Adaptive mating control based multiobjective evolutionary algorithm

In order to balance the exploitation and exploration during the search process, this paper designs a reproduction utility based adaptive mating control strategy with the assistance of K-means algorithm, and thus proposes an adaptive mating control based multiobjective evolutionary algorithm(ACEA). K-means algorithm is firstly applied to discover the population distribution structure. Then the mating control probability is used to restrict parents to be selected from the neighbors in the same cluster or from the whole population for reproduction to emphasize on exploitation or exploration respectively. The mating control probability is updated at each generation according to the reproduction utility by different reproduction mechanisms in previous generations. This paper adopts the standard test instances and five representative multiobjective evolutionary algorithms to test the performance of ACEA. The results verify the superiority of the proposed algorithm.