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Research on an Improved Coordinating Method Based on Genetic Algorithms and Particle Swarm Optimization

Research on an Improved Coordinating Method Based on Genetic Algorithms and Particle Swarm Optimization

Rongrong Li, Linrun Qiu, Dongbo Zhang
Copyright: © 2019 |Volume: 13 |Issue: 2 |Pages: 12
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781522564577|DOI: 10.4018/IJCINI.2019040102
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MLA

Li, Rongrong, et al. "Research on an Improved Coordinating Method Based on Genetic Algorithms and Particle Swarm Optimization." IJCINI vol.13, no.2 2019: pp.18-29. http://doi.org/10.4018/IJCINI.2019040102

APA

Li, R., Qiu, L., & Zhang, D. (2019). Research on an Improved Coordinating Method Based on Genetic Algorithms and Particle Swarm Optimization. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 13(2), 18-29. http://doi.org/10.4018/IJCINI.2019040102

Chicago

Li, Rongrong, Linrun Qiu, and Dongbo Zhang. "Research on an Improved Coordinating Method Based on Genetic Algorithms and Particle Swarm Optimization," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 13, no.2: 18-29. http://doi.org/10.4018/IJCINI.2019040102

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Abstract

In this article, a hierarchical cooperative algorithm based on the genetic algorithm and the particle swarm optimization is proposed that the paper should utilize the global searching ability of genetic algorithm and the fast convergence speed of particle swarm optimization. The proposed algorithm starts from Individual organizational structure of subgroups and takes full advantage of the merits of the particle swarm optimization algorithm and the genetic algorithm (HCGA-PSO). The algorithm uses a layered structure with two layers. The bottom layer is composed of a series of genetic algorithm by subgroup that contributes to the global searching ability of the algorithm. The upper layer is an elite group consisting of the best individuals of each subgroup and the particle swarm algorithm is used to perform precise local search. The experimental results demonstrate that the HCGA-PSO algorithm has better convergence and stronger continuous search capability, which makes it suitable for solving complex optimization problems.

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