Parallel particle swarm optimization algorithm based on space-division and layered search
基于分区和分层搜索的并行粒子群算法*

To improve the efficiency of particle swarm optimization, this paper proposed a novel parallel particle swarm optimization algorithm(SLPSO).The basic idea is parallel mechanism and space-division and layered search. The main contributions include, divided whole search space into n sub ares. For certain generations, let the best sub area be the search space. This shrinked the search space to the solution space. Proposed two layers partition of particles, the lower and topper work well for global and local search respectively. By experiments on four benchmark functions, shows that, the new algorithm increases precision by 80.37% compared with IPPSO.