计算机科学 ›› 2022, Vol. 49 ›› Issue (10): 118-125.doi: 10.11896/jsjkx.210800008
刘成汉, 何庆
LIU Cheng-han, HE Qing
摘要： 针对算数优化算法(Arithmetic Optimization Algorithm,AOA)寻优速度慢、精度低和易受局部极值点影响的问题,提出了一种自适应分组融合改进算数优化算法(Adaptive Grouping Fusion Improved Arithmetic Optimization Algorithm,AG-AOA)。首先,采用Halton序列初始化个体位置,提高迭代初期算法的多样性;然后,引入自适应分组策略对种群进行分组操作,根据适应度值大小把个体自适应分为优势组、均势组和劣势组;最后,对各组个体分别采用教与学优化策略、精英反向学习策略和振荡扰动算子进行位置更新,以提高AOA的搜索能力,减小局部极值点对算法的影响。通过包含各种复杂程度的测试函数对AG-AOA的性能进行验证,包括基准测试函数、统计显著性的Wilcoxon秩和检验以及部分CEC2014测试函数。将AG-AOA应用于两个实际工程优化问题,并将所得结果与其他元启发式算法进行了比较和分析,验证了AG-AOA的优越性。
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