Quantum Multi-guide Particle Swarm Optimisation for Dynamic Multi-objective Optimisation Problems

Abstract The multi-guide particle swarm optimisation (MGPSO) algorithm, originally developed for static multi-objective optimisation problems (SMOPs), has been recently adapted for dynamic multi-objective optimisation problems (DMOPs). The MGPSO is a multi-swarm approach where each subswarm optimises one of the objectives. It uses a bounded, crowding distance archive implementation that is managed at each environment change. This paper further adapts the MGPSO for DMOPs by proposing alternative quantum particle swarm optimisation (QPSO) strategies to allow efficient tracking of the changing Pareto-optimal set (POS) and Pareto-optimal front (POF). Specifically, the self-adaptive QPSO and the parent-centric crossover (PCX) QPSO are explored with varying quantum proportions of particles. A total of twenty-nine benchmark functions and six performance measures were implemented to evaluate the performance of the QPSO approaches. The experiments were run against five environment types with varying temporal and spatial severities. The best QPSO strategy was then compared with other state-of-the-art dynamic multi-objective optimisation algorithms (DMOAs). An extensive empirical analysis shows that MGPSO with 10% proportion of self-adaptive quantum particles achieves very competitive and oftentimes better results when compared with other DMOAs.