Reference point-based particle swarm optimization using a steady-state approach

Allmendinger, R, Li, X and Branke, J 2008, 'Reference point-based particle swarm optimization using a steady-state approach', in X Li et al. (ed.) Proceedings of the 7th International Conference on Simulated Evolution and Learning (SEAL 2008), Melbourne, Australia, 7-10 December 2008.


Document type: Conference Paper
Collection: Conference Papers

Title Reference point-based particle swarm optimization using a steady-state approach
Author(s) Allmendinger, R
Li, X
Branke, J
Year 2008
Conference name The 7th International Conference on Simulated Evolution and Learning (SEAL 2008)
Conference location Melbourne, Australia
Conference dates 7-10 December 2008
Proceedings title Proceedings of the 7th International Conference on Simulated Evolution and Learning (SEAL 2008)
Editor(s) X Li et al.
Publisher Springer
Place of publication Berlin, Germany
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.
Subjects Neural, Evolutionary and Fuzzy Computation
Keyword(s) particle swarm optimization
multiobjective optimization
DOI - identifier 10.1007/978-3-540-89694-4_21
Copyright notice © 2008 Springer-Verlag Berlin Heidelberg
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