On the optimality of particle swarm parameters in dynamic environments

Barend J. Leonard, A. Engelbrecht
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引用次数: 18

Abstract

This paper investigates whether the optimal parameter configurations for particle swarm optimizers (PSO) change when changes in the search landscape occur. To test this, specific environmental changes that may occur during dynamic function optimization are deliberately constructed, using the moving peaks function generator. The parameters of the chargedand quantum PSO algorithms are then optimized for the initial environment, as well as for each of the constructed problems. It is shown that the optimal parameter configurations for the various environments differ not only with respect to the initial optimal configurations, but also with respect to each other. The results lead to the conclusion that PSO parameters need to be re-optimized or selfadapted whenever environmental changes are detected.
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动态环境下粒子群参数的最优性研究
本文研究了当搜索环境发生变化时,粒子群优化器的最优参数配置是否发生变化。为了测试这一点,使用移动峰值函数生成器,故意构造了动态函数优化期间可能发生的特定环境变化。电荷粒子群算法和量子粒子群算法的参数针对初始环境以及每个构建的问题进行了优化。结果表明,各种环境下的最优参数配置不仅与初始最优配置不同,而且彼此之间也存在差异。结果表明,当检测到环境变化时,PSO参数需要重新优化或自适应。
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