Particle Swarm Optimization with Adaptive Local Search
一种结合自适应局部搜索的粒子群优化算法

In this paper,a hybrid particle swarm optimization algorithm combined with adaptive local search method is proposed.During the global searching process,the adaptive local search operator which can vary the size of the local search area adaptively in response to the current state of the population is used to enforce the local search ability of particle swarm optimization.The performance of the hybrid algorithm is validated on several famous benchmark functions,and the results show that this method can achieve higher success radio and better solution quality on most selected functions,especially it is a promising way for complex functions optimization with high dimensions.