Multi-swarm optimization in dynamic environments

Many real-world problems are dynamic, requiring an optimization algorithm which is able to continuously track a changing optimum over time. In this paper, we present new variants of Particle Swarm Optimization (PSO) specifically designed to work well in dynamic environments. The main idea is to extend the single population PSO and Charged Particle Swarm Optimization (CPSO) methods by constructing interacting multi-swarms. In addition, a new algorithmic variant, which broadens the implicit atomic analogy of CPSO to a quantum model, is introduced. The multi-swarm algorithms are tested on a multi-modal dynamic function - the moving peaks benchmark - and results are compared to the single population approach of PSO and CPSO, and to results obtained by a state-of-the-art evolutionary algorithm, namely self-organizing scouts (SOS). We show that our multi-swarm optimizer significantly outperforms single population PSO on this problem, and that multi-quantum swarms are superior to multi-charged swarms and SOS.