A cooperative coevolutionary algorithm for multiobjective particle swarm optimization

Coevolutionary architectures have been shown to be effective ways to improve the performance of multiobjective (MO) optimization problems. This paper presents a cooperative coevolutionary algorithm for multiobjective particle swarm optimization (COMOPSO), which applies the divide-and-conquer approach to decompose decision vectors into smaller components and evolves multiple solutions in the form of cooperative subswarms. Representatives from each evolving subswarm are combined to form the solution to the whole system. The fitness of each individual is related to its ability to collaborate with individuals from other species, thereby encouraging the development of cooperative strategies. An adaptive niche sharing algorithm is introduced to handle the selection of the niche radius in a dynamic manner. Coupled with the adaptive niche sharing algorithm COMOPSO demonstrates its effectiveness and efficiency in evolving highly competitive solution sets against various MO algorithms on benchmark problems characterized by different difficulties with consistent results.