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 },
}