Improved particle swarm optimization algorithm and its global convergence analysis

This paper proposed an novel improved particle swarm optimizer (PSO) algorithm with global convergence performance. The global optimum position is unpredictable, so a random solution is introduced to the improved PSO as the best solution(P_g) in the end of every generation. The novel search strategy enables the improved PSO to make use of the uncertain information, in addition to experience, to achieve better quality solutions. Theoretical proof shows the novel random search strategy enables the improved PSO to own the performance of global convergence. Five of well-known benchmarks used in evolutionary optimization methods are used to evaluate the performance of the improved PSO. From experiments, we observe that the improved PSO significantly improves the PSO´s performance and performs better than the basic PSO and other recent variants of PSO.