Approximating the epsilon -Efficient Set of an MOP with Stochastic Search Algorithms

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.

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