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工程科学与技术:2008,40(5):171-176
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一种基于多种群分层的粒子群优化算法
(1.四川大学电气信息学院;2.四川大学 电气信息学院,四川 成都 610065)
A Hierarchical Structure Poly-Particle Swarm Optimization Algorithm
(1.School of Electrical Information, Sichuan University;2.School of Electrical Info., Sichuan Univ., Chengdu 610065,China)
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投稿时间:2008-03-26    修订日期:2008-05-27
中文摘要: 为解决粒子群优化(PSO)算法收敛速度慢,易于早熟的不足,本文采用控制理论的分层思想,提出了多种群分层PSO算法(HSPPSO)。在第一层采用多种群粒子群并行计算。第二层把每个种群看成一个粒子,种群的最优值作为当前粒子的个体最优值,进行第二层粒子群优化。并把优化结果返回到第一层。在PSO算法的运行过程中,对有集聚倾向的粒子进行速度变异处理,重新初始化速度。最后对4个典型的测试函数进行了测试,研究结果表明,与基本微粒群算法比较,本文提出的算法提高了算法的收敛速度和收敛精度,改善了算法的性能。本文提出的算法对大规模系统的优化问题求解提供了一个新的思路。
Abstract: In order to improve the performances of particle swarm optimization(PSO)on convergence rate and accurate, a hierarchical structure poly-particle swarm optimization(HSPPSO) approach using the hierarchical structure concept of control theory was presented. In the bottom layer, parallel optimization calculation was performed on poly-particle swarms. In the top layer, each particle swam in the bottom layer was treated as a particle of the single particle swarm. The best position found by each particle swarm in the bottom layer was regard as the best position of single particle of the top layer. The result of optimization on the top layer was fed back to the bottom layer. If some particles trended to local extremum in PSO algorithm implementation, the particle velocity was updated and re-initialized. The test of proposed method on four typical functions showed that HSPPSO performs better than PSO both on convergence rate and accurate solutions. The HSPPSO proposed in this paper provided a new idea for large scale system optimization problem.
文章编号:200800068     中图分类号:    文献标志码:
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吕林,罗绮,刘俊勇.一种基于多种群分层的粒子群优化算法[J].工程科学与技术,2008,40(5):171-176.
Lv Lin,罗绮,刘俊勇.A Hierarchical Structure Poly-Particle Swarm Optimization Algorithm[J].Advanced Engineering Sciences,2008,40(5):171-176.