计算机科学 ›› 2022, Vol. 49 ›› Issue (8): 237-246.doi: 10.11896/jsjkx.210700150
陈俊, 何庆, 李守玉
CHEN Jun, HE Qing, LI Shou-yu
摘要: 针对基础阿基米德优化算法收敛速度慢、容易陷入局部最优的问题,文中提出了一种基于自适应反馈调节因子的阿基米德优化算法。首先,通过佳点集初始化种群,增强初始种群的遍历性,提高初始解的质量;其次,提出自适应反馈调节因子,平衡算法的全局探索与局部开发能力;最后,提出了莱维旋转变换策略,增加种群的多样性,以防止算法陷入局部最优。将所提算法与主流算法在14个基准测试函数以及部分CEC2014函数上进行30次比较实验,结果表明,所提算法的平均寻优精度、标准差以及收敛曲线均优于对比算法。同时将所提算法分别与对比算法在14个基准函数上进行Wilcoxon秩和检验,检验结果显示所提算法与对比算法的差异性显著。将所提算法应用于焊接梁设计问题,其相比原始算法提升了2%,验证了所提算法的有效性。
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