Multi-strategy competitive-cooperative co-evolutionary algorithm and its application

In order to effectively solve multi-objective optimization problems (MOPs) and fully balance uni-formity and convergence, a multi-strategy competitive-cooperative co-evolutionary algorithm based on adaptive random competition and neighborhood crossover, namely MSCOEA is developed in this paper. In the MSCOEA, a new adaptive random competition strategy is designed to determine whether one sub-population loses diversity through the performance. A random competition pro-cess is executed to increase the sub-population diversity in order to compete for participation op-portunities in the next iteration. And the extra population is employed to store the found non -dominated solutions. A new neighborhood crossover strategy is designed to enhance the local search ability. Finally, three different types of multi-objective benchmark functions are selected to verify the effectiveness of the MSCOEA. The experiment results show that the MSCOEA can effec-tively balance convergence and uniformity, and obtains better optimization performance and robustness by comparing with other algorithms. The convergence performance of the adaptive random competition and the neighborhood crossover strategies are also analyzed in detail.