Chaotic Differential Evolution Particle Swarm Cooperative Optimization Algorithm

To improve the performance of differential evolution particle swarm optimization,a novel chaotic differential evolution particle swarm cooperative optimization algorithm is proposed,which is combined the opposition-based learning and the interactive learning strategy.In this algorithm,an initialization strategy based on the opposition-based learning is applied to diversify the initial individuals in the search space.All individuals are randomly divided into two sub-swarm,one sub-swarm searches via improved chaotic differential evolution,and the other searches via improved chaotic particle swarm optimization at the same time.The interactive learning strategy is introduced in the bi-group to maintain the population diversity and accelerate the convergence speed.Experiments on four complex benchmark functions with high dimension,simulation results further demonstrate that,the algorithm not only effectively avoids the premature convergence,but also has rapid convergence speed,high solution precision,good searching ability and robustness.