Modified Particle Swarm Optimization algorithm by enhancing search ability of global optimal particle

Firstly, the laws of the Instantaneous movement of different particles were analyzed in geometric view in this paper. The important conclusion “In the process of Particle Swarm Optimization (PSO) iteration, the movement diversity will be seriously weaken and the search ability will be greatly reduced as soon as a particle becomes the global optimal particle” was drawn. Then, for the purpose of improving the convergence speed of PSO, a novel improved strategy based on the search ability enhancement of the global optimal particle which is by means of making the inertia coefficient of the global optimal particle bigger than other particles and adjusting the search direction of the global optimal particle. Four benchmark functions were used to test the proposed improved PSO algorithm, the standard PSO algorithm and the PSO algorithm with the leader. The variance analysis of statistic theory is used to compare the performance of the three algorithms. Experiments show that the proposed algorithm converges faster in the optimization of single extreme value and multiple extreme values without severe oscillation.