEC 2006 constrained optimization problems. Results of Sequential DPGA and OpenMP DPGA are compared on the basis of accuracy and run time. OpenMP DPGA gives speed up in execution.

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“…Umbarkar and Joshi in [89] compared the performance of DPGA with simple GA and OpenMP GA for multimodal function optimization. DPGA shows the performance over simple GA and programming parallel OpenMP GA.…”

Section: Pgas and Diversitymentioning

confidence: 99%

Untitled

2013

IJSC

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Genetic Algorithm (GA), a stochastic optimization technique, doesn't ensure optimal solution every time. Nowadays there is a need to improve the performance of each and every application so that the time required for obtaining quality solution can be minimized. This paper gives a brief overview of theoretical advances and computing trends, particularly population diversity in PGA (Parallel GA) and provides information about how various authors, researchers, scientists have parallelized GA over various parallel computing paradigms viz. Cluster, MPP (Massively Parallel Processing), GPGPU (General purpose Graphics Processing Units), Grid, Cloud, Multicore/HPC to ensure more optimal solution every time with efficacy and efficiency.

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Dual Population Genetic Algorithm (GA) versus OpenMP GA for Multimodal Function Optimization

Cited by 12 publications

References 32 publications

Comparative Study of CEC’2013 Problem Using Dual Population Genetic Algorithm

Comparative Study of CEC’2013 Problem Using Dual Population Genetic Algorithm

OpenMP Dual Population Genetic Algorithm for Solving Constrained Optimization Problems

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