A study on cooperative multi-objective group search optimizer

Group Search Optimizer (GSO) is a swarm intelligence algorithm inspired from animal´s foraging behavior. The algorithm demonstrated its obvious superiority in solving complex engineering problems. Based on the strategy of divide-and-conquer and cooperative coevolution framework, a Cooperative Coevolutionary Multi-objective Group Search Optimizer (CMOGSO) is proposed in this paper. In CMOGSO, multi-objective optimization problems are decomposed according to their decision variables and are optimized by corresponding sub-groups respectively. Collaborators are selected randomly from archive and employed to construct context vectors in order to evaluate the members in sub-groups. Experimental results demonstrate that CMOGSO can more effectively and efficiently solve multi-objective optimization problems compared with other evolutionary multi-objective optimizers.