Compensate information from multimodal dynamic landscapes: An anti-pathology cooperative coevolutionary algorithm

Cooperative coevolutionary algorithms (CCEAs) divides a problem into several components and optimizes them independently. Some coevolutionary information will be lost due to the search space separation. This may lead some algorithmic pathologies, such as relative overgeneralization. In addition, according to the interactive nature of the CCEA, the coevolutionary landscapes are dynamic. In this paper, a multipopulation strategy is proposed to simultaneously search local or global optima in each dynamic landscape and provide them to the other components. Besides, a grid-based archive scheme is proposed to archive these historic collaborators for reasonable fitness evaluation. Two benchmark problems were used to test and compare the proposed algorithm to three classical CCEAs. Experimental results show that the proposed algorithm effectively counteract relative overgeneralization pathology and significantly improve the rate of converging to global optimum.