Enhanced sparrow search algorithm based on multiple improvement strategies

doi:10.11772/j.issn.1001-9081.2022081270

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (9): 2845-2854.DOI: 10.11772/j.issn.1001-9081.2022081270

• Advanced computing • Previous Articles Next Articles

Enhanced sparrow search algorithm based on multiple improvement strategies

Dahai LI, Meixin ZHAN(), Zhendong WANG

School of Information Engineering，Jiangxi University of Science and Technology，Ganzhou Jiangxi 341000，China

Received:2022-08-26 Revised:2022-10-23 Accepted:2022-11-03 Online:2023-01-11 Published:2023-09-10
Contact: Meixin ZHAN
About author:LI Dahai， born in 1975， Ph. D.， associate professor. His research interests include intelligent optimization algorithm， reinforcement learning.
WANG Zhendong， born in 1982， Ph. D.， associate professor. His research interests include wireless sensor network， intelligent optimization algorithm.
Supported by:
National Natural Science Foundation of China(620620237);Science Foundation of Jiangxi University of Science and Technology(205200100013)

基于多个改进策略的增强麻雀搜索算法

李大海, 詹美欣(), 王振东

江西理工大学信息工程学院，江西赣州 341000

通讯作者: 詹美欣
作者简介:李大海（1975—），男，山东乳山人，副教授，博士，CCF会员，主要研究方向：智能优化算法、强化学习
王振东（1982－），男，湖北随州人，副教授，博士，主要研究方向：无线传感器网络、智能优化算法。
基金资助:
国家自然科学基金资助项目(620620237);江西理工大学校级基金资助项目(205200100013)

Abstract

Abstract:

Aiming at the drawbacks that Sparrow Search Algorithm （SSA） has relatively low search accuracy and is easy to fall into the local optimum， an Enhanced Sparrow Search Algorithm based on Multiple Improvement strategies （EMISSA） was proposed. Firstly， in order to balance the global search and local search abilities of the algorithm， fuzzy logic was introduced to adjust the scale of sparrow finders dynamically. Secondly， the mixed differential mutation operation was performed on sparrow followers to generate mutation subgroups， thereby enhancing the ability of EMISSA to jump out of the local optimum. Finally， Topological Opposition-Based Learning （TOBL） was used to obtain topological opposition solutions of sparrow finders， thereby fully mining high-quality position information in the search space. EMISSA， standard SSA and Chaotic Sparrow Search Optimization Algorithm （CSSOA） were evaluated by 12 test functions in 2013 Congress on Evolutionary Computation （CEC2013）. Experimental results show that EMISSA achieves 11 first places on 12 test functions in the 30-dimensional case； in the 80-dimensional case， the proposed algorithm has the optimal results on all the test functions. In the Friedman test， EMISSA ranks first on all the test functions. Experimental results of applying EMISSA to the Wireless Sensor Network （WSN） node deployment in obstacle environment show that compared with other algorithms， EMISSA achieves the highest wireless node coverage with more uniform node distribution and less coverage redundancy.

Key words: Sparrow Search Algorithm (SSA), fuzzy logic, mixed differential mutation operation, Topological Opposition-Based Learning (TOBL), Wireless Sensor Network (WSN), node deployment

摘要：

针对麻雀搜索算法（SSA）存在寻优精度不高且易陷入局部最优的问题，提出一种基于多个改进策略的增强麻雀搜索算法（EMISSA）。首先，为平衡算法的全局和局部搜索能力，引入模糊逻辑来动态调整麻雀发现者的规模；其次，对麻雀跟随者进行混合差分变异操作以产生变异子群，从而增强EMISSA跳出局部最优的能力；最后，通过拓扑对立学习（TOBL）产生当前麻雀发现者个体的拓扑对立解，以充分挖掘搜索空间内的优质位置信息。通过2013年进化计算大会（CEC2013）中的12个测试函数评估EMISSA、标准SSA以及混沌麻雀搜索优化算法（CSSOA）等改进麻雀算法的性能。实验结果表明，EMISSA在30维情况下，在12个测试函数上获得了11个第一；在80维情况下，在所有的测试函数上都获得了第一。而在Friedman检验中，EMISSA的排名均获得了第一。将EMISSA应用于障碍物环境下的无线传感器网络（WSN）节点部署，实验结果表明，相较于其他算法，EMISSA获得了最高的无线节点覆盖率，节点分布更均匀，覆盖冗余更少。

关键词: 麻雀搜索算法, 模糊逻辑, 混合差分变异操作, 拓扑对立学习, 无线传感器网络, 节点部署

CLC Number:

TP301.6

Dahai LI, Meixin ZHAN, Zhendong WANG. Enhanced sparrow search algorithm based on multiple improvement strategies[J]. Journal of Computer Applications, 2023, 43(9): 2845-2854.

李大海, 詹美欣, 王振东. 基于多个改进策略的增强麻雀搜索算法[J]. 《计算机应用》唯一官方网站, 2023, 43(9): 2845-2854.

Figures/Tables 17

References 31

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规则序号	模糊规则
1	If （iteration is Early） and （diversity is Low） then （PD is Very_Big）（1）
2	If （iteration is Early） and （diversity is Medium） then （PD is Big）（1）
3	If （iteration is Early） and （diversity is High） then （PD is Medium）（1）
4	If （iteration is Medium） and （diversity is Low） then （PD is Big）（1）
5	If （iteration is Medium） and （diversity is Medium） then （PD is Medium）（1）
6	If （iteration is Medium） and （diversity is High） then （PD is Small）（1）
7	If （iteration is Late） and （diversity is Low） then （PD is Medium）（1）
8	If （iteration is Late） and （diversity is Medium） then （PD is Small）（1）
9	If （iteration is Late） and （diversity is High） then （PD is Very_Small）（1）

规则序号	模糊规则
1	If （iteration is Early） and （diversity is Low） then （PD is Very_Big）（1）
2	If （iteration is Early） and （diversity is Medium） then （PD is Big）（1）
3	If （iteration is Early） and （diversity is High） then （PD is Medium）（1）
4	If （iteration is Medium） and （diversity is Low） then （PD is Big）（1）
5	If （iteration is Medium） and （diversity is Medium） then （PD is Medium）（1）
6	If （iteration is Medium） and （diversity is High） then （PD is Small）（1）
7	If （iteration is Late） and （diversity is Low） then （PD is Medium）（1）
8	If （iteration is Late） and （diversity is Medium） then （PD is Small）（1）
9	If （iteration is Late） and （diversity is High） then （PD is Very_Small）（1）

函数	函数名	F^*
f₁	Sphere Function	-1 400
f₂	Rotated Bent Cigar Function	-1 200
f₃	Rotated Discus Function	-1 100
f₄	Different Powers Function	-1 000
f₅	Rotated Rosenbrock’s Function	-900
f₆	Rotated Ackley’s Function	-700
f₇	Rotated Rastrigin’s function	-300
f₈	Non-Continuous RotatedRastrigin’s Function	-200
f₉	Expanded Scaffer’s F6 Function	600
f₁₀	Composition Function 3	900
f₁₁	Composition Function 6	1 200
f₁₂	Composition Function 8	1 400

函数	函数名	F^*
f₁	Sphere Function	-1 400
f₂	Rotated Bent Cigar Function	-1 200
f₃	Rotated Discus Function	-1 100
f₄	Different Powers Function	-1 000
f₅	Rotated Rosenbrock’s Function	-900
f₆	Rotated Ackley’s Function	-700
f₇	Rotated Rastrigin’s function	-300
f₈	Non-Continuous RotatedRastrigin’s Function	-200
f₉	Expanded Scaffer’s F6 Function	600
f₁₀	Composition Function 3	900
f₁₁	Composition Function 6	1 200
f₁₂	Composition Function 8	1 400

函数	指标	EMISSA	SSA	CSSOA	MSSSA	ISSA	ESSA
f₁	Mean	9.73E+00	2.23E+04	1.86E+04	4.80E+01	1.01E+01	6.89E+04
	Std	5.07E+00	6.38E+03	5.81E+03	2.38E+01	1.28E+01	7.21E+02
	Rank	1	5	4	3	2	6
f₂	Mean	1.55E+10	2.13E+15	6.09E+13	2.55E+10	1.75E+10	1.62E+21
	Std	5.38E+09	7.42E+15	1.06E+14	1.86E+10	9.28E+09	4.30E+21
	Rank	1	5	4	3	2	6
f₃	Mean	3.39E+04	6.04E+04	5.54E+04	6.00E+04	4.53E+04	6.94E+04
	Std	6.35E+03	5.52E+03	6.17E+03	4.71E+03	7.64E+03	5.84E+02
	Rank	1	5	3	4	2	6
f₄	Mean	4.82E+01	6.90E+03	4.42E+03	1.69E+02	1.25E+02	3.91E+04
	Std	2.84E+01	2.97E+03	1.15E+03	3.54E+01	4.45E+01	1.81E+04
	Rank	1	5	4	3	2	6
f₅	Mean	1.36E+02	2.45E+03	2.11E+03	1.43E+02	1.67E+02	1.95E+04
	Std	2.86E+01	8.61E+02	1.32E+03	3.84E+01	4.47E+01	4.65E+03
	Rank	1	5	4	2	3	6
f₆	Mean	2.10E+01	2.10E+01	2.10E+01	2.10E+01	2.10E+01	2.10E+01
	Std	3.86E-02	6.05E-02	5.20E-02	5.95E-02	4.70E-02	7.81E-02
	Rank	1	5	3	4	2	6
f₇	Mean	4.35E+02	8.52E+02	5.08E+02	8.97E+02	8.76E+02	1.13E+03
	Std	1.06E+02	1.38E+02	1.10E+02	1.05E+02	1.01E+02	3.38E+01
	Rank	1	3	2	5	4	6
f₈	Mean	5.02E+02	1.00E+03	6.37E+02	8.79E+02	9.13E+02	1.08E+03
	Std	7.10E+01	1.16E+02	9.24E+01	1.37E+02	1.56E+02	6.85E+01
	Rank	1	5	2	3	4	6
f₉	Mean	1.44E+01	1.50E+01	1.40E+01	1.50E+01	1.50E+01	1.50E+01
	Std	5.09E-01	9.15E-02	5.56E-01	8.34E-02	0.00E+00	4.79E-09
	Rank	2	6	1	5	3	4
f₁₀	Mean	6.35E+03	6.85E+03	6.60E+03	7.00E+03	7.23E+03	9.43E+03
	Std	9.26E+02	8.62E+02	8.13E+02	8.44E+02	5.60E+02	4.10E+02
	Rank	1	3	2	4	5	6
f₁₁	Mean	2.43E+02	4.05E+02	3.44E+02	4.08E+02	4.33E+02	4.57E+02
	Std	7.51E+01	6.63E+01	9.58E+01	9.70E+00	1.63E+02	4.66E+01
	Rank	1	34	2	4	5	6
f₁₂	Mean	4.40E+03	7.15E+03	4.49E+03	6.09E+03	5.71E+03	8.20E+03
	Std	4.63E+02	1.31E+03	7.24E+02	9.06E+02	4.14E+02	1.10E+03
	Rank	1	5	2	4	3	6
Count		11	0	1	0	0	0
Ave Rank		1.29	4.33	2.79	3.58	3.33	5.67
Total Rank		1	5	2	4	3	6

Enhanced sparrow search algorithm based on multiple improvement strategies

基于多个改进策略的增强麻雀搜索算法

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Figures/Tables 17

References 31

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Metrics

函数	指标	EMISSA	SSA	CSSOA	MSSSA	ISSA	ESSA
f₁	Mean	2.05E+03	1.15E+05	1.15E+05	2.18E+04	5.78E+03	1.54E+05
	Std	4.03E+02	8.18E+03	8.76E+03	6.00E+03	3.35E+03	4.55E+02
	Rank	1	4	5	3	2	6
f₂	Mean	7.76E+10	8.40E+20	3.19E+18	2.95E+13	1.18E+12	1.38E+24
	Std	3.19E+10	2.11E+21	6.43E+18	7.18E+13	2.60E+12	8.43E+23
	Rank	1	5	4	3	2	6
f₃	Mean	1.03E+05	2.07E+05	1.64E+05	3.44E+05	1.06E+05	2.02E+05
	Std	1.46E+04	1.62E+04	1.52E+04	5.52E+04	3.87E+04	3.18E+04
	Rank	1	5	3	6	2	4
f₄	Mean	5.60E+02	3.63E+04	2.29E+04	2.74E+03	8.72E+02	8.47E+04
	Std	2.04E+02	9.84E+03	2.97E+03	7.76E+02	2.73E+02	2.86E+03
	Rank	1	5	4	3	2	6
f₅	Mean	6.12E+02	2.45E+04	1.99E+04	2.43E+03	6.67E+02	4.48E+04
	Std	1.11E+02	3.17E+03	2.81E+03	7.45E+02	1.28E+02	3.64E+02
	Rank	1	5	4	3	2	6
f₆	Mean	2.10E+01	2.12E+01	2.12E+01	2.13E+01	2.12E+01	2.12E+01
	Std	7.09E-02	3.21E-02	2.67E-02	3.53E-02	3.98E-02	4.80E-02
	Rank	1	3	2	6	4	5
f₇	Mean	1.54E+03	2.33E+03	1.92E+03	2.11E+03	1.80E+03	2.62E+03
	Std	5.30E+01	8.01E+01	1.36E+02	6.61E+01	9.57E+01	1.71E+01
	Rank	1	5	3	4	2	6
f₈	Mean	2.05E+03	2.54E+03	2.20E+03	2.39E+03	2.10E+03	2.63E+03
	Std	1.58E+02	9.75E+01	1.85E+02	9.58E+01	1.49E+02	9.33E+01
	Rank	1	5	3	4	2	6
f₉	Mean	3.81E+01	3.89E+01	3.83E+01	3.86E+01	3.86E+01	3.92E+01
	Std	6.14E-01	3.70E-01	2.22E-01	3.60E-01	3.68E-01	1.85E-01
	Rank	1	5	2	3	4	6
f₁₀	Mean	2.04E+04	2.35E+04	2.33E+04	2.30E+04	2.54E+04	2.88E+04
	Std	1.01E+03	1.68E+03	2.02E+03	2.56E+03	1.91E+03	1.13E+03
	Rank	1	4	3	2	5	6
f₁₁	Mean	2.25E+02	9.49E+02	6.52E+02	6.54E+02	5.77E+02	2.34E+03
	Std	8.38E+01	9.32E+02	1.97E+01	2.24E+01	1.58E+02	1.38E+03
	Rank	1	5	3	4	2	6
f₁₂	Mean	1.36E+04	2.30E+04	2.00E+04	2.21E+04	1.78E+04	2.36E+04
	Std	8.69E+02	1.71E+03	1.44E+03	2.25E+03	1.62E+03	1.40E+03
	Rank	1	5	3	4	2	6
Count		12	0	0	0	0	0
Ave Rank		1.00	4.75	3.33	3.79	2.50	5.63
Total Rank		1	5	3	4	2	6

实验参数	参数设置
部署区域	100 m×100 m二维平面
区域大小/m²	9 000
节点数量	28
感知半径/m	12
障碍物类型	三角形、菱形
障碍物数量	5
种群规模	30
最大迭代次数	1 500

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