An Evolutionary Optimization Method Based on Scalarization for Multi-objective Problems

In this paper, we perform some computational experiments on the new global scalarization method for multi-objective optimization problems. Its main idea is to construct, for a given multi-objective optimization problem, a global scalarization function whose values are non-negative real numbers. The points where the scalarization function attains the zero value are exactly weak Pareto stationary points for the original multi-objective problem. We apply two different evolutionary algorithms to minimize the scalarization function; both of them are designed for solving scalar optimization problems. The first one is the classical Genetic Algorithm (GA). The second one is a new algorithm called Dissimilarity and Similarity of Chromosomes (DSC), which has been designed by the authors. The computational results presented in this paper show that the DSC algorithm can find more minimizers of the scalarization function than the classical GA.