A Hybrid Particle Swarm Optimization Method

This paper proposes a hybrid particle swarm optimization (PSO) method, which is based on the fusion of the PSO, clonal selection algorithm (CSA), and mind evolutionary computation (MEC). The clone function borrowed from the CSA and MEC-characterized similartaxis and dissimilation operations are embedded in the original PSO. Simulations of nonlinear function optimization are made to compare this hybrid PSO with the regular PSO. It has been demonstrated that our hybrid algorithm can achieve a better convergence performance, and provide diverse solutions to multi-model optimization problems.