Computing the Set of Approximate Solutions of an MOP with Stochastic Search Algorithms

Abstract

research reportIn this work we develop a framework for the approximation of the entire set of $\epsilon$-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