Improved Sparrow Algorithm Combining Cauchy Mutation and Opposition-Based Learning

doi:10.3778/j.issn.1673-9418.2010032

Abstract

Abstract:

Aiming at the problem that the population diversity of basic sparrow search algorithm decreases and it is easy to fall into local extremum in the late iteration, an improved sparrow search algorithm combining Cauchy variation and reverse learning (ISSA) is proposed. Firstly, this paper uses a Sin chaotic initialization population with an unlimited number of mapping folds to lay the foundation for global optimization. Secondly, this paper introduces the previous generation global optimal solution into the discoverer location-update method to enhance the sufficiency of global search. At the same time, the adaptive weight is added to coordinate the ability of local mining and global exploration, and the convergence speed is accelerated. Then, the Cauchy mutation operator and the opposition-based learning strategy are combined to perform disturbance mutation to generate new solutions at the optimal solution position, and the algorithm??s ability to jump out of local space is enhanced. Finally, this algorithm is compared with 3 basic algorithms and 2 improved sparrow algorithms. Simulation and Wilcoxon rank and inspection are performed on 8 benchmark test functions. The optimization performance of ISSA is assessed, and time complexity analysis of ISSA is carried out. The results show that ISSA has faster convergence rate and higher precision than the other 5 algorithms. And the overall optimization capabilities are improved.

Key words: sparrow search algorithm, Sin chaos, adaptive, Cauchy variation, opposition-based learning

摘要：

针对基本麻雀搜索算法在迭代后期种群多样性减小，容易陷入局部极值的问题，提出一种融合柯西变异和反向学习的改进麻雀算法（ISSA）。首先，采用一种映射折叠次数无限的Sin混沌初始化种群，为全局寻优奠定基础；其次，在发现者位置更新方式中引入上一代全局最优解，提高全局搜索的充分性，同时加入自适应权重，协调局部挖掘和全局探索的能力，并加快收敛速度；然后，融合柯西变异算子和反向学习策略，在最优解位置进行扰动变异，产生新解，增强算法跃出局部空间的能力；最后，与3种基本算法和2种改进的麻雀算法进行对比，对8个基准测试函数进行仿真实验以及Wilcoxon秩和检验，评估ISSA的寻优性能，并对ISSA进行时间复杂度分析。结果表明ISSA与其余5种算法相比，收敛速度更快，精度更高，全局寻优能力得到较大提升。

关键词: 麻雀搜索算法, Sin混沌, 自适应, 柯西变异, 反向学习

MAO Qinghua, ZHANG Qiang. Improved Sparrow Algorithm Combining Cauchy Mutation and Opposition-Based Learning[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(6): 1155-1164.

毛清华, 张强. 融合柯西变异和反向学习的改进麻雀算法[J]. 计算机科学与探索, 2021, 15(6): 1155-1164.

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