基于自适应 t分布与随机游走的麻雀搜索算法

doi:10.16180/j.cnki.issn1007-7820.2023.07.011

摘要/Abstract

摘要：

针对麻雀搜索算法在解决复杂问题时存在的收敛精度降低以及陷入局部最优等问题,文中提出了一种基于自适应t分布与随机游走的麻雀搜索算法。该算法在初始化过程中使用反向学习来生成反向解,从中选择优秀的个体组成初始化种群。在原始麻雀搜索算法上采用自适应t分布策略和高斯随机游走策略可以提高麻雀个体的寻优能力,同时防止算法早熟。仿真结果表明,相较于对比算法,文中所提算法的收敛精度和收敛速度都有所提升。

关键词: 麻雀搜索算法, 自适应t分布, 反向学习策略, 随机游走策略, 函数优化, 局部最优, 全局最优, 优化算法

Abstract:

In view of the problems of low convergence accuracy and falling into local optimum when solving complex problems, a sparrow search algorithm based on adaptive t-distribution and random walk is proposed in this study. In the initialization process, the algorithm uses reverse learning to generate reverse solutions from which excellent individuals are selected to form the initial population. In the original sparrow search algorithm, the adaptive t-distribution strategy and Gaussian random walk strategy are used to improve the optimization ability of the sparrow individuals, and can prevent the algorithm from premature. The simulation results show that the proposed algorithm improves the convergence accuracy and convergence speed when compared with the comparison algorithm.

Key words: sparrow search algorithm, adaptive t-distribution, opposition-based learning strategy, random walk strategy, function optimization, local optimum, global optimum, optimistic algorithm

中图分类号:

TP301.6

聂方鑫,王宇嘉. 基于自适应 t分布与随机游走的麻雀搜索算法[J]. 电子科技, 2023, 36(7): 75-80.

NIE Fangxin,WANG Yujia. Sparrow Search Algorithm Based on Adaptive t-Distribution and Random Walk[J]. Electronic Science and Technology, 2023, 36(7): 75-80.

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参考文献 19

[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]	Wang Y R, Ding S F, Wang L J, et al. A manifold p-spectral clustering with sparrow search algorithm[J]. Soft Computing, 2022, 26(4):1765-1777. doi: 10.1007/s00500-022-06741-5
[3]	Zhang Z, He R, Yang K. A bioinspired path planning approach for mobile robots based on improved sparrow search algorithm[J]. Advances in Manufacturing, 2022(10):1-17.
[4]	Abdulhammed O Y. Load balancing of IoT tasks in the cloud computing by using sparrow search algorithm[J]. The Journal of Supercomputing, 2021(2):1-27.
[5]	杨巍, 毛剑琳, 熊晔. 一种动态权值自适应粒子群优化典型函数问题[J]. 电子科技, 2018, 31(9):9-12.
	Yang Wei, Mao Jianlin, Xiong Ye. A dynamic weighted adaptive particle swarm optimization for typical functional problems[J]. Electronic Science and Technology, 2018, 31(9):9-12.
[6]	刘晨旻, 王亚刚. 基于连续空间的萤火虫算法改进[J]. 电子科技, 2022, 35(2):40-45.
	Liu Chenmin, Wang Yagang. Optimization of firefly algorithm based on continuous space[J]. Electronic Science and Technology, 2022, 35(2):40-45.
[7]	Ma J, Hao Z Y, Sun W J. Enhancing sparrow search algorithm via multi-strategies for continuous optimization problems[J]. Information Processing & Management, 2022, 59(2):1-12.
[8]	Zhang C L, Ding S F. A stochastic configuration network based on chaotic sparrow search algorithm[J]. Knowledge-Based Systems, 2021, 22(10):1-20. doi: 10.1016/j.knosys.2008.04.006
[9]	Zhu Y L, Yousefi N. Optimal parameter identification of PEMFC stacks using adaptive sparrow search algorithm[J]. International Journal of Hydrogen Energy, 2021, 46(14):9541-9552. doi: 10.1016/j.ijhydene.2020.12.107
[10]	张晓萌, 张艳珠, 刘禄, 等. 融合多策略的改进麻雀搜索算法[J]. 计算机应用研究, 2022, 39(4):1086-1091.
	Zhang Xiaomeng, Zhang Yanzhu, Liu Lu, et al. Improved sparrow search algorithm fused with multiple strategies[J]. Application Research of Computers, 2022, 39(4):1086-1091.
[11]	楚哲宇, 唐秀英, 谭庆, 等. 基于逐维高斯变异的混沌麻雀优化算法[J]. 自动化应用, 2021(8):60-63.
	Chu Zheyu, Tang Xiuying, Tan Qing, et al. Chaos sparrow optimization algorithm based on dimensional gaussian mutation[J]. Automation Application, 2021(8):60-63.
[12]	王正通, 程凤芹, 尤文, 等. 基于翻筋斗觅食策略的灰狼优化算法[J]. 计算机应用研究, 2021, 38(5):1434-1437.
	Wang Zhengtong, Cheng Fengqin, You Wen, et al. Grey wolf optimization algorithm based on somersault foraging strategy[J]. Application Research of Computers, 2021, 38(5):1434-1437.
[13]	薛建凯. 一种新型的群智能优化技术的研究与应用:麻雀搜索法[D]. 上海: 东华大学, 2020:1-50.
	Xue Jiankai. Research and application of a novel swarm intelligence optimization technique: sparrow search algorithm[D]. Shanghai: Donghua University, 2020:1-50.
[14]	Li M D, Xu G H, Lai Q, et al. A chaotic strategy-based quadratic opposition-based learning adaptive variable-speed whale optimization algorithm[J]. Mathematics and Computers in Simulation, 2022, 19(3):71-99.
[15]	韩斐斐, 刘升. 基于自适应t分布变异的缎蓝园丁鸟优化算法[J]. 微电子学与计算机, 2018, 35(8):117-121.
	Han Feifei, Liu Sheng. Adaptive satin bower bird optimization algorithm based on t distribution mutation[J]. Microelectronics & Computer, 2018, 35(8):117-121.
[16]	黎素涵, 叶春明. 重选精英个体的非线性收敛灰狼优化算法[J]. 计算机工程与应用, 2021, 57(1):62-68. doi: 10.3778/j.issn.1002-8331.2003-0121
	Li Suhan, Ye Chunming. Improved grey wolf optimizer algorithm using nonlinear convergence factor and elite re-election strategy[J]. Computer Engineering and Applications, 2021, 57(1):62-68. doi: 10.3778/j.issn.1002-8331.2003-0121
[17]	Rao R V, Savsani V J, Vakharia D P. Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems[J]. Computer-Aided Design, 2011, 43(3):303-315. doi: 10.1016/j.cad.2010.12.015
[18]	唐延强, 李成海, 宋亚飞, 等. 自适应变异麻雀搜索优化算法[J]. 北京航空航天大学学报, 2022, 48(6):1-6.
	Tang Yanqiang, Li Chenghai, Song Yafei, et al. Adaptive mutation sparrow search optimization algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(6):1-6.
[19]	汤安迪, 韩统, 徐登武, 等. 基于等级制度和布朗运动的混沌麻雀搜索算法[J]. 空军工程大学学报(自然科学版), 2021, 22(3):96-103.
	Tang Andi, Han Tong, Xu Dengwu, et al. A chaos sparrow search algorithm based on hierarchy and brownian motion[J]. Journal of Air Force Engineering University(Natural Science Edition), 2021, 22(3):96-103.

算法	参数设置
EGWO	a=2×((1-t/max_iter)^1/3),A=2ar₂-a,C=2r₁
TLBO	-
SSA	PD=20%,SD=10%,ST=0.8
AMSSA	PD=20%,SD=10%,ST=0.8,m=2
CSSAHB	PD=20%,SD=10%,ST=0.8,P=0.5
ARSSA	PD=20%,SD=10%,ST=0.8,η=1-i/iter_max

函数	性能指标	EGWO	TLBO	SSA	AMSSA	CSSAHB	ARSSA
F₁	平均值	8.62×10^-144	1.62×10^-167	2.21×10^-177	2.01×10^-317	0	0
F₁	标准差	2.66×10^-143	0	0	0	0	0
F₂	平均值	7.80×10^-83	3.96×10^-83	3.67×10^-80	7.53×10^-150	7.60×10^-173	3.94×10^-208
F₂	标准差	8.23×10^-83	2.48×10^-83	2.01×10^-79	4.12×10^-149	0	0
F₃	平均值	7.85×10^-45	3.23×10^-34	2.51×10^-119	0	0	0
F₃	标准差	1.72×10^-44	7.80×10^-34	1.27×10^-118	0	0	0
F₄	平均值	3.71×10^-39	3.96×10^-67	2.53×10^-87	1.94×10^-147	1.02×10^-142	1.44×10^-173
F₄	标准差	5.32×10^-39	2.20×10^-67	1.36×10^-86	1.07×10^-146	5.56×10^-142	0
F₅	平均值	2.66×10¹	8.32×10⁰	3.39×10^-6	2.79×10^-8	2.43×10^-8	1.69×10^-9
F₅	标准差	3.97×10^-1	1.58×10⁰	6.34×10^-6	7.28×10^-8	4.15×10^-8	3.00×10^-9
F₆	平均值	1.37×10^-1	4.98×10^-31	1.69×10^-17	4.45×10^-19	8.89×10^-20	2.54×10^-20
F₆	标准差	1.42×10^-1	3.08×10^-31	5.75×10^-17	7.24×10^-19	3.73×10^-20	1.31×10^-20
F₇	平均值	1.69×10^-5	3.99×10^-4	3.10×10^-4	4.30×10^-5	3.33×10^-5	5.80×10^-5
F₇	标准差	1.96×10^-5	1.25×10^-4	2.98×10^-4	7.64×10^-5	4.26×10^-5	4.25×10^-6
F₈	平均值	-3.36×10³	-8.48×10³	-8.47×10³	-1.01×10⁴	-1.08×10⁴	-1.11×10⁴
F₈	标准差	2.80×10²	8.02×10²	6.90×10²	2.60×10³	1.90×10³	1.71×10³
F₉	平均值	0	0	0	0	0	0
F₉	标准差	0	0	0	0	0	0
F₁₀	平均值	0	0	0	0	0	0
F₁₀	标准差	0	0	0	0	0	0

函数	性能指标	EGWO	TLBO	SSA	AMSSA	CSSAHB	ARSSA
F₁	平均值	4.59×10^-68	2.82×10^-151	8.18×10^-172	4.98×10^-270	6.09×10^-286	0
F₁	标准差	1.08×10^-67	2.02×10^-151	0	0	0	0
F₂	平均值	4.36×10^-41	1.09×10^-75	3.40×10^-77	8.11×10^-126	5.72×10^-117	6.20×10^-168
F₂	标准差	2.45×10^-41	5.46×10^-76	1.86×10^-76	4.44×10^-125	3.13×10^-116	0
F₃	平均值	1.01×10^-15	1.20×10^-13	1.61×10^-99	3.18×10^-202	9.44×10^-234	0
F₃	标准差	3.65×10^-15	4.19×10^-13	8.80×10^-99	0	0	0
F₄	平均值	2.12×10^-16	1.93×10^-59	3.36×10^-84	1.75×10^-133	8.95×10^-127	9.00×10^-139
F₄	标准差	1.69×10^-16	6.94×10^-60	1.84×10^-83	9.56×10^-133	4.90×10^-126	4.93×10^-138
F₅	平均值	9.69×10¹	8.85×10¹	1.13×10^-4	2.20×10^-6	6.28×10^-6	1.40×10^-7
F₅	标准差	5.67×10^-1	1.83×10⁰	3.24×10^-4	8.58×10^-6	8.98×10^-6	7.01×10^-7
F₆	平均值	1.07×10¹	2.17×10^-4	1.62×10^-7	8.69×10^-9	6.71×10^-10	4.97×10^-10
F₆	标准差	6.17×10^-1	1.00×10^-3	8.40×10^-7	7.11×10^-9	8.79×10^-9	7.90×10^-10
F₇	平均值	2.22×10^-5	5.75×10^-4	3.78×10^-4	4.42×10^-5	1.61×10^-5	3.21×10^-5
F₇	标准差	2.20×10^-5	1.86×10^-4	3.48×10^-4	7.56×10^-5	1.97×10^-5	3.42×10^-5
F₈	平均值	-5.77×10³	-2.16×10⁴	-2.57×10⁴	-3.91×10⁴	-3.95×10⁴	-4.05×10⁴
F₈	标准差	4.85×10²	4.88×10³	1.23×10³	3.05×10³	5.86×10³	2.82×10³
F₉	平均值	0	0	0	0	0	0
F₉	标准差	0	0	0	0	0	0
F₁₀	平均值	0	0	0	0	0	0
F₁₀	标准差	0	0	0	0	0	0