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| 一种改进的自适应逃逸微粒群算法及实验分析 |
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赫然1, 王永吉1,2, 王青1, 周津慧1, 胡陈勇1
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1.中国科学院,软件研究所,互联网软件技术实验室,北京,100080;2.中国科学院,软件研究所,计算机科学重点实验室,北京,100080
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| 摘要: |
| 分析了变异操作对微粒群算法(particle swarmoptimization,简称PSO)的影响,针对收敛速度慢、容易陷入局部极小等缺点,结合生物界中物种发现生存密度过大时会自动分家迁移的习性,给出了一种自适应逃逸微粒群算法,并证明了它依概率收敛到全局最优解.算法中的逃逸行为是一种简化的确定变异操作.当微粒飞行速度过小时,通过逃逸运动使微粒能够有效地进行全局和局部搜索,减弱了随机变异操作带来的不稳定性.典型复杂函数优化的仿真结果表明,该算法不仅具有更快的收敛速度,而且能更有效地进行全局搜索. |
| 关键词: 微粒群算法 逃逸速度 自适应 变异操作 群体智能 |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.60373053,60473060(国家自然科学基金);the National High-Tech Research and Development Plan of China under Grant No.2004AAll2080(国家高技术研究发展计划(863));the Hundred Talents of the Chinese Academy of Sciences(中国科学院"百人计划") |
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| An Improved Particle Swarm Optimization Based on Self-Adaptive Escape Velocity |
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HE Ran,WANG Yong-Ji,WANG Qing,ZHOU Jin-Hui,HU Chen-Yong
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| Abstract: |
| 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. |
| Key words: particle swarm optimization escape velocity self-adaptive mutation swarm intelligence |
