Updating the global best and archive solutions of the dynamic vector-evaluated PSO algorithm using Є-dominance

Abstract

Dynamic multi-objective optimisation problems have more than one objective, at least two objectives that are in conflict with one another and at least one objective that changes over time. These kinds of problems do not have a single optimum due to the conflict between the objectives. Therefore, a new approach is required to determine the quality of a solution. Traditionally in multi-objective optimisation (MOO) Pareto-dominance have been used to compare the quality of two solutions. However, in order to increase the speed of convergence and the diversity of the found solutions, Є-dominance has been proposed. This study investigates the effect of using Є-dominance for two aspects of the dynamic vector evaluated particle swarm optimisation (DVEPSO) algorithm, namely: updating the global best and managing the archive solutions. The results indicate that applying Є-dominance instead of Pareto-dominance to either both of these aspects of the algorithm, or only to the global best update, does improve the performance of DVEPSO.