计算机科学 ›› 2023, Vol. 50 ›› Issue (2): 317-323.doi: 10.11896/jsjkx.211100162
贾凯烨1, 董砚2
JIA Kaiye1, DONG Yan2
摘要: 针对麻雀搜索算法初始种群分布不均匀,种群间信息交流少,易陷入局部最优,收敛速度慢等不足,提出了一种基于双样本学习与单维搜索改进的精英麻雀搜索算法。首先,采用Hammersley低差异序列与反向学习相结合产生精英初始种群,增强个体质量和多样性;然后,通过双样本学习策略,改进追随者的位置更新公式,加强种群间的信息交流,提高算法跳出局部最优的能力;最后,在算法迭代后期采用单维搜索模式,增强算法在后期的深度挖掘能力,提高算法的精度。通过对时间复杂度进行分析,证明了该改进未增加算法的时间复杂度。选取12个不同特征的测试函数进行寻优,测试结果表明,与其他算法相比,该算法在收敛速度、精度和稳定性上都有明显的优越性。
中图分类号:
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