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|Abstract=This paper presents quantitative comparison of the performance of different methods for selecting the guide particle for multi-objective particle swarm optimization (MOPSO). Two principal methods are compared: the recently described Sigma method, and a new "Centroid" method. Drawing on the different dominant behaviors exhibited by the different selection methods, a variety of hybridizations of these is proposed to develop a more robust optimization algorithm. Statistical analysis of the hybrid methods demonstrates their contribution to improved performance of the optimization algorithm. | |Abstract=This paper presents quantitative comparison of the performance of different methods for selecting the guide particle for multi-objective particle swarm optimization (MOPSO). Two principal methods are compared: the recently described Sigma method, and a new "Centroid" method. Drawing on the different dominant behaviors exhibited by the different selection methods, a variety of hybridizations of these is proposed to develop a more robust optimization algorithm. Statistical analysis of the hybrid methods demonstrates their contribution to improved performance of the optimization algorithm. | ||
|ISBN=0-7695-2734-5 | |ISBN=0-7695-2734-5 | ||
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Aktuelle Version vom 24. September 2009, 16:55 Uhr
Hybrid Particle Guide Selection Methods in Multi-Objective Particle Swarm Optimization
Hybrid Particle Guide Selection Methods in Multi-Objective Particle Swarm Optimization
Published: 2006
Dezember
Buchtitel: eSceience Conference
Seiten: auf CD erschienen
Verlag: IEEE Computer Society Washington, DC, USA
Referierte Veröffentlichung
BibTeX
Kurzfassung
This paper presents quantitative comparison of the performance of different methods for selecting the guide particle for multi-objective particle swarm optimization (MOPSO). Two principal methods are compared: the recently described Sigma method, and a new "Centroid" method. Drawing on the different dominant behaviors exhibited by the different selection methods, a variety of hybridizations of these is proposed to develop a more robust optimization algorithm. Statistical analysis of the hybrid methods demonstrates their contribution to improved performance of the optimization algorithm.
ISBN: 0-7695-2734-5