Please wait a minute...
浙江大学学报(工学版)  2022, Vol. 56 Issue (12): 2426-2435    DOI: 10.3785/j.issn.1008-973X.2022.12.011
计算机技术     
基于种群多样性的自适应乌鸦搜索算法
何杰光1(),彭志平2,3,崔得龙1,李启锐1
1. 广东石油化工学院 计算机学院,广东 茂名 525000
2. 江门职业技术学院 信息工程学院,广东 江门 529030
3. 广东石油化工学院 广东省石化装备故障诊断重点实验室,广东 茂名 525000
Adaptive crow search algorithm based on population diversity
Jie-guang HE1(),Zhi-ping PENG2,3,De-long CUI1,Qi-rui LI1
1. College of Computer Science, Guangdong University of Petrochemical Technology, Maoming 525000, China
2. School of Information Engineering, Jiangmen Polytechnic, Jiangmen 529030, China
3. Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming 525000, China
 全文: PDF(1175 KB)   HTML
摘要:

针对原始乌鸦搜索算法对种群多样性控制不强、个体位置更新方式单一、局部搜索精细度不高等缺点,提出新的自适应乌鸦搜索算法.设计多种搜索引导个体,基于进化不同阶段的种群多样性,实现搜索引导个体的自适应选择策略,使算法在迭代前期加强全局勘探,在迭代后期强化局部开发. 结合正余弦搜索理念,构建基于线性递减、混合正余弦震荡递减的多种飞行长度控制参数及相应的多种搜索方式,提升算法的搜索遍历性,增加算法在迭代后期找到更优解的概率. 为了验证新算法的有效性,通过标准测试函数,将新算法与原始乌鸦搜索算法、改进乌鸦搜索算法和其他优秀的智能优化算法进行仿真实验,比较分析各算法的收敛精度、收敛速度、稳定性、Wilcoxon符号秩检验和Friedman检验. 实验结果表明,新算法的性能优于其他比较算法的性能,新算法实现了全局勘探和局部开发、收敛精度和收敛速度的平衡.

关键词: 群智能优化乌鸦搜索算法种群多样性搜索引导个体自适应选择正余弦搜索    
Abstract:

A new adaptive crow search algorithm was proposed to solve the shortcomings of the original crow search algorithm, such as weak control of population diversity, single updating of individual position and low precision of local search. Firstly, multiple search-guided individuals were designed, and an adaptive selection strategy of search-guided individuals was realized based on population diversity at different stages of evolution. The global exploration in the early iteration and local exploitation in the late iteration were achieved using the strategy. Secondly, by combining the idea of sine-cosine search, several flight length control parameters based on linear decline or mixed sine-cosine oscillation decline were used to constructed different search modes for improving the search ergodicity of the algorithm and increasing the probability that the algorithm finding a better solution in the late iteration. Thirdly, to verify the effectiveness of the new algorithm, standard test functions were selected, and the new algorithm was simulated with the original crow search algorithm, the improved crow search algorithms, and other excellent intelligent optimization algorithms. All the algorithms were compared and analyzed in terms of convergence accuracy, convergence speed, stability, Wilcoxon signed rank and Friedman tests. Experimental results show that the performance of the new algorithm is better than that of other comparison algorithms, and the balance between global exploration and local exploitation, convergence accuracy and convergence speed are achieved by the new algorithm.

Key words: swarm intelligence optimization    crow search algorithm    population diversity    search-guided individual    adaptive selection    sine-cosine search
收稿日期: 2022-01-19 出版日期: 2023-01-03
CLC:  TP 273  
基金资助: 广东省基础与应用基础研究基金资助项目(2020A1515010727,2021A1515012252,2022A1515012022);广东省普通高校特色创新类资助项目(2019KTSCX108);广东省重点领域研发计划资助项目(2021B0707010003);茂名市科技计划资助项目(mmkj2020008)
作者简介: 何杰光(1981—),男,讲师,博士,从事智能优化算法、云计算、机器学习研究. orcid.org/0000-0003-2321-1022.E-mail: hubice@163.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章  
何杰光
彭志平
崔得龙
李启锐

引用本文:

何杰光,彭志平,崔得龙,李启锐. 基于种群多样性的自适应乌鸦搜索算法[J]. 浙江大学学报(工学版), 2022, 56(12): 2426-2435.

Jie-guang HE,Zhi-ping PENG,De-long CUI,Qi-rui LI. Adaptive crow search algorithm based on population diversity. Journal of ZheJiang University (Engineering Science), 2022, 56(12): 2426-2435.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2022.12.011        https://www.zjujournals.com/eng/CN/Y2022/V56/I12/2426

$\alpha $ $\;\beta $ avg R
F5 F12 F15 F20
1 0.1 2.67×10?6 2.58×10?6 0.00035123 ?3.1876 10.00
1 0.2 2.37×10?5 6.74×10?7 0.00037324 ?3.2714 10.00
1 0.3 3.30×10?5 1.58×10?5 0.00036407 ?3.2935 11.50
0.9 0.1 4.96×10?5 1.47×10?5 0.00036082 ?3.2484 12.25
0.9 0.2 4.35×10?4 2.57×10?4 0.00034539 ?3.3009 12.00
0.9 0.3 8.16×10?5 2.04×10?4 0.00033519 ?3.3067 9.00
0.8 0.1 3.62×10?4 9.69×10?7 0.00036086 ?3.2369 12.50
0.8 0.2 3.37×10?4 2.74×10?4 0.00034989 ?3.2875 13.50
0.8 0.3 1.03×10?5 2.04×10?6 0.0003377 ?3.3155 4.00
0.7 0.1 1.36×10?6 8.89×10?7 0.00034967 ?3.2049 8.25
0.7 0.2 5.01×10?4 3.33×10?6 0.00034187 ?3.2855 10.50
0.7 0.3 1.61×10?5 1.56×10?7 0.00032797 ?3.3173 2.25
0.6 0.1 3.90×10?5 2.11×10?7 0.00034895 ?3.2294 9.25
0.6 0.2 2.40×10?5 7.28×10?5 0.00033706 ?3.3095 7.63
0.6 0.3 1.13×10?5 6.91×10?5 0.00034585 ?3.3048 8.00
0.5 0.1 1.38×10?5 4.70×10?5 0.00035523 ?3.2247 12.00
0.5 0.2 1.94×10?4 1.17×10?5 0.00033200 ?3.3095 6.88
0.5 0.3 6.78×10?4 3.32×10?5 0.00034609 ?3.3038 11.50
表 1  不同种群多样性阈值组合的收敛结果
图 1  不同进化时期搜索引导个体及其对应种群多样性分布图
搜索方式 avg
F1 F5 F7 F9 F12 F15 F20 F23
ACSA 0 9.50×10?4 2.68×10?4 0 1.91×10?9 3.46×10?4 ?3.3139 ?10.5364
rc=1 7.94×10?5 1.19×10?3 7.38×10?4 5.82×10?4 3.31×10?6 3.19×10?4 ?3.3099 ?10.5364
rc=2 2.29×10?4 1.03×10?3 1.05×10?3 1.81×10?4 3.02×10?6 3.20×10?4 ?3.318 0 ?10.5364
rc=3 1.62×10?4 2.86×10 3.04×10?3 1.52×10?3 2.97×10?2 3.56×10?4 ?3.2005 ?10.5364
rc=4 0 2.90×10 6.61×10?4 0 7.39×10?1 1.36×10?3 ?2.8081 ?10.536 0
表 2  不同搜索方式的收敛结果
算法 函数 avg std 函数 avg std 函数 avg std 函数 avg std
ACSA 0 0 0 0 0 0 0 0
CSA 2.09×10?1 9.70×10?2 2.58 8.48×10?1 1.30×102 4.63×10 5.70 1.81
SCCSA 3.37×10?32 1.82×10?31 2.39×10?17 5.84×10?17 1.42×10?16 5.47×10?16 5.65×10?17 1.80×10?16
ICSA 8.09×10?144 4.11×10?143 3.27×10?71 1.78×10?70 2.74×10?143 1.43×10?142 8.83×10?72 2.99×10?71
SCA F1 1.67×10?1 4.45×10?1 F2 3.58×10?5 7.96×10?5 F3 6.35×103 4.86×103 F4 2.69×10 1.29×10
MTDE 5.15×10?2 1.46×10?1 9.41×10?3 1.75×10?2 5.62×102 4.58×102 2.98×10 8.86
BLPSO 1.24×10?1 1.82×10?1 3.56×10?2 3.19×10?2 2.37×103 7.80×102 2.95×10 4.97
EO 3.97×10?73 1.10×10?72 1.45×10?42 3.14×10?42 3.32×10?17 1.37×10?16 1.49×10?18 3.23×10?18
HHO 8.38×10?180 0 1.29×10?91 6.86×10?91 2.06×10?134 9.45×10?134 7.19×10?86 3.94×10?85
ACSA 9.50×10?4 5.15×10?3 3.60×10?3 1.97×10?2 2.68×10?4 2.70×10?4 ?12 569.486 6 2.82×10?8
CSA 1.45×102 1.20×102 1.94×10?1 1.40×10?1 4.62×10?2 1.50×10?2 ?8 599.526 0 1.42×103
SCCSA 1.74×10?3 4.50×10?3 1.09×10?4 1.49×10?4 1.79×10?4 1.80×10?4 ?12 569.486 6 1.63×10?5
ICSA 2.90×10 1.42×10?2 6.66 6.33×10?1 3.20×10?4 3.20×10?4 ?5 117.599 6 8.81×102
SCA F5 2.55×103 6.61×103 F6 4.93 5.31×10?1 F7 5.33×10?2 8.60×10?2 F8 ?3 754.152 4 2.33×102
MTDE 2.93×102 3.34×102 1.18×10?2 2.92×10?2 1.28×10?1 6.05×10?2 ?11 574.031 2 2.85×102
BLPSO 2.58×102 2.84×102 1.24×10?1 2.31×10?1 2.08×10?2 9.52×10?3 ?11 524.497 0 3.16×102
EO 2.50×10 1.58×10?1 1.55×10?6 2.12×10?6 8.56×10?4 6.38×10?4 ?8 934.221 5 5.45×102
HHO 5.88×10?3 7.62×10?3 8.42×10?5 1.46×10?4 9.54×10?5 1.12×10?4 ?12 569.480 6 1.70×10?2
ACSA 0 0 8.88×10?16 0 0 0 1.91×10?9 5.16×10?9
CSA 3.66×10 1.80×10 4.49 1.12 4.03×10?1 1.26×10?1 6.75 2.67
SCCSA 0 0 5.27×10?15 1.79×10?15 0 0 8.95×10?6 2.12×10?5
ICSA 0 0 8.88×10?16 0 0 0 1.13 2.34×10?1
SCA F9 1.70×10 2.08×10 F10 1.24×10 8.99 F11 2.98×10?1 2.88×10?1 F12 1.07×102 4.07×102
MTDE 2.84×10 7.01 3.10 1.27 7.39×10?2 1.12×10?1 1.45×103 7.90×103
BLPSO 1.41×10 4.40 8.55×10?1 6.51×10?1 1.07×10?1 1.18×10?1 3.10×10?1 4.15×10?1
EO 0 0 5.86×10?15 1.77×10?15 8.20×10?4 4.49×10?3 3.46×10?3 1.89×10?2
HHO 0 0 8.88×10?16 0 0 0 3.43×10?6 4.82×10?6
ACSA 2.64×10?7 1.10×10?6 0.998 0 2.33×10?14 3.46×10?4 4.92×10?5 ?1.031 6 6.64×10?9
CSA 5.78 1.04×10 0.998 0 1.78×10?15 4.93×10?4 4.50×10?4 ?1.031 6 5.83×10?16
SCCSA 6.18×10?5 1.30×10?4 0.998 0 3.55×10?12 3.12×10?4 5.49×10?6 ?1.031 6 5.58×10?8
ICSA 2.98 7.65×10?2 4.579 0 3.42 7.59×10?3 1.23×10?2 ?1.031 6 6.01×10?10
SCA F13 4.06×103 1.39×104 F14 2.512 0 2.44 F15 9.47×10?4 3.34×10?4 F16 ?1.031 6 2.58×10?5
MTDE 7.40×103 4.03×104 0.998 0 0 3.07×10?4 1.32×10?19 ?1.031 6 6.05×10?16
BLPSO 2.96 3.06×10 0.998 0 0 7.01×10?4 4.81×10?5 ?1.031 6 6.78×10?16
EO 5.57×10?2 6.40×10?2 1.064 1 3.62×10?1 2.35×10?3 6.11×10?3 ?1.031 6 6.32×10?16
HHO 4.78×10?5 6.09×10?5 1.031 1 1.81×10?1 3.78×10?4 1.82×10?4 ?1.031 6 2.55×10?10
ACSA 0.397 89 4.47×10?6 3 5.22×10?7 ?3.862 8 4.80×10?5 ?3.313 9 1.12×10?2
CSA 0.397 89 0 3 2.44×10?15 ?3.862 8 2.49×10?15 ?3.294 2 5.12×10?2
SCCSA 0.397 89 4.89×10?6 3 5.87×10?5 ?3.857 4 1.86×10?2 ?3.213 0 9.23×10?2
ICSA 0.397 89 0 4.826 4 6.84 ?3.837 8 3.05×10?2 ?2.861 8 3.06×10?1
SCA F17 0.399 67 2.00×10?3 F18 3 3.14×10?5 F19 ?3.854 4 2.37×10?3 F20 ?2.718 6 5.65×10?1
MTDE 0.397 89 0 3 2.19×10?15 ?3.862 8 2.70×10?15 ?3.298 2 4.84×10?2
BLPSO 0.397 89 0 3 1.59×10?15 ?3.862 8 2.71×10?15 ?3.301 1 4.50×10?2
EO 0.397 89 0 3 8.12×10?16 ?3.862 8 2.57×10?15 ?3.267 9 6.98×10?2
HHO 0.397 89 3.98×10?6 3 2.72×10?7 ?3.861 9 1.37×10?3 ?3.161 9 8.03×10?2
ACSA ?10.153 2 2.34×10?7 ?10.402 9 2.71×10?5 ?10.536 4 3.24×10?5
CSA ?8.649 8 2.83 ?9.539 5 2.28 ?10.010 4 2.00
SCCSA ?10.153 2 4.30×10?5 ?10.402 8 2.90×10?6 ?10.536 3 1.81×10?5
ICSA ?6.470 5 1.97 ?6.761 6 2.65 ?6.296 7 2.83
SCA F21 ?1.727 2 1.57 F22 ?3.831 9 1.93 F23 ?3.300 4 2.02
MTDE ?9.646 4 1.55 ?9.165 5 2.28 ?9.635 1 2.05
BLPSO ?8.294 2 3.00 ?9.271 4 2.60 ?9.963 8 1.76
EO ?8.802 0 2.55 ?9.716 6 2.12 ?10.356 1 9.87×10?1
HHO ?5.901 6 1.93 ?5.085 6 2.22×10?3 ?5.126 3 2.44×10?3
表 3  基于种群多样性的自适应乌鸦搜索算法(ACSA)与其他算法的收敛结果对比
算法 t 算法 t 算法 t
ACSA 4.800 ICSA 4.660 BLPSO 7.976
CSA 4.511 SCA 3.547 EO 3.810
SCCSA 4.095 MTDE 13.856 HHO 9.565
表 4  不同算法对所有测试函数的总平均运行时间
图 2  基于种群多样性的自适应乌鸦搜索算法(ACSA)与其他算法在不同函数上的收敛曲线
算法 +/=/? R 排名
ACSA 2.195 7 1
CSA 14/9/0 6.043 5 8
SCCSA 14/8/1 3.543 5 2
ICSA 15/8/0 5.695 7 6
SCA 21/2/0 7.891 3 9
MTDE 15/7/1 5.782 6 7
BLPSO 16/7/0 5.565 2 5
EO 13/10/0 4.347 8 4
HHO 14/8/1 3.934 8 3
表 5  非参数统计检验
1 ASKARZADEH A A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm[J]. Computers and Structures, 2016, 169: 1- 12
doi: 10.1016/j.compstruc.2016.03.001
2 JAIN M, RANI A, SINGH V An improved crow search algorithm for high-dimensional problems[J]. Journal of Intelligent and Fuzzy Systems, 2017, 33 (6): 3597- 3614
doi: 10.3233/JIFS-17275
3 ZAMANI H, NADIMI-SHAHRAKI M H, GANDOMI A H CCSA: conscious neighborhood-based crow search algorithm for solving global optimization problems[J]. Applied Soft Computing, 2019, 85: 105583
doi: 10.1016/j.asoc.2019.105583
4 SAYED G I, HASSANIEN A E, AZAR A T Feature selection via a novel chaotic crow search algorithm[J]. Neural Computing and Applications, 2019, 31: 171- 188
doi: 10.1007/s00521-017-2988-6
5 OUADFEL S, ABD ELAZIZ M Enhanced crow search algorithm for feature selection[J]. Expert Systems with Applications, 2020, 159: 113572
doi: 10.1016/j.eswa.2020.113572
6 UPADHYAY P, CHHABRA J K Kapur’s entropy based optimal multilevel image segmentation using crow search algorithm[J]. Applied Soft Computing, 2020, 97: 105522
doi: 10.1016/j.asoc.2019.105522
7 FRED A L, KUMAR S, PADMANABAN P, et al. Fuzzy-crow search optimization for medical image segmentation [M]// OLIVA D, HINOJOSA S. Applications of hybrid metaheuristic algorithms for image processing. [S. l.]: Springer, 2020: 413-439.
8 SAHA A, BHATTACHARYA A, DAS P, et al. Crow search algorithm for solving optimal power flow problem [C]// 2017 Second International Conference on Electrical, Computer and Communication Technologies. Coimbatore: IEEE, 2017: 1-8.
9 FATHY A, ABDELAZIZ A Single-objective optimal power flow for electric power systems based on crow search algorithm[J]. Archives of Electrical Engineering, 2018, 67: 123- 138
10 MOHAMMADI F, ABDI H A modified crow search algorithm (MCSA) for solving economic load dispatch problem[J]. Applied Soft Computing, 2018, 71: 51- 65
doi: 10.1016/j.asoc.2018.06.040
11 SPEA S R Solving practical economic load dispatch problem using crow search algorithm[J]. International Journal of Electrical and Computer Engineering, 2020, 10 (4): 3431- 3440
12 KUMAR C A, VIMALA R C-FDLA: crow search with integrated fractional dragonfly algorithm for load balancing in cloud computing environments[J]. Journal of Circuits, Systems and Computers, 2019, 28 (7): 1950115
doi: 10.1142/S0218126619501159
13 KUMAR K R P, KOUSALYA K Amelioration of task scheduling in cloud computing using crow search algorithm[J]. Neural Computing and Applications, 2020, 32: 5901- 5907
doi: 10.1007/s00521-019-04067-2
14 TURGUT M S, TURGUT O E, ELIIYI D T Island-based crow search algorithm for solving optimal control problems[J]. Applied Soft Computing, 2020, 90: 106170
doi: 10.1016/j.asoc.2020.106170
15 ABDALLH G Y, ALGAMAL Z Y A QSAR classification model of skin sensitization potential based on improving binary crow search algorithm[J]. Electronic Journal of Applied Statistical Analysis, 2020, 13 (1): 86- 95
16 赵世杰, 高雷阜, 于冬梅, 等 基于变因子加权学习与邻代维度交叉策略的改进CSA算法[J]. 电子学报, 2019, 47 (1): 40- 48
ZHAO Shi-jie, GAO Lei-fu, YU Dong-mei, et al Improved crow search algorithm based on variable-factor weighted learning and adjacent-generations dimension crossover strategy[J]. Acta Electronica Sinica, 2019, 47 (1): 40- 48
doi: 10.3969/j.issn.0372-2112.2019.01.006
17 NECIRA A, NAIMI D, SALHI A, et al Dynamic crow search algorithm based on adaptive parameters for large-scale global optimization[J]. Evolutionary Intelligence, 2021, 15: 2153- 2169
18 MOGHADDAM S, BIGDELI M, MORADLOU M, et al Designing of stand-alone hybrid PV/wind/battery system using improved crow search algorithm considering reliability index[J]. International Journal of Energy and Environmental Engineering, 2019, 10: 429- 449
doi: 10.1007/s40095-019-00319-y
19 ARORA S, SINGH H, SHARMA M, et al A new hybrid algorithm based on grey wolf optimization and crow search algorithm for unconstrained function optimization and feature selection[J]. IEEE Access, 2019, 7: 26343- 26361
doi: 10.1109/ACCESS.2019.2897325
20 JAVAID N, MOHSIN S M, IQBAL A, et al. A hybrid bat-crow search algorithm based home energy management in smart grid [C]// Conference on Complex, Intelligent, and Software Intensive Systems. [S. l.]: Springer, 2018: 75-88.
21 MAHESH N, VIJAYACHITRA S DECSA: hybrid dolphin echolocation and crow search optimization for cluster-based energy-aware routing in WSN[J]. Neural Computing and Applications, 2019, 31: 47- 62
doi: 10.1007/s00521-018-3637-4
22 FARH H M H, AL-SHAALAN A M, ELTAMALY A M, et al A novel crow search algorithm auto-drive PSO for optimal allocation and sizing of renewable distributed generation[J]. IEEE Access, 2020, 8: 27807- 27820
doi: 10.1109/ACCESS.2020.2968462
23 MIRJALILI S SCA: a sine cosine algorithm for solving optimization problems[J]. Knowledge-Based Systems, 2016, 96: 120- 133
doi: 10.1016/j.knosys.2015.12.022
24 YAO X, LIU Y, LIN G Evolutionary programming made faster[J]. IEEE Transactions on Evolutionary Computation, 1999, 3 (2): 82- 102
doi: 10.1109/4235.771163
25 KHALILPOURAZARI S, PASANDIDEH S H R Sine–cosine crow search algorithm: theory and applications[J]. Neural Computing and Applications, 2020, 32: 7725- 7742
doi: 10.1007/s00521-019-04530-0
26 GHOLAMI J, MARDUKHI F, ZAWBAA H M An improved crow search algorithm for solving numerical optimization functions[J]. Soft Computing, 2021, 25: 9441- 9454
doi: 10.1007/s00500-021-05827-w
27 NADIMI-SHAHRAKI M H, TAGHIAN S, MIRJALILI S, et al MTDE: an effective multi-trial vector-based differential evolution algorithm and its applications for engineering design problems[J]. Applied Soft Computing, 2020, 97: 106761
doi: 10.1016/j.asoc.2020.106761
28 CHEN X, LI K, XU B, et al Biogeography-based learning particle swarm optimization for combined heat and power economic dispatch problem[J]. Knowledge-Based Systems, 2020, 208: 106463
doi: 10.1016/j.knosys.2020.106463
29 HEIDARI A A, MIRJALILI S, FARIS H, et al Harris hawks optimization: algorithm and applications[J]. Future Generation Computer Systems, 2019, 97: 849- 872
doi: 10.1016/j.future.2019.02.028
30 FARAMARZI A, HEIDARINEJAD M, STEPHENS B, et al Equilibrium optimizer: a novel optimization algorithm[J]. Knowledge-Based Systems, 2020, 191: 105190
doi: 10.1016/j.knosys.2019.105190
[1] 刘洲洲, 李士宁, 李彬, 王皓, 张倩昀, 郑然. 基于弹性碰撞优化算法的传感云资源调度[J]. 浙江大学学报(工学版), 2018, 52(8): 1431-1443.
[2] 柳景青, 罗志逢, 周晓燕, 何晓芳, 任红星, 胡宝兰, 裘尚德. 水流剪切力对供水管道管壁生物膜生长的影响[J]. 浙江大学学报(工学版), 2016, 50(2): 250-256.