The Research Of Multi-objective Genetic Algorithm For Searching Pareto Front

Genetic Algorithm (GA) is a set of new-global-optimistic search algorithm repeatedly which simulate the process of creature evolution that of Darwinian's genetic selection and natural elimination. It is widely applied to the domain of combinational evolutionary problem seeking, self-adapt controlling, planning devising, machine learning and artificial life etc. However, there are multi-objective attributes in real-world optimization problems that always conflict, so the multi-objective Genetic Algorithm (MOGA) is put forward. MOGA can deal simultaneously with many objections, and find gradually Pareto-optimal solutions.This paper presents a critical review of MOGA' current researches mainly in the last 15 years. The multi-objective optimization techniques have two branches, one with parameters and another with no parameters. It's difficult for us to select parameters in the methods with parameters and its performance is highly dependent on an appropriate selection of the sharing factor. In addition, the work speed is very low in the methods with no parameters. Therefore, we focus on proceeding the algorithm's performance with increasing the speed of searching non-dominated solutions, reducing the number of non-dominated solutions in precondition of ensuring a better distribution of individuals, and constructing new populations. The multi-objective Genetic Algorithm based on sorting and clustering efficiently increase its run efficiency, debase its compute complexity and improve its convergence performance. In this paper, we takeNSGA-II as a benchmark. It has been improved, and specially proposed: Firstly, we has increased run speed and ensure the diversity of population is with constructing non-dominated set by throwing off the dominated solutions, expressing the interior relation of individuals each other by the crowding distance, and constructing new population. Secondly, we have further improved its convergence performance by clustering in precondition of ensuring a better distribution of individuals. Simulation results on six difficult optimization problems show that the multi-objective Genetic Algorithm based on sorting and clustering have ideal effects on the aspects of its speed and diversify.