Cooperative particle swarm optimization in dynamic environments

Abstract

Most optimization algorithms are designed to solve static, unchanging problems. However, many real-world problems exhibit dynamic behavior. Particle swarm optimization (PSO) is a successful metaheuristic methodology which has been adapted for locating and tracking optima in dynamic environments. Recently, a powerful new class of PSO strategies using cooperative principles was shown to improve PSO performance in static environments. While there exist many PSO algorithms designed for dynamic optimization problems, only one cooperative PSO strategy has been introduced for this purpose, and it has only been studied under one type of dynamism. This study proposes a new cooperative PSO strategy designed for dynamic environments. The newly proposed algorithm is shown to achieve significantly lower error rates when compared to well-known algorithms across problems with varying dimensionalities, temporal change severities, and spatial change severities.