计算机科学 ›› 2020, Vol. 47 ›› Issue (8): 291-296.doi: 10.11896/jsjkx.190600107
李阳, 李维刚, 赵云涛, 刘翱
LI Yang, LI Wei-gang, ZHAO Yun-tao, LIU Ao
摘要: 在标准灰狼优化算法寻优的中后期, 由于衰减因子减小, 灰狼群体中的个体均向领导层灰狼所在区域靠近, 导致算法的全局寻优能力差, 降低了寻优精度。针对该问题, 提出了一种改进灰狼优化算法(Improved Grey Wolf Optimization, IGWO)。该算法首先分析了衰减因子对灰狼算法(Grey Wolf Optimization, GWO)的影响, 提出了一种分段可调节衰减因子, 用于平衡算法的勘探能力与开发能力。其可以根据不同优化问题来寻找适当的参数, 实现更高精度的寻优, 并且保证了在寻优过程的中后期, 算法也具有一定的全局搜索能力。数值仿真实验表明, 提高勘探比例有利于提高算法的收敛精度。同时, 在寻优过程中, 根据概率选择对领导层灰狼分别进行莱维飞行操作或随机游动操作。利用莱维飞行短距离搜索与偶尔较长距离行走相间的搜索特点, 提高算法的全局寻优能力;利用随机游动相对集中的搜索特性, 提高局部寻优能力。最后, 对8个标准测试函数进行仿真实验, 并与其他几种算法进行比较, 实验结果表明, 所提算法在寻优精度、算法稳定性及收敛速度上都有较大优势。
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