Discussion of parallel model of multi-objective genetic algorithms on heterogeneous computational resources

Abstract

In this paper, a parallel model of multi-objective genetic algorithm supposing a grid environment is discussed. In this proposed parallel model, we extended master-slave model which has high degree of parallelism, and 2 individuals as a crossover pair are transmitted to each slave process. Then the number of offspring generated by crossover is changed dynamically adapting to the performance of the each calculation resource. This mechanism is effective for heterogeneous computational resources. In addition, total communication cost can be reduced by increasing processing load of the slave processes, and reduction of the overhead time is expected. Moreover, we incorporated the neighborhood crossover, in which the crossover is performed between individuals that are close to each other in the objective space. Therefore, 2 individuals which are close to each other are sent to each slave process. This neighborhood crossover improves the search ability. Computational experiments on heterogeneous computational resources indicated that the proposed model was able to utilize the maximum performance of all calculation resources and reduce the overhead time.