Cluster-structured Particle Swarm Optimization with interaction
Cluster-structured Particle Swarm Optimization with interaction
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发表时间：
2009-11
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通讯作者：
K. Yazawa;M. Motoki;K. Yasuda
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作者：
K. Yazawa;M. Motoki;K. Yasuda
提出了一种新的具有参数相互作用和多样性的簇结构粒子群优化算法。将一群粒子群划分为若干子群（簇），增加子群之间的相互作用，增加粒子群参数的多样性，从而提高粒子群的搜索能力。通过数值模拟分析了该粒子群的簇结构和相互作用。通过四个典型优化测试问题的数值模拟，验证了所提出的聚类结构粒子群优化算法的可行性和优越性。
A new cluster-structured Particle Swarm Optimization (PSO) with interaction and diversity of parameters is proposed in this paper. A swarm of PSO is divided into some sub-swarms (clusters), and not only interactions between sub-swarms and but also diversity of PSO parameters are added so as to improve the search ability of PSO. The cluster structure and the interaction of the proposed PSO are analyzed through some numerical simulations. The feasibility and the advantage of the proposed cluster-structured PSO are demonstrated through numerical simulations using four typical optimization test problems.