Particle swarm optimization using dynamic neighborhood topology for large scale optimization

In this paper, a novel particle swarm optimization (PSO) with dynamic neighborhood topology is considered for large scale optimization. Because the large scale computation problem exists commonly in industry, and is different from the canonical optimization process, solving this problem is imperative. The dynamic neighborhood topology could assist the PSO algorithm cooperate with neighbor particles and overcome the premature problem. Then according to established topology, constitute sub-swarms to improve large-scale computing effects. The simulation results demonstrate good performance of the proposed algorithm in solving a series of significant benchmark test functions.