结合粒子群和单纯形的改进飞蛾优化算法

doi:10.13229/j.cnki.jdxbgxb20170066

Abstract

Abstract:

In order to improve the ability of moth optimization algorithm in solving practical problems, a novel hybrid moth optimization algorithm based on single storage is proposed. The algorithm uses particle swarm optimization to obtain the initial optimal location of the moths, so that it has a better and richer population. The optimal solution is also obtained by the simplex method. The proposed algorithm is compared with other four kinds of algorithms by tests on ten functions. Experimental results show that the convergence speed and solution quality of the proposed algorithm are better than other algorithms, it has better solving and optimizing performance, and can be used as an effective optimization tool.

Key words: computer application, simplex method, moth-flame optimization method, particle swarm optimization(PSO), function optimization

CLC Number:

TP393

ZHAO Dong,SUN Ming-yu,ZHU Jin-long,YU Fan-hua,LIU Guang-jie,CHEN Hui-ling. Improved moth-flame optimization method based on combination of particle swarm optimization and simplex method[J].Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1867-1872.

Figures/Tables 7

Fig.1

Table 1

Benchmark functions"

函数	参数取值	最小值
$f 1 x = ∑ i = 1 n x i 2$	[-100,100]	0
$f 2 (x) = ∑ i = 1 n (∑ j = 1 i x j) 2$	[-100,100]	0
$f 3 (x) = ∑ i = 1 n ([x i + 0.5]) 2$	[-100,100]	0
$f 4 (x) = ∑ i = 1 n [x i 2 - 10 cos (2 π x i) + 10]$	[-5.12,5.12]	0
$f 5 (x) = - 20 exp - 0.2 1 n ∑ i = 1 n x i 2 - exp 1 n ∑ i = 1 n cos 2 π x i + 20 + e$	[-32,32]	0
$f 6 (x) = 14000 ∑ i = 1 n x i 2 - ∏ i = 1 n cos x i i + 1$	[-600,600]	0
$f 7 (x) = - ∑ i = 1 4 c i exp - ∑ j = 1 6 a ij x j - p ij 2$	[0,1]	-3.32
$f 8 (x) = - ∑ i = 1 5 X - a i X - a i T + c i - 1$	[0,10]	-10.1532
$f 9 (x) = - ∑ i = 1 7 X - a i X - a i T + c i - 1$	[0,10]	-10.4028
$f 10 (x) = - ∑ i = 1 10 X - a i X - a i T + c i - 1$	[0,10]	-10.5363

Table 1

Table 2

Fig.2

Fig.3

Fig.4

Fig.5

References 19

[1]	Mirjalili S . Moth-flame optimization algorithm:a novel nature-inspired heuristic paradigm[J]. Knowledge-Based Systems, 2015,89:228-249. doi: 10.1016/j.knosys.2015.07.006
[2]	Allam D, Yousri D A, Eteiba M B . Parameters extraction of the three diode model for the multi-crystalline solar cell/module using moth-flame optimization algorithm[J]. Energy Conversion & Management, 2016,123:535-548. doi: 10.1016/j.enconman.2016.06.052
[3]	Li Cun-bin, Li Shu-ke, Liu Yun-qi . A least squares support vector machine model optimized by moth-flame optimization algorithm for annual power load forecasting[J]. Applied Intelligence, 2016,45(4):1166-1178. doi: 10.1007/s10489-016-0810-2
[4]	Zhao Hui-ru, Zhao Hao-ran, Guo Sen . Using GM (1,1) optimized by MFO with rolling mechanism to forecast the electricity consumption of inner mongolia[J]. Applied Sciences, 2016,6(1):1-18.
[5]	Aziz M A E, Ewees A A, Hassanien A E . Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation[J]. Expert Systems with Applications, 2017,83:242-256. doi: 10.1016/j.eswa.2017.04.023
[6]	Mei R N S, Sulaiman M H, Mustaffa Z , et al. Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique[J]. Applied Soft Computing, 2017,59:210-222. doi: 10.1016/j.asoc.2017.05.057
[7]	Li C Y, Hou L X, Sharma B Y , et al. Developing a new intelligent system for the diagnosis of tuberculous pleural effusion[J]. Comput Methods Programs Biomed, 2018,153:211-225. doi: 10.1016/j.cmpb.2017.10.022 pmid: 29157454
[8]	Li Zhi-ming, Zhou Yong-quan, Zhang Sen , et al. Lévy-flight moth-flame algorithm for function optimization and engineering design problems[J]. Mathematical Problems in Engineering, 2016,2016:1423930.
[9]	Khalilpourazari S, Khalilpourazary S . An efficient hybrid algorithm based on water cycle and moth-flame optimization algorithms for solving numerical and constrained engineering optimization problems[J/OL].[2017-10-28].
[10]	Reddy M P K, Babu M R . A hybrid cluster head selection model for Internet of Things[J]. Cluster Computing, 2017(1):1-13. doi: 10.1007/s10586-017-1261-1
[11]	Wang Ming-jing, Chen Hui-ling, Yang Bo , et al. Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses[J]. Neurocomputing, 2017,267:69-84. doi: 10.1016/j.neucom.2017.04.060
[12]	Kazimipour B, Li X D, Qin K . A review of population initialization techniques for evolutionary algorithms [C]//2014 IEEE Congress on Evolutionary Computation,Beijing, China, 2014: 2585-2592.
[13]	Ma Z K, Vandenbosch G A E . Impact of Random Number Generators on the performance of particle swarm optimization in antenna design [C]//6th European Conference on Antennas and Propagation, Prague, Czech Republic, 2012: 925-929.
[14]	黄璇, 郭立红, 李姜 , 等. 改进粒子群优化BP神经网络的目标威胁估计[J]. 吉林大学学报:工学版, 2017,47(3):996-1002. doi: 10.13229/j.cnki.jdxbgxb201703042
	Huang Xuan, Guo Li-hong, Li Jiang , et al. Target threat assessment based on BP neural network optimized by modified particle swarm optimization[J]. Journal of Jilin University(Engineering and Technology Edition), 2017,47(3):996-1002. doi: 10.13229/j.cnki.jdxbgxb201703042
[15]	Dai Jing-yi, Wang Shao-wei . Clustering-based interference management in densely deployed femtocell networks[J]. Digital Communications and Networks, 2016(4):175-183. doi: 10.1016/j.dcan.2016.10.002
[16]	何莉, 王淼, 李博 . 面向单目标优化的集成粒子群算法[J]. 重庆邮电大学学报:自然科学版, 2017,29(4):527-534.
	He Li, Wang Miao, Li Bo . Ensemble particle swarm optimizer for single objective optimizationl[J]. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 2017,29(4):527-534.
[17]	Hamid N F A, Rahim N A, Selvaraj J . Solar cell parameters identification using hybrid Nelder-Mead and modified particle swarm optimization[J]. Journal of Renewable & Sustainable Energy, 2016,8(1):78-81. doi: 10.1063/1.4941791
[18]	Zhou Yong-quan, Zhou Yu-xiang, Luo Qi-fang , et al. A simplex method-based social spider optimization algorithm for clustering analysis[J]. Engineering Applications of Artificial Intelligence, 2017,64:67-82. doi: 10.1016/j.engappai.2017.06.004
[19]	戴月明, 朱达祥, 吴定会 . 核矩阵协同进化的震荡搜索粒子群优化算法[J]. 重庆邮电大学学报自然科学版, 2016,28(2):247-253. doi: 10.3979/j.issn.1673-825X.2016.02.017
	Dai Yue-ming, Zhu Da-xiang, Wu Ding-hui . Shock search particle swarm optimization algorithm based on kernel matrix synergistic evolution[J]. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 2016,28(2):247-253. doi: 10.3979/j.issn.1673-825X.2016.02.017

Related Articles 15

[1]	Nan WANG,Jin⁃bao LI,Yong LIU,Yu⁃jie ZHANG,Ying⁃li ZHONG. TPR⁃TF： time⁃aware point of interest recommendation model based on tensor factorization [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(3): 920-933.
[2]	LIU Fu,ZONG Yu-xuan,KANG Bing,ZHANG Yi-meng,LIN Cai-xia,ZHAO Hong-wei. Dorsal hand vein recognition system based on optimized texture features [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1844-1850.
[3]	WANG Li-min,LIU Yang,SUN Ming-hui,LI Mei-hui. Ensemble of unrestricted K-dependence Bayesian classifiers based on Markov blanket [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1851-1858.
[4]	JIN Shun-fu,WANG Bao-shuai,HAO Shan-shan,JIA Xiao-guang,HUO Zhan-qiang. Synchronous sleeping based energy saving strategy of reservation virtual machines in cloud data centers and its performance research [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1859-1866.
[5]	LIU En-ze,WU Wen-fu. Agricultural surface multiple feature decision fusion disease judgment algorithm based on machine vision [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1873-1878.
[6]	OUYANG Dan-tong, FAN Qi. Clause-level context-aware open information extraction [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1563-1570.
[7]	LIU Fu, LAN Xu-teng, HOU Tao, KANG Bing, LIU Yun, LIN Cai-xia. Metagenomic clustering method based on k-mer frequency optimization [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1593-1599.
[8]	GUI Chun, HUANG Wang-xing. Network clustering method based on improved label propagation algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1600-1605.
[9]	LIU Yuan-ning, LIU Shuai, ZHU Xiao-dong, CHEN Yi-hao, ZHENG Shao-ge, SHEN Chun-zhuang. LOG operator and adaptive optimization Gabor filtering for iris recognition [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1606-1613.
[10]	CHE Xiang-jiu, WANG Li, GUO Xiao-xin. Improved boundary detection based on multi-scale cues fusion [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1621-1628.
[11]	ZANG Guo-shuai, SUN Li-jun. Method based on inertial point for setting depth to rigid layer [J]. 吉林大学学报(工学版), 2018, 48(4): 1037-1044.
[12]	ZHAO Hong-wei, LIU Yu-qi, DONG Li-yan, WANG Yu, LIU Pei. Dynamic route optimization algorithm based on hybrid in ITS [J]. 吉林大学学报(工学版), 2018, 48(4): 1214-1223.
[13]	HUANG Hui, FENG Xi-an, WEI Yan, XU Chi, CHEN Hui-ling. An intelligent system based on enhanced kernel extreme learning machine for choosing the second major [J]. 吉林大学学报(工学版), 2018, 48(4): 1224-1230.
[14]	FU Wen-bo, ZHANG Jie, CHEN Yong-le. Network topology discovery algorithm against routing spoofing attack in Internet of things [J]. 吉林大学学报(工学版), 2018, 48(4): 1231-1236.
[15]	CAO Jie, SU Zhe, LI Xiao-xu. Image annotation method based on Corr-LDA model [J]. 吉林大学学报(工学版), 2018, 48(4): 1237-1243.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 10

[1]	LI Shoutao, LI Yuanchun. Autonomous Mobile Robot Control Algorithm Based on Hierarchical Fuzzy Behaviors in Unknown Environments[J]. 吉林大学学报(工学版), 2005, 35(04): 391 -397 .
[2]	Nie Jian-jun，Du Fa-rong，Gao Feng . Finite time thermodynamics of real combined power cycle operating between internal combustion engine and Stirling engine with heat leak[J]. 吉林大学学报(工学版), 2007, 37(03): 518 -0523 .
[3]	Li Xue-xia, Feng Jiu-chao . Non-coherent detection scheme for chaotic digital communications based on inverse iteration[J]. 吉林大学学报(工学版), 2007, 37(01): 202 -205 .
[4]	Bao Tie，Liu Shu-fen . Network fault management formal description based on Communication Sequential Processes （CSP）[J]. 吉林大学学报(工学版), 2007, 37(01): 117 -120 .
[5]	. [J]. 吉林大学学报(工学版), 2007, 37(06): 1284 -1287 .
[6]	Yang Qing-fang, Chen Lin . Division approach of traffic control work zone[J]. 吉林大学学报(工学版), 2006, 36(增刊2): 139 -142 .
[7]	Yu Hua-nan， Kang Jian . An improved blind multiuser detection scheme based on Kalman filter[J]. 吉林大学学报(工学版), 2006, 36(增刊2): 122 -125 .
[8]	Liang Ji-cai, Li Yi, Li Zhong-ran, Zhang Wei, Liu Cheng-de . Numerical simulation of filling process in resin transfer molding for automobile bumper[J]. 吉林大学学报(工学版), 2006, 36(增刊2): 15 -19 .
[9]	Zhao Chun-ming,Wu Zhi-xin,Ma Ning,Li Jun . On-line monitoring of high voltage system parameters in electric vehicle[J]. 吉林大学学报(工学版), 2007, 37(01): 37 -42 .
[10]	Shen Chuan-liang，Liu Guo-jun，Dong Jing-shi，Yang Zhi-gang，Cheng Guang-ming . Piezoelectric multi-vibrator and single-chamber pump for precise drug delivery[J]. 吉林大学学报(工学版), 2007, 37(01): 89 -94 .

函数	评估指标	ASMFO算法	MFO算法	BA算法	DA算法	PSO算法
f₁	mean	6.61587×10^-7	1333.333611	14.41469608	1310.364321	128.8093818
	std	1.06073×10^-6	3457.459063	1.950027759	702.372311	16.73078547
	rank	1	5	2	4	3
f₂	mean	11.13415206	16890.85645	66.99110732	12941.56989	424.1775374
	std	5.154097921	11761.33616	15.65998979	9035.062058	91.97276795
	rank	1	5	2	4	3
f₃	mean	3.33979×10^-7	336.6754613	14.13230939	1206.616187	130.4472998
	std	3.80072×10^-7	1844.044846	2.021082608	556.6614262	13.69394367
	rank	1	4	2	5	3
f₄	mean	123.6179	161.0097	269.4918	159.1627	371.1542
	std	34.70155	29.88465	21.51611	35.78036	21.12408
	rank	1	3	4	2	5
f₅	mean	0.949335	13.62444	4.415123	9.1096	8.560911
	std	0.987226	7.940168	0.22009	1.792495	0.273829
	rank	1	5	2	4	3
f₆	mean	0.012629	15.08013	0.57143	12.63523	1.029492
	std	0.016012	34.26064	0.055542	6.425597	0.006695
	rank	1	5	2	4	3
f₇	mean	-3.28199	-3.211	-2.92727	-3.24876	-2.91484
	std	0.065413	0.062542	0.139138	0.095367	0.167127
	rank	1	3	4	2	5
f₈	mean	-8.9743	-5.47333	-6.01592	-7.66817	-3.76938
	std	2.173469	3.262726	2.988532	2.663623	1.264555
	rank	1	4	3	2	5
f₉	mean	-9.69972	-8.12273	-5.70277	-7.75656	-4.61679
	std	1.823505	3.337846	3.16243	3.087822	1.289141
	rank	1	2	4	3	5
f₁₀	mean	-10.3561	-8.3255	-5.95764	-7.71155	-4.64473
	std	0.987348	3.481528	3.131923	2.864567	1.056005
	rank	1	2	4	3	5
排名和		10	38	27	33	40
总体排名		1	4	2	3	5

Improved moth-flame optimization method based on combination of particle swarm optimization and simplex method

RICH HTML

PDF (PC)