一种结合自适应局部搜索的粒子群优化算法

本文提出一种结合自适应局部搜索的混合粒子群优化算法.该方法在粒子群优化算法的全局搜索过程中,使用能根据当前种群搜索状态自适应地调整局部搜索空间大小的局部搜索算法加强其局部搜索能力.采用了著名的基准函数对算法的性能进行测试,并与其他已有算法进行了比较.结果表明,这种混合粒子群优化算法能获得更高的搜索成功率和质量更好的解,特别在高维复杂函数优化上具有很强的竞争力.

Particle Swarm Optimization with Adaptive Local Search

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.