Opposition-based learning fully informed particle swarm optimizer without velocity

Abstract

By applying full information and employing the notion of opposition-based learning, a new opposition based learning fully information particle swarm optimiser without velocity is proposed for optimization problems. Different from the standard PSO, particles in swarm only have position without velocity and the personal best position gets updated using opposition-based learning in the algorithm. Besides, all personal best positions are considered to update particle position. The theoretical analysis for the proposed algorithm implies that the particle of the swarm tends to converge to a weighted average of all personal best position. Because of discarding the particle velocity, and using full information and opposition-based learning, the algorithm is the simpler and more effective. The proposed algorithm is applied to some well-known benchmarks. The relative experimental results show that the algorithm achieves better solutions and faster convergence. © 2013 Springer-Verlag Berlin Heidelberg.