Reference Point-Based Particle Swarm Optimization Using a Steady-State Approach

Conventional multi-objective Particle Swarm Optimization (PSO) algorithms aim to find a representative set of Pareto-optimal solutions from which the user may choose preferred solutions. For this purpose, most multi-objective PSO algorithms employ computationally expensive comparison procedures such as non-dominated sorting. The crucial task of choosing a single preferred solution from the obtained Pareto-optimal solution set can be difficult for the user, especially when the number of objectives is large. We address these two issues and suggest a PSO algorithm, Reference point-based PSO using a SteadyState approach (RPSO-SS), that finds a preferred set of solutions near user-provided reference points, instead of the entire set of Pareto-optimal solutions. RPSO-SS reduces the high computational effort of the non-dominated sorting procedure by using simple replacement strategies within a steady-state environment. The efficacy of RPSO-SS in finding desired regions of solutions is illustrated using some well-known two and threeobjective test problems. Moreover, the performance of RPSO-SS is benchmarked using a hypervolume-based measure.