Hybrid particle swarm-based-simulated annealing optimization techniques

Particle Swann Optimization (PSO) algorithms recently invented as intelligent optimizers with several highly desirable attributes. In this paper, two new hybrid Particle Swam Optimization schemes are proposed. The proposed hybrid algorithms are based on using the Particle Swarm Optimization techniques in conjunction with the Simulated Annealing (SA) approach. By simulating three different test functions, it is shown how the proposed hybrid algorithms offer the capability of converging toward the global minimum or maximum points. More importantly, the simulation results indicate that the proposed hybrid particle swarm-based simulated annealing approaches have much superior convergence characteristics than the previously developed PSO methods.