Approximating the Ɛ-efficient set of an MOP with stochastic search algorithms

Authors:
Oliver Schütze;Carlos A. Coello Coello;El-Ghazali Talbi
Affiliations:
CINVESTAV-IPN, Computer Science Department, México D.F., Mexico;CINVESTAV-IPN, Computer Science Department, México D.F., Mexico;INRIA Futurs, LIFL, CNRS, Villeneuve d'Ascq, France
Venue:
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Year:
2007

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Abstract

In this paper we develop a framework for the approximation of the entire set of Ɛ-efficient solutions of a multi-objective optimization problem with stochastic search algorithms. For this, we propose the set of interest, investigate its topology and state a convergence result for a generic stochastic search algorithm toward this set of interest. Finally, we present some numerical results indicating the practicability of the novel approach.