Subpopulation Diversity Based Setting Success Rate of Migration for Distributed Evolutionary Algorithms

摘要

In most of distributed evolutionary algorithms (DEAs), migration interval is used to decide the frequent of migration. Nevertheless, a predetermined interval cannot match the dynamic situation of evolution. Consequently, migration may happen at a wrong moment and just exert a negative influence to evolution. In this paper, a scheme of setting the success rate of migration based on subpopulation diversity is proposed. In the scheme, migration still happens at intervals, but the probability of immigrants entering the target subpopulation will be decided by the diversity of this subpopulation. An analysis shows that the extra time consumption for our scheme in a DEA is acceptable. In our experiments, outcomes of the DEA based on the proposed scheme are compared with those of a traditional DEA on six benchmark instances of the Traveling Salesman Problem. The results show that the former performs better than its peer. Moreover, the DEA based on our scheme shows an advantage in stability.