Improved Particle Swarm Optimization Algorithm Based on Random Perturbations

摘要

This paper proposed an novel improved particle swarm optimizer algorithm based on random perturbations (PSO-RP) with global convergence performance. Random perturbations are introduced to improve the performance of global convergence of the particle swarm optimizer (PSO). The novel search strategy enables the PSO-RP to make use of random information, in addition to experience, to achieve better quality solutions. Simulations show the novel random search strategy enables the PSO-RP 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 PSO-RP. From experiments, we observe that the PSO-RP significantly improves the PSO's performance and performs better than the basic PSO and other recent variants of PSO.