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

Proposes a divided-range multi-objective genetic algorithm (DRMOGA), which is a model for the parallel processing of genetic algorithms (GAs) for multi-objective problems. 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, a simple GA for multi-objective problems is performed. After some generations, all the individuals are gathered and they are sorted again. In this model, the Pareto-optimal solutions which are close to each other are collected into one sub-population. Therefore, this algorithm increases the calculation efficiency and a neighborhood search can be performed. Through numerical examples, the following facts become clear: (i) the DRMOGA is a very suitable GA model for parallel processing, and (ii) in some cases it can derive better solutions compared to both the single-population model and the distributed model