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An Improved Particle Swarm Optimization Based on Self-Adaptive Escape Velocity
Ran He(赫然); Yongji Wang; Qing Wang; Jinhui Zhou
2005
发表期刊Chinese Journal of Software
卷号16期号:12页码:2036-2044
摘要To deal with the problem of premature convergence and slow search speed, this paper proposes a novel particle swarm optimization (PSO) called self-adaptive escape PSO, which is guaranteed to converge to the global optimization solution with probability one. Considering that the organisms have the phenomena of escaping from the original cradle when they find the survival density is too high to live, this paper uses a special mutation –escape operator to make particles explore the search space more efficiently. The novel strategy produces a large speed value dynamically according to the variation of the speed, which makes the algorithm explore the local and global minima thoroughly at the same time. Experimental simulations show that the proposed method can not only significantly speed up the convergence, but also effectively solve the premature convergence problem .
关键词Particle Swarm Optimization Escape Velocity Self-adaptive Mutation Swarm Intelligence
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/21081
专题09年以前成果
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GB/T 7714
Ran He,Yongji Wang,Qing Wang,et al. An Improved Particle Swarm Optimization Based on Self-Adaptive Escape Velocity[J]. Chinese Journal of Software,2005,16(12):2036-2044.
APA Ran He,Yongji Wang,Qing Wang,&Jinhui Zhou.(2005).An Improved Particle Swarm Optimization Based on Self-Adaptive Escape Velocity.Chinese Journal of Software,16(12),2036-2044.
MLA Ran He,et al."An Improved Particle Swarm Optimization Based on Self-Adaptive Escape Velocity".Chinese Journal of Software 16.12(2005):2036-2044.
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