Abstract
Most practical problems are formulated as multi-objective optimization problem with conflicting objectives so as to meet diversified demands of a decision maker. Generally, there is no solution to optimize all objective functions simultaneously in multi-objective optimization, thus Pareto optimal solution is introduced. Recently, many methods using evolutionary algorithms have been developed for generating Pareto optimal solutions. In the cases with two or three objective functions, especially, those methods been shown to be useful for visualizing the set of Pareto optimal solutions, which is called Pareto frontier. This paper proposes a multi-objective optimization technique by using computational intelligence in order to generate well approximate Pareto frontiers. By combining a machine learning algorithm and an evolutionary algorithm, it will be shown that the proposed method can find effectively, actively and fast the close Pareto frontier to the real one, through several numerical and practical examples.
