Reverse-Learning Particle Swarm Optimization Algorithm Based on Niching Technology

Abstract

To solve the precocious and convergence problem, a reverse-learning particle swarm optimization algorithm based on niching technology (NRPSO) was proposed. The algorithm not only retains the historically optimal position of each particle, but also retains the historically worst position of the particle. When the particle has been trapped into a local optimum, the reverse-learning mechanism is adopted. Niche is generated through fuzzy clustering. The simulated annealing method is used inside the niches to guide excellent particles for local mining and learning. The reverse-learning mechanism is adopted between the niches and the particles are guided by the niche territory with low average of fitness to jump out of the local optimum. The experiment results on a set of benchmark functions with different dimensions show that the optimization performance, search efficiency and convergence speed of NRPSO algorithm are much better than other modified PSO algorithm.