Three Approaches for Solving the Stochastic Multiobjective Programming Problem

In this paper, we consider the multiobjective optimization problem in which each objective function is disturbed by noise. Three approaches using learning automata, random optimization method, and stochastic approximation method are proposed to solve this problem. It is shown that these three approaches are able to find appropriate solutions of this problem. Several computer simulation results also confirm our theoretical study.

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