To solve the premature convergence problem of particle swarm optimization (PSO) in dealing with complex high dimensional function optimization

a novel hybrid particle swarm optimization algorithm merging crossover mutation and chaos (CMCPSO) was proposed. The main approaches included using chaos strategy to initiate positions and velocities of all particles in the design space

introducing crossover operation in each iteration to increase the diversity of particles

breaching the restrictions of local optimization points with a new chaotic disturbance mechanism and mutation operation during the later computation period. Four standard test functions were selected to have a simulation study on the proposed algorithm. The results showed that CMCPSO had a fast convergence rate and effective global optimization ability.