Multi-objective particle swarm optimization by fuzzy-pareto-dominance meta-heuristic

Article type: Research Article

Authors: Köppen, Mario^{a; *} | Veenhuis, Christian^b

Affiliations: [a] Kyushu Institute of Technology, Department Artificial Intelligence, 680-4, Kawazu, Iizuka, Fukuoka 820-8502, Japan | [b] Fraunhofer IPK, Department Security Technology, Pascalstr. 8-9, 10587 Berlin, Germany

Correspondence: [*] Corresponding author. E-mail: [email protected]

Abstract: This paper introduces a new approach to multi-objective Particle Swarm Optimization (PSO). The approach is based on the recently proposed Fuzzy-Pareto-Dominance (FPD) relation. FPD is a generic ranking scheme, where ranking values are mapped to element vectors of a set. These ranking values are directly computed from the element vectors of the set and can be used to perform rank operations (e.g. selecting the “largest”) with the vectors within the given set. FPD can be seen as a paradigm or meta-heuristic to formally expand single-objective optimization algorithms to multi-objective optimization algorithms, as long as such vector-sets can be defined. This was already shown for the Standard Genetic Algorithm. Here, we explore the application of this concept to PSO, where a swarm of particles is maintained. The resulting PSOf2r algorithm is studied on a fundamental optimization problem (so-called Pareto-Box-Problem) where a complete analysis is possible. The PSOf2r algorithm is shown to handle the case of a larger number of objectives, and shows similar properties like the (single-objective) PSO.

DOI: 10.3233/HIS-2006-3401

Journal: International Journal of Hybrid Intelligent Systems, vol. 3, no. 4, pp. 179-186, 2006