Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO)

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Details zur Publikation

Autor(en): Mostaghim S, Teich J
Verlag: Institute of Electrical and Electronics Engineers Inc.
Jahr der Veröffentlichung: 2013
Seitenbereich: 26-33
ISBN: 9780780379145


Abstract


In multi-objective particle swarm optimization (MOPSO) methods, selecting the best local guide (the global best particle) for each particle of the population from a set of Pareto-optimal solutions has a great impact on the convergence and diversity of solutions, especially when optimizing problems with high number of objectives. This paper introduces the Sigma method as a new method for finding best local guides for each particle of the population. The Sigma method is implemented and is compared with another method, which uses the strategy of an existing MOPSO method for finding the local guides. These methods are examined for different test functions and the results are compared with the results of a multi-objective evolutionary algorithm (MOEA).



FAU-Autoren / FAU-Herausgeber

Teich, Jürgen Prof. Dr.-Ing.
Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design)


Autor(en) der externen Einrichtung(en)
Universität Paderborn


Zitierweisen

APA:
Mostaghim, S., & Teich, J. (2013). Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO). (pp. 26-33). Institute of Electrical and Electronics Engineers Inc..

MLA:
Mostaghim, Sanaz, and Jürgen Teich. "Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO)." Proceedings of the 2003 IEEE Swarm Intelligence Symposium, SIS 2003 Institute of Electrical and Electronics Engineers Inc., 2013. 26-33.

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Zuletzt aktualisiert 2018-16-10 um 21:50