Multi-guide particle swarm optimisation archive management strategies for dynamic optimisation problems

This study presents archive management approaches for dynamic multi-objective optimisation problems (DMOPs) using the multi-guide particle swarm optimisation (MGPSO) algorithm by Scheepers et al. (Swarm Intell, 13(3–4):245–276, 2019, https://doi.org/10.1007/s11721-019-00171-0). The MGPSO is a multi-swarm approach developed for static multi-objective optimisation problems, where each subswarm optimises one of the objectives. It uses a bounded archive that is based on a crowding distance archive implementation. This paper adapts the MGPSO algorithm to solve DMOPs by proposing alternative archive update strategies to allow efficient tracking of the changing Pareto-optimal front. To evaluate the adapted MGPSO for DMOPs, a total of twenty-nine benchmark functions and six performance measures were implemented. The problem set consists of problems with only two or three objectives, and the exact time of the changes is assumed to be known beforehand. The experiments were run against five different environment types, where both the frequency of changes and the severity of changes parameters control how often and how severe the changes are during the optimisation of a DMOP. The best archive management approach was compared to the other state-of-the-art dynamic multi-objective optimisation algorithms (DMOAs). An extensive empirical analysis shows that MGPSO with a local search approach to the archive management achieves very competitive and oftentimes better performance when compared with the other DMOAs.