DIVIDED RANGE GENETIC ALGORITHMS IN MULTIOBJECTIVE OPTIMIZATION PROBLEMS
Tomoyuki Hiroyasu, Mitsunori Miki, Sinya Watanabe · 1999
In this paper, Divided Range Genetic Algorithm in Multi objective optimization Problems (DRGA) is proposed. In this method, population of GAs is sorted with respect to the objective function and divided into sub populations. In this model, the Pareto optimum solutions which are close to each other are collected by one sub population. Therefore, by this algorithm, the calculation efficiency is increased, and the neighborhood search can be performed. Through the numerical examples, the followings are made cleared . DRGA is very suitable GA model for parallel processing. DRGA can derive the good solutions compared to the single population model and the distributed model. Keywords: Multi Objective Problems, Genetic Algorithms, Distributed Processing, Parallel Processing 1. INTRODUCTION Genetic Algorithm (GA) is one of the random search methods and simulates the mechanism of heredity and evolution of creatures (Goldberg,1989). The usual optimization methods are the kinds of gradient meth...