hybridizing surrogate techniques, rough sets and evolutionary algorithms to efficiently solve multi-objective optimization problems
This paper presents an approach in which a multi-objective evolutionary algorithm (MOEA) is coupled to a surrogate method in order to explore the search space in an efficient manner. A small comparative study among three surrogate methods is conducted: an artificial neural network (ANN), a radial basis function (RBF) and a support vector machine (SVM). The winner in this comparative study was the SVM. However, our results indicated that the spread of solutions achieved by our surrogate-based MOEA was poor. Thus, we decided to introduce a second phase to the algorithm in which it is hybridized with the rough sets in order to improve the spread of solutions and help to reach the true Pareto front. We show that our proposed hybrid approach only requires 2,000 fitness function evaluations in order to solve test problems with up to 30 decision variables.
surrogates multi-objective optimization hybrid algorithms