A Scalable Coevolutionary Particle Swarm Optimizer

Most existing evolutionary algorithms are only effective for optimization problems with no more than one hundred decision variables. However, many optimization problems involve more than several hundreds decision variables, even one thousand. To deal with these problems, a fast cooperative coevolutionary particle swarm optimizer (FCPSO) is proposed, which is constructed based on cooperative coevolutionary framework and particle swarm optimizer with simple mutation operator. Simulation experiments on several benchmark functions from one hundred to one thousand decision variables are presented. FCPSO can solve these unimodal and multimodal benchmark functions while the function evaluations needed only linearly increase with decision variables. Therefore, FCPSO is a competitive optimizer for large scale and complex problems.