Multi-swarm particle swarm optimiser with Cauchy mutation for dynamic optimisation problems
Chengyu Hu, Bo Wang, Yongji Wang · International Journal of Innovative Computing and Applications · 2009
Many real-world problems are dynamic, requiring an optimisation algorithm which is able to continuously track a changing optimum over time. In this paper, we present a new variant of particle swarm optimisation (PSO) specifically designed to work well in dynamic environments. The main idea is to divide the population of particles into a set of interacting swarms. These swarms interact locally by dynamic regrouping and dispersing. Cauchy mutation is applied to the global best particle when the swarm detects the environment of the change. The dynamic function [proposed by Morrison and De Jong (1999)] is used to test the performance of the proposed algorithm. The numerical experimental results are compared with other variant PSO from the literature, showing that the proposed algorithm is an excellent alternative to track dynamically changing optima.