A Local-Search-Based Particle Swarm Optimization Algorithm and Its Performance Analysis

Particle Swarm Optimization (PSO) is an evolutionary computation technique and an optimization tool based on iteration. However, the PSO algorithm does not search the solution space of the points closest to the extrema, which leads to the bad local extrema of the final solutions. Moreover the PSO algorithm needs enough iterations to get better solutions, and it usually cannot get better solutions with the limit of iteration steps or the situation of break at any time. Based on those shortages, the f-PSO algorithm which is based on neighborhood search is presented. The algorithm searches for local optimization solutions when it upgrades the global optimization solutions each time in the iteration steps of PSO.Experiments indicate that this algorithm has a strong theoretical value and good robustness, even with the lack of computation, insufficient iterations or break at any time.