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Discrete Dynamics in Nature and Society Volume 2017 ,2017-06-27
Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History
Research Article
Danping Wang 1 , 2 , 3 Kunyuan Hu 1 Lianbo Ma 1 , 2 Maowei He 1 , 2 , 4 Hanning Chen 4
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DOI:10.1155/2017/5193013
Received 2016-11-04, accepted for publication 2017-04-19, Published 2017-04-19
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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.

授权许可

Copyright © 2017 Danping Wang et al. 2017
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

图表

K-means clustering result with k=3.

Convergence curves of MCPSO-PSH, NrGA, CMA-ES, PSO, and DE against test functions  f1–f10 in low dimension, 10D, and high dimension, 30D, respectively.

Box plots of results obtained by all algorithms. Here, 1 to 5 in the horizontal axis are indices of MCPSO-PSH, NrGA, CMA-ES, PSO, and DE, respectively (the box has lines at the lower quartile, median, and upper quartile values. The whiskers extend to the most extreme data points not considered outliers. Outliers (denoted by +) are data with values beyond 100 units of interquartile range).

Test images and their histograms.

通讯作者

1. Maowei He.Department of Information Service & Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China, cas.cn;University of Chinese Academy of Sciences, Beijing 100039, China, ucas.ac.cn;School of Computer Science and Software, Tianjin Polytechnic University, Tianjin 300387, China, tjpu.edu.cn.hemaowei@hotmail.com
2. Hanning Chen.School of Computer Science and Software, Tianjin Polytechnic University, Tianjin 300387, China, tjpu.edu.cn.perfect_chn@hotmail.com

推荐引用方式

Danping Wang,Kunyuan Hu,Lianbo Ma,Maowei He,Hanning Chen. Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History. Discrete Dynamics in Nature and Society ,Vol.2017(2017)

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