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| 论文题目： |
Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History |
| 第一作者： |
Wang DP(王丹萍);Hu KY(胡琨元);Ma LB(马连博);He MW(何茂伟);Chen HN(陈瀚宁) |
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| 发表刊物： |
DISCRETE DYNAMICS IN NATURE AND SOCIETY |
| 发表年度： |
2017 |
| 卷,期,页： |
2017,,1-22 |
| 论文出处： |
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| 论文摘要： |
A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on k-means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH) is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals' revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid premature convergence to local optimum. To demonstrate the power of the method, comparisons between the proposed algorithm and state-of-the-art algorithms are grouped into two categories: 10 basic benchmark functions (10-dimensional and 30-dimensional), 10 CEC2005 benchmark functions (30-dimensional), and a real-world problem (multilevel image segmentation problems). Experimental results show that MCPSO-PSH displays a competitive performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests. |
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