Multi-objective optimization technique using computational intelligence

Abstract

In many practical engineering design problems, the form of objective functions is not given explicitly in terms of design variables. Given the value of design variables, under this circumstance, the values of objective functions are obtained by real/computational experiments such as structural analysis, fluid-mechanical analysis, ihermodynamic analysis, and so on. Since those experiments are considerably expensive and also time consuming, thus it is actually almost impossible to find the exact solution to those problems by using conventional optimization methods. Recently, approximation methods using computational intelligence, for example, evolutionary algorithms and neural networks have been developed remarkably. Even those algorithms need a tremendous number of experiments to obtain an approximate solution. Furthermore, most engineering design problems should be formulated as multi-objective optimization problems so as to meet the diversified demands of designer. It also causes that the number of experiments goes on increasing. This paper proposes a new method using computational intelligence methods, which are a machine learning algorithm and an evolutionary algorithm, in order to make the number of experiments for finding the solution of problem with multi-objective functions as few as possible. Furthermore, this paper shows that the proposed method combining a machine learning algorithm and an evolutionary algorithm can generate well approximate Pareto frontier, and a decision making with two or three objective functions can be easily performed on the basis of visualized Pareto frontiers by our method. Finally, the effectiveness of the proposed method will be illustrated through several numerical and practical examples.