摘要
PSO算法对复杂函数有较强的寻优能力和收敛速度快等特点,但是它依然无法保证在搜索空间中找到全局最优点。针对粒子群算法易于陷入局部最小的弱点,提出了一种基于小波变换的粒子群算法。该算法使用全局变异因子使粒子具有了良好的全局搜索能力,同时使用了局部变异因子,使算法在搜索过程中具有较高的收敛速度。典型函数优化的仿真结果表明,该算法具有寻优能力强、搜索精度高、稳定性好等优点,适合于工程应用中的函数优化问题。
in spite of PSO has comparable or even superior search perforrnance for many hard optimization problems with faster and more stable convergence rates, but it can＇ t guarantee to find the global optima in the search space. To conquer the shortcoming of particle swarm optimization, a novel particle swarm optimization based wavelet function （WPSO） is introduced. The algorithm first uses a global mutation operator which makes the particle have excellent ability of search in a global scope. Furthermore, for improving the searching ability in local area, the algorithm uses the local mutation operator which makes the algorithm behaves well in local searching. Experimental simulations shows that the proposed algorithm has powerful optimizing ability, good stability and higher optimizing precision, so it can be applied in optimization problems.
出处
《计算机工程与设计》
CSCD
北大核心
2011年第2期693-695,699,共4页
Computer Engineering and Design
基金
中国博士后科学基金项目(20090460323)
关键词
粒子群
小波
变异
全局搜索
收敛速度
particle swarm optimization
wavelet
mutation
global searching
convergence rate
