| 一种改进的粒子群算法 |
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| 引用本文： | 徐青鹤,刘士荣,吕强.一种改进的粒子群算法[J].杭州电子科技大学学报,2008,28(6):103-106. |
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| 作者姓名： | 徐青鹤 刘士荣 吕强 |
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| 作者单位： | 杭州电子科技大学自动化研究所,浙江,杭州,310018 |
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| 基金项目： | 国家自然科学基金资助项目
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浙江省科技计划资助项目
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| 摘 要： | 针对粒子群算法搜索精度不高的问题,提出了一种改进的粒子群算法。该算法一方面通过跟踪个体极值、全局极值和周围极值来搜索解空间的最优值;另一方面通过引入3种非线性递减函数对惯性权重进行调整,仿真结果表明改进的粒子群算法具有更强的寻优能力及更高的搜索精度。
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| 关 键 词： | 粒子群算法 极值 惯性权重 |
A Modified Particle Swarm Optimization Algorithm |
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| XU Qing-he,LIU Shi-rong,LV Qiang.A Modified Particle Swarm Optimization Algorithm[J].Journal of Hangzhou Dianzi University,2008,28(6):103-106. |
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| Authors: | XU Qing-he LIU Shi-rong LV Qiang |
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| Affiliation: | ( Institute of Automation, Hangzhou Dianzi University, Hangzhou Zhejiang 310018, China) |
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| Abstract: | A modified particle swarm optimization is proposed to overcome the problems such as low precision that exist in the standard PSO algorithm. On one hand this algorithm searches for the extreme value by tracking three extreme values （individual extreme value, global extreme value, circumference extreme value）. On the other hand three non - linear functions are introduced to adjust the inertia weight of the particle swarm optimization algorithm. Simulations show that modified particle swarm optimization algorithm has more powerful optimizing ability and higher optimizing precision. |
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| Keywords: | particle swarm optimization extreme value inertia weight |
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