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.