Heterogeneous dynamic vector evaluated particle swarm optimisation for dynamic multi-objective optimisation

Optimisation problems with more than one objective, where at least one objective changes over time, are called dynamic multi-objective optimisation problems (DMOOPs). Since at least two objectives are in conflict with one another, a single solution does not exist, and therefore the goal of a dynamic multi-objective optimisation algorithm (DMOA) is to track the set of optimal trade-off solutions over time. One of the major issues when solving optimisation problems, is balancing exploration and exploitation during the search process. This paper investigates the performance of the dynamic vector evaluated particle swarm optimisation (DVEPSO) algorithm using heterogeneous PSOs (HPSOs), where each particle has a different behaviour. The goal of the study is to determine whether the use of heterogeneous particle swarm optimisation (HPSO) algorithms will improve the performance of DVEPSO by incorporating particles with exploration and exploitation behaviour in a single particle swarm optimisation (PSO) algorithm. The results indicate that using HPSOs improves the performance of DVEPSO, especially for type I and type III DMOOPs.