mk:ijhis06

Summary

Multi-Objective Particle Swarm Optimization by Fuzzy-Pareto-Dominance Meta-Heuristic. Mario Köppen and Christian Veenhuis. International Journal of Hybrid Intelligent Systems, 3(4):179-186, 2006. (URL)

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 PSOf? 2r algorithm is studied on a fundamental optimization problem (so-called Pareto-Box-Problem) where a complete analysis is possible. The PSOf? 2r algorithm is shown to handle the case of a larger number of objectives, and shows similar properties like the (single-objective) PSO. 

Bibtex entry

@ARTICLE { mk:ijhis06,
    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 PSOf? 2r algorithm is studied on a fundamental optimization problem (so-called Pareto-Box-Problem) where a complete analysis is possible. The PSOf? 2r algorithm is shown to handle the case of a larger number of objectives, and shows similar properties like the (single-objective) PSO. },
    AUTHOR = { Mario Köppen and Christian Veenhuis },
    ADDED = { 2007-01-24 15:04:15 +0900 },
    MODIFIED = { 2008-02-28 11:31:31 +0900 },
    HASABSTRACT = { Yes },
    JOURNAL = { International Journal of Hybrid Intelligent Systems },
    NUMBER = { 4 },
    PAGES = { 179-186 },
    PDF = { ijhis06_fm.pdf },
    TITLE = { Multi-Objective Particle Swarm Optimization by Fuzzy-Pareto-Dominance Meta-Heuristic },
    URL = { http://iospress.metapress.com/link.asp?id=6cnd6p92yxeye033 },
    VOLUME = { 3 },
    YEAR = { 2006 },
    1 = { http://iospress.metapress.com/link.asp?id=6cnd6p92yxeye033 },
}

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News

Next conferences COMPSAC 2014 (Vasteras, Sweden, July 2014), INCoS-2014 (Salerno, Italy, September 2014).

New edited book "Soft Computing in Industrial Applications", V. Snasel, P. Kroemer, M. Koeppen, G. Schaefer, Springer AISC 223, July 2013.