Improved Particle Swarm Optimization Algorithm Based on Random Perturbations

This paper proposed an novel improved particle swarm optimizer algorithm based on random perturbations (PSORP)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’sperformance and performs better than the basic PSO and other recent variants of PSO.