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

Authors:
Richard Allmendinger;Xiaodong Li;Jürgen Branke
Affiliations:
Institute AIFB, University of Karlsruhe, Karlsruhe, Germany;School of Computer Science and Information Technology, RMIT University, Melbourne, Australia;Institute AIFB, University of Karlsruhe, Karlsruhe, Germany
Venue:
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Year:
2008

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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 Steady-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.