Improved Particle Swarm Optimization Algorithm Based on Social Psychology

Aiming at the disadvantages of particle swarm optimization algorithm (PSO), which is easy to trap into local optima and converge slowly in later period of iteration, an improved particle swarm optimization algorithm based on social psychology (BSPSO) was proposed. Unlike the standard PSO algorithm, this BSPSO algorithm used asynchronous version of PSO algorithm, and adopted two strategies (divided particles into some growth stages and introduced mutations) to improve the original PSO algorithm. Division of growth stages can make particles have different learning factors at different stages, and mutations can make particles jump out of local optima effectively, so the algorithm performance was improved effectively. The simulation result shows that the BSPSO is more available than those previously proposed PSO algorithms through experiments with several benchmark functions.