计算机科学 ›› 2014, Vol. 41 ›› Issue (7): 246-249.doi: 10.11896/j.issn.1002-137X.2014.07.051
吕太之,李卓
LV Tai-zhi and LI Zhuo
摘要： 针对传统粒子群优化算法(PSO)收敛速度慢及容易陷入局部极小化的问题,提出了一种改进的粒子群优化算法。新算法结合分数阶微分具有的记忆特性,使得粒子的更新融入了轨迹信息,提高了算法的收敛速度。使用Alpha稳定分布代替均匀分布使得粒子在一定概率条件下可以逃逸局部极小点,提高了粒子的全局搜索能力。仿真结果表明,算法不仅在单模态函数下具有更快的收敛速度和更有效的全局搜索能力,在复杂的具有欺骗性的多模态函数下也取得较理想的实验结果,证实了动态分数阶和Alpha稳定分布可以有效地提高粒子群优化算法的性能。
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