A modified particle swarm optimization algorithm linking dynamic neighborhood topology to parallel computation

Abstract

In this paper, a novel approach is considered, based on Particle Swarm Optimization (PSO) technique, using two concepts: evolutionary neighborhood topology associated to parallel computation for complex optimization problems. The idea behind using dynamic neighborhood topology is to overcome premature convergence of PSO algorithm, by well exploring and exploiting the search space for a better solution quality. Parallel computation is used to accelerate calculations especially for complex optimization problems. The simulation results demonstrate good performance of the proposed algorithm in solving a series of significant benchmark test functions.