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

Abstract

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. We propose a PSO algorithm, 
Reference point-based PSO using a Stead-State 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 uses 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 three-objective test problems.