A novel enhanced particle swarm optimization method with diversity and neighborhood search

By learning from different particle in local neighborhood and global neighborhood Particle Swarm Optimization (PSO) algorithm achieved a trade-off between exploration and exploitation abilities. In this paper, we propose a new approach by combining diversity mechanism and neighborhood search strategies, called a novel enhanced PSO method with Diversity and Neighborhood Search (EPSODNS). In this paper we propose a new local neighborhood search strategy that promotes the exploitation ability of algorithm. Our experiments are conducted on test benchmarks include 13 well-known numerical benchmarks. The results show that EPSODNS obtains a better majority of the test problems.