一种新的求解优化问题的混合粒子群算法

将粒子群优化算法与一种自适应局部搜索算法相结合,提出了一种新的混合粒子群优化算法,使粒子群算法寻优过程中的全局搜索能力和局部搜索能力良好平衡;采用了典型函数和模糊神经网络优化问题对算法性能进行测试,并与其它方法进行比较.实验结果表明,这种混合粒子群优化算法能获得质量更好的解,具有较高的收敛性,特别是在高维复杂函数优化上具有很强的竞争力,其性能大大优于单一的优化方法.

A New Hybrid Particle Swarm Optimization Algorithm for Optimization Problems

A hybrid particle swarm optimization algorithm combined with adaptive local search method is proposed,which can efficiently balance the global and local search ability during the searching process.The performance of the hybrid algorithm is validated on benchmark functions and fuzzy neural network optimization problems,and the results show that this method can achieve higher success radio and better solution quality,especially it is a promising way for complex functions optimization with high dimensions.The performance of the hybrid algorithm excels the single optimization methods.