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工程科学与技术:2009,41(1):139-146
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粒子群优化算法中的位置矢量的评价策略
(1.四川大学计算机学院;2.四川大学 计算机学院,四川 成都610065)
The Strategy to Evaluate Position Vector in Particle Swarm Optimization
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投稿时间:2008-06-12    修订日期:2008-09-01
中文摘要: 粒子群优化算法(particle swarm optimization,简称PSO)是一种新兴的并流行的群智能优化技术,但是其在解决高维多极值问题时容易陷入局部最优而早熟。很多研究者已经致力于研究此问题,但是他们大多数增加了算法的复杂性而使其更难理解。本文旨在解决这些不足,认为原算法的位置矢量的评价策略存在“两进一退”和“两退一进”的缺陷,提出了一种新的评价策略,对各粒子的位置矢量逐维进行评价,使粒子向目标最优位置“稳步前进”,其具有和标准PSO一样的收敛性分析过程,没有增加对PSO的理解难度,并且经实验证明,可以很好地解决对高维多极值问题的早熟。定义了广义评价策略,实验证明,通过调节分组数的大小,可以有效地在收敛速度和防止早熟之间平衡,并达到很好的优化性能。
Abstract:The particle swarm optimization (PSO) is a stochastic population-based optimization technique that is gaining popularity. However, it may be trapped in local optima and fail to converge to global optimum, especially for multimodal and high-dimensional problems. Many variations of PSO were aimed at the phenomenon of prematurity, but most of them added complexity to the paradigm and made PSO harder to be understood. In this paper, the evaluation of a particle’s position was investigated, and it was proposed that the evaluation strategy in standard PSO had two shortcomings, i.e. “two steps forward, and one step back” and “two steps back, and one step forward”. A novel evaluation strategy with the same convergence analysis as the standard PSO was presented, whereby each particle was evaluated in dimension-by-dimension order so as to step steadily toward the aimed position, and experiments showed that the novel strategy was very promising for multimodal and high-dimensional problems. A general evaluation strategy was defined, and experiments showed that the number of grouping could balance effectively the convergence speed and the best fitness so to get excellent performance.
文章编号:200800165     中图分类号:    文献标志码:
基金项目:国家科技部中小型科技企业创新基金(06C26225101730)
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引用文本:
胡建,李志蜀,欧鹏.粒子群优化算法中的位置矢量的评价策略[J].工程科学与技术,2009,41(1):139-146.
Hu Jian,李志蜀,欧鹏.The Strategy to Evaluate Position Vector in Particle Swarm Optimization[J].Advanced Engineering Sciences,2009,41(1):139-146.