A New Particle Swarm Optimization Algorithm for Dynamic Environments

摘要

Many real world optimization problems are dynamic in which global optimum and local optima change over time. Particle swarm optimization has performed well to find and track optima in dynamic environments. In this paper, we propose a new particle swarm optimization algorithm for dynamic environments. The proposed algorithm utilizes a parent swarm to explore the search space and some child swarms to exploit promising areas found by the parent swarm. To improve the search performance, when the search areas of two child swarms overlap, the worse child swarms will be removed. Moreover, in order to quickly track the changes in the environment, all particles in a child swarm perform a random local search around the best position found by the child swarm after a change in the environment is detected. Experimental results on different dynamic environments modelled by moving peaks benchmark show that the proposed algorithm outperforms other PSO algorithms, including FMSO, a similar particle swarm algorithm for dynamic environments, for all tested environments.