计算机科学 ›› 2023, Vol. 50 ›› Issue (4): 204-211.doi: 10.11896/jsjkx.220100242
刘晓楠, 安家乐, 何明, 宋慧超
LIU Xiaonan, AN Jiale, HE Ming, SONG Huichao
摘要： 为提升量子萤火虫算法(Quantum Firefly Algorithm,QFA)的搜索性能,解决其在面对部分问题时易陷入局部最优等问题,文中提出了一种引入混沌映射、邻域搜索以及自适应随机扰动的改进量子萤火虫算法——混沌自适应量子萤火虫算法(Chaotic Adaptive Quantum Firefly Algorithm,CAQFA)。该算法将混沌映射应用于种群的初始化阶段,提高初始种群的质量;并在更新阶段对当前种群中的最优个体进行邻域搜索,增强算法跳出局部最优的能力;对其他个体引入自适应的随机扰动,增加算法的随机性,在对搜索空间的探索和开发之间寻找平衡,以此提升算法的性能。文中选取了18个不同类型的基准函数对算法的性能进行测试,并将其与萤火虫算法(Firefly Algorithm,FA)、QFA以及量子粒子群优化(Quantum Particle Swarm Optimization,QPSO)算法进行对比。实验结果表明,CAQFA具有更好的搜索能力和稳定性,表现出了较强的竞争力。
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