A new multi-objective optimization algorithm based on differential evolution and neighborhood exploring evolution strategy

• Autorzy: Lobato F. S. , Steffen, Jr V.
• Języki publikacji: EN

Abstrakty

EN

In this paper a new optimization algorithm based on Differential Evolution, non-dominated sorting strategy and neighborhood exploration strategy for guaranteeing convergence and diversity through the generation of neighborhoods of different sizes to potential candidates in the population is presented. The performance of the algorithm proposed is validated by using standard test functions and metrics commonly adopted in the specialized literature. The sensitivity analysis of some relevant parameters of the algorithm is performed and compared with the classical DE algorithm without the strategy of neighborhood exploration and with other state-of-the-art evolutionary algorithms.

Słowa kluczowe

EN

multi-objective optimization; differential evolution; neighborhood exploring; evolution strategy; sorting strategy

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2011
• Tom: Vol. 1, No. 4
• Strony: 259--267
• Opis fizyczny: Bibliogr. 32 poz., rys.

Twórcy

autor

Lobato F. S.

• School of Chemical Engineering, Federal University of Uberlàndia

autor

Steffen, Jr V.

• School of Mechanical Engineering, Federal University of Uberlândia, Av. João Naves de Ávila 2121, Campus Santa Mônica, P.O. Box 593, 38408-144, Uberlândia-MG, Brazil

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