OpenMP Dual Population Genetic Algorithm for Solving Constrained Optimization Problems

OpenMP Dual Population Genetic Algorithm for Solving Constrained Optimization Problems

Автор: A. J. Umbarkar, M. S. Joshi, P. D. Sheth

Журнал: International Journal of Information Engineering and Electronic Business(IJIEEB) @ijieeb

Статья в выпуске: 1 vol.7, 2015 года.

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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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Dual Population Genetic Algorithm (DPGA), Open Multiprocessing (OpenMP), Constrained Optimization Problems (COPs), High Performance Computing (HPC), Meta-heuristic Algorithms, Function Optimization

Короткий адрес: https://sciup.org/15013330

IDR: 15013330

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