融合柯西变异和反向学习的改进麻雀算法

doi:10.3778/j.issn.1673-9418.2010032

摘要/Abstract

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

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

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.

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.

参考文献

[1] XUE J K, SHEN B. A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems Science & Control Engineering, 2020, 8(1): 22-34.
[2] OLIVA D, AZIZ M A E, HASSANIEN A E. Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm[J]. Applied Energy, 2017, 200: 141-154.
[3] YANG W L, ZHOU X T, CHEN M N. New chaotic simplified particle swarm optimization algorithm based on logistic mapping[J]. Computer and Modernization, 2019(12): 15-20.
杨万里, 周雪婷, 陈孟娜. 基于Logistic映射的新型混沌简化PSO算法[J]. 计算机与现代化, 2019(12): 15-20.
[4] HEGAZY A E, MAKHLOUF M A, EL-TAWEL S G. Im-proved salp swarm algorithm for feature selection[J]. Journal of King Saud University-Computer and Information Sciences, 2020, 32(3): 335-344.
[5] WANG X W, WANG W, WANG Y. An adaptive bat algorithm [C]//LNCS 7996: Proceedings of the 9th International Con-ference on Intelligent Computing Theories and Technology, Nanning, Jul 28-31, 2013. Berlin, Heidelberg: Springer, 2013: 216-223.
[6] WANG Y R, ZHANG D M, FAN Y. A mutually beneficial adaptive satin bowerbird optimization algorithm based on non-uniform mutation[J]. Computer Engineering & Science, 2020, 42(12): 2233-2241.
王依柔, 张达敏, 樊英. 非均匀变异的互利自适应缎蓝园丁鸟优化算法[J]. 计算机工程与科学, 2020, 42(12): 2233-2241.
[7] WANG W H, XU L, CHAU K W, et al. Yin-Yang firefly algorithm based on dimensionally Cauchy mutation[J]. Expert Systems with Applications, 2020, 150: 113216.
[8] LI J, LUO Y K, WANG C, et al. Simplified particle swarm algorithm based on nonlinear decrease extreme disturbance and Cauchy mutation[J]. International Journal of Parallel, Emergent and Distributed Systems, 2020, 35(3): 236-245.
[9] WEN G L, GE H M, LI H P, et al. A multi-objective particle swarm optimization algorithm based on reverse learning[J]. Electronics Science Technology and Application, 2020, 7(2).
[10] XU Y T, CHEN H L, LUO J, et al. Enhanced Moth-flame optimizer with mutation strategy for global optimization[J]. Information Sciences, 2019, 492: 181-203.
[11] PAPPULA L, GHOSH D. Synthesis of linear aperiodic array using Cauchy mutated cat swarm optimization[J]. AEU- International Journal of Electronics and Communications, 2017, 72: 52-64.
[12] YANG H D, E J Q. An adaptive chaos immune optimiza-tion algorithm with mutative scale and its application[J]. Control Theory & Applications, 2009, 26(10): 1069-1074.
杨海东, 鄂加强. 自适应变尺度混沌免疫优化算法及其应用[J]. 控制理论与应用, 2009, 26(10): 1069-1074.
[13] LIU J S, YUAN M M, ZUO F. Global search-oriented adap-tive leader salp swarm algorithm[J/OL]. Control and Decision[2021-04-09]. https://doi.org/10.13195/j.kzyjc.2020.0090.
刘景森, 袁蒙蒙, 左方. 面向全局搜索的自适应领导者樽海鞘群算法[J/OL]. 控制与决策[2021-04-09]. https://doi.org/10.13195/j.kzyjc.2020.0090.
[14] HE Q, LIN J, XU H. Hybrid Cauchy mutation and uniform distribution of grasshopper optimization algorithm[J/OL]. Control and Decision [2021-04-09]. https://doi.org/10.13195/j.kzyjc.2019.1609.
何庆, 林杰, 徐航. 混合柯西变异和均匀分布的蝗虫优化算法[J/OL]. 控制与决策[2021-04-09]. https://doi.org/10. 13195/j.kzyjc.2019.1609.
[15] MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61.
[16] MIRJALILI S. Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm[J]. Knowledge-Based Sys-tems, 2015, 89: 228-249.
[17] LV X, MU X D, ZHANG J, et al. Chaos sparrow search optimization algorithm[J/OL]. Journal of Beijing University of Aeronautics and Astronautics [2020-09-11]. https://doi.org/ 10.13700/j.bh.1001-5965.2020.0298.
吕鑫, 慕晓冬, 张钧, 等. 混沌麻雀搜索优化算法[J/OL]. 北京航空航天大学学报[2020-09-11]. https://doi.org/10. 13700/j.bh.1001-5965.2020.0298.