Multi-objective optimization with cross entropy method: Stochastic learning with clustered pareto fronts

This paper presents a novel multiobjective optimization strategy based on the cross entropy method (MOCE). The cross-entropy method (CE) is a stochastic learning algorithm inspired from rare event simulations and proved to be successful in the solution of difficult single objective real-valued optimization problems. The presented work extends the use of cross-entropy method to real-valued multiobjective optimization. For this purpose, parameters of CE search are adapted using the information collected from clustered nondominated solutions on the Pareto front. Comparison with well known multiobjective optimization algorithms on the solution of provably difficult benchmark problem instances demonstrated that CEMO performs at least as good as its competitors.