Improving the optimization performance of NSGA-II algorithm by experiment design methods

NSGA-II is an effective multi-objective optimization algorithm, and how to further improve its optimizing performance is an interesting but difficult problem. The Orthogonal Array method(OA) and the Taguchi method are two important kinds of experiment design methods. In this paper, the classical genetic operators are replaced by these experiment design methods to generate new individuals in NSGA-II. This results into two hybrid NSGA-II algorithms, whose optimizing ability is approved by the experiments on the typical multi-objective test functions, and the algorithm combined with Taguchi method is better than the other one with OA, while the calculation complexity of the former is a little higher. In fact, the differences between NSGA-II and the two hybrid algorithms are just the steps to generate new individuals, and the hybrid algorithms don´t change any other operations of NSGA-II, which makes them easy for implementation.