Studies On Particle Swarm Optimization With Its Application To Power Unit Coordinate Control

Particle Swarm Optimization(PSO)is an intelligent modern heuristic algorithm,which is inspired by the simulation of the behavior of prayers in nature.It is vastly employed in various industrial projects due to its fast convergence and easy to carry out.Based on the analysis of current PSO algorithms,a Hybrid Improved PSO(HIPSO)is proposed in this paper,in which chaos initialization strategy is introduced to improve the population variety,and specific algorithm parameters' control strategy based on simulated annealing is designed to enhance the search ability of the algorithm.Besides, the acceptance policy of the new derived solution is taken under the guidance of Metropolis rule to guarantee the convergence of the algorithm.In order to verify the effectiveness of the algorithm,benchmarks of numerical and power unit coordinate control case studies are taken for comparison with the other existing PSO algorithms. Statistical results analysis reviews that,our algorithm has outperformed the other three typical PSO algorithms,either in solution optimality,or in search ability,which further demonstrates the effectiveness and efficiency of our proposed algorithm.