The new model of parallel genetic algorithm in multi-objective optimization problems - Divided range multi-objective genetic algorithm

In this paper, Divided Range Multi-Objective Genetic Algorithm (DRMOGA) is proposed. The DRMOGA is a model of genetic algorithm in multi-objective problems for parallel processing. In the DRMOGA, the population of GAs is sorted with respect to the values of the objective function and divided into sub populations. In each sub population, simple GA for multiobjective problems is performed. After some generations, all individual are gathered and they are sorted again. In this model, the Pareto optimum solutions which are close to each other are collected by one sub population. Therefore, this algorithm increases the calculation efficiency, and the neighborhood search can be performed. Through the numerical examples, the followings are become cleared. The DRMOGA is very suitable GA model for parallel processing. In some cases, the DRMOGA can derive the better solutions compared to both the single population model and the distributed model.