计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 217-222.doi: 10.11896/jsjkx.210700032
李丹丹, 吴宇翔, 朱聪聪, 李仲康
LI Dan-dan, WU Yu-xiang, ZHU Cong-cong, LI Zhong-kang
摘要: 针对麻雀搜索算法收敛速度慢、易陷入局部最优值、寻优精度低等缺陷进行研究,提出一种基于多种改进策略的改进麻雀搜索算法(IM-SSA)。首先,利用Tent混沌序列丰富麻雀搜索算法的初始种群,扩大搜索区域范围,并在发现者中引入自适应交叉变异算子,丰富发现者种群的多样性,平衡全局与局部的搜索能力;其次,按照每次迭代后种群个体的特点选用t-分布扰动或差分变异进行扰动,避免算法后期种群单一,提升算法跳出局部最优值的能力;最后,分别使用IM-SSA算法、灰狼算法、粒子群算法、鲸鱼算法和经典麻雀搜索算法对8个测试函数进行仿真。通过仿真结果对比分析可得,IM-SSA比其他4种算法收敛速度更快,跳出局部最优值能力更强,寻优精度更高。与当前现有改进麻雀搜索算法仿真结果的对比也表明,IM-SSA算法的改进策略更优。
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