An improved Particle Swarm Optimization algorithm with rank-based selection

Particle swarm optimization (PSO) is a population-based, self adaptive search optimization technique that has been applied to find optimal or near-optimal solutions for real-world optimization problems. In this paper, rank-based selection is proposed for the particle swarm optimizer. The method applies rank-based selection to replace half of the lower fitness population with the higher fitness population of the swarm. Performance is compared with some other methods using the benchmark function.