A New Particle Swarm Optimization Algorithm with Random Inertia Weight and Evolution Strategy

The paper gives a new particle swarm optimization algorithm with random inertia weight and evolution strategy (REPSO). The proposed random inertia weight is using simulated annealing idea and the given evolution strategy is using the fitness variance of particles to improve the global search ability of PSO. The experiments with six benchmark functions show that the convergent speed and accuracy of REPSO is significantly superior to the one of The PSO with linearly decreasing inertia weight LDW-PSO.