一种混合多策略改进的麻雀搜索算法

A hybrid multi-strategy improved sparrow search algorithm

针对麻雀搜索算法SSA求解目标函数最优解时具有过早收敛、在多峰条件下易陷入局部最优和在高维情况下求解精度不足等问题,提出了一种混合多策略改进的麻雀搜索算法MISSA。考虑到算法初始解的质量很大程度上会影响整个算法的收敛速度与精度,引入精英反向学习策略,扩大算法的搜索区域,提升初始种群的质量和多样性;对步长进行分阶段控制,以提高算法的求解精度;通过在跟随者的位置中加入Circle映射参数与余弦因子,提高算法的遍历性与搜索能力;采用自适应选择机制在麻雀个体位置更新中加入Lévy飞行,增强算法寻优和跳出局部最优的能力。将改进后的算法与麻雀搜索算法及其他算法在13个测试函数上进行对比,并进行Friedman检验。实验结果表明,改进后的麻雀搜索算法能够有效提高寻优精度与收敛速度,并在高维问题中也具备较高的稳定性。

Aiming at the problems that the Sparrow Search Algorithm(SSA)still has premature convergence when solving the optimal solution of the objective function,it is easy to fall into local optimum under multi-peak conditions,and the solution accuracy is insufficient under high-dimensional conditions,a hybrid multi-strategy improved Sparrow Search Algorithm(MISSA)is proposed.Considering that the quality of the initial solution of the algorithm will greatly affect the convergence speed and accuracy of the entire algorithm,an elite reverse learning strategy is introduced to expand the search area of the algorithm and improve the quality and diversity of the initial population;the step size is controlled in stages,in order to improve the solution accuracy of the algorithm.By adding the Circle mapping parameter and cosine factor to the position of the follower,the ergodicity and search ability of the algorithm are improved.The adaptive selection mechanism is used to update the individual position of the sparrow and add Lévy flight to enhance the algorithm optimization and the ability to jump out of local optima.The improved algorithm is compared with Sparrow Search Algorithm and other algorithms in 13 test functions,and the Friedman test is carried out.The experimental comparison results show that the improved sparrow search algorithm can effectively improve the optimization accuracy and convergence speed,and it can be used in high-dimensional problems.It also has high stability.