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SGOA: annealing-behaved grasshopper optimizer for global tasks

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Abstract

An improved grasshopper optimization algorithm (GOA) is proposed in this paper, termed as SGOA, which combines simulated annealing (SA) mechanism with the original GOA that is a natural optimizer widely used in finance, medical and other fields, and receives more promising results based on grasshopper behavior. To compare performance of the SGOA and other algorithms, an investigation to select CEC2017 benchmark function as the test set was carried out. Also, the Friedman assessment was performed to check the significance of the proposed method against other counterparts. In comparison with ten meta-heuristic algorithms such as differential evolution (DE), the proposed SGOA can rank first in the CEC2017, and also ranks first in comparison with ten advanced algorithms. The simulation results reveal that the SA strategy notably improves the exploration and exploitation capacity of GOA. Moreover, the SGOA is also applied to engineering problems and parameter optimization of the kernel extreme learning machine (KELM). After optimizing the parameters of KELM using SGOA, the model was applied to two datasets, Cleveland Heart Dataset and Japanese Bankruptcy Dataset, and they achieved an accuracy of 79.2% and 83.5%, respectively, which were better than the KELM model obtained other algorithms. In these practical applications, it is indicated that the proposed SGOA can provide effective assistance in settling complex optimization problems with impressive results.

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References

  1. Wang G-G, Tan YJITOC (2017) Improving metaheuristic algorithms with information feedback models. IEEE Trans Cybern 49(2):542–555

    Article  Google Scholar 

  2. Wang G-G et al (2014) Chaotic krill herd algorithm. Inf Sci 274:17–34

    Article  MathSciNet  Google Scholar 

  3. Gao D, Wang G-G, Pedrycz WJITOFS (2020) Solving fuzzy job-shop scheduling problem using de algorithm improved by a selection mechanism. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2020.3003506

    Article  Google Scholar 

  4. Wang G-G et al (2019) Monarch butterfly optimization. Neural Comput Appl 31(7):1995–2014

    Article  Google Scholar 

  5. Yi J-H et al (2018) An improved NSGA-III algorithm with adaptive mutation operator for big data optimization problems. Future Gener Comput Syst 88:571–585

    Article  Google Scholar 

  6. Zhang X et al (2019) Robust low-rank tensor recovery with rectification and alignment. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2019.2929043

    Article  Google Scholar 

  7. Deng W (2020) An enhanced MSIQDE algorithm with novel multiple strategies for global optimization problems. IEEE Trans Syst Man Cybern Syst. https://doi.org/10.1109/TSMC.2020.3030792

    Article  Google Scholar 

  8. Deng W et al (2020) An effective improved co-evolution ant colony optimization algorithm with multi-strategies and its application. Int J Bio-Inspir Comput 16(3):158–170

    Article  Google Scholar 

  9. Deng W et al (2020) An improved quantum-inspired differential evolution algorithm for deep belief network. IEEE Trans Instrum Meas. https://doi.org/10.1109/TIM.2020.2983233

    Article  Google Scholar 

  10. Zhao H et al (2019) Performance prediction using high-order differential mathematical morphology gradient spectrum entropy and extreme learning machine. IEEE Trans Instrum Meas. https://doi.org/10.1109/TIM.2019.2948414

    Article  Google Scholar 

  11. Song Y et al (2021) MPPCEDE: multi-population parallel co-evolutionary differential evolution for parameter optimization. Energy Convers Manag 228:113661

    Article  Google Scholar 

  12. Chen H et al (2020) Multi-population differential evolution-assisted Harris hawks optimization: framework and case studies. Future Gener Comput Syst 111:175–198

    Article  Google Scholar 

  13. Wang M, Chen H (2020) Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Appl Soft Comput 88:105946. https://doi.org/10.1016/j.asoc.2019.105946

    Article  Google Scholar 

  14. Zhao X et al (2019) Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients. Comput Biol Chem 78:481–490

    Article  Google Scholar 

  15. Wang M et al (2017) Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses. Neurocomputing 267:69–84

    Article  Google Scholar 

  16. Shen L et al (2016) Evolving support vector machines using fruit fly optimization for medical data classification. Knowl Based Syst 96:61–75

    Article  Google Scholar 

  17. Xu X, Chen HL (2014) Adaptive computational chemotaxis based on field in bacterial foraging optimization. Soft Comput 18(4):797–807

    Article  Google Scholar 

  18. Chen H et al (2019) A balanced whale optimization algorithm for constrained engineering design problems. Appl Math Model 71:45–59

    Article  MathSciNet  MATH  Google Scholar 

  19. Luo J et al (2019) Multi-strategy boosted mutative whale-inspired optimization approaches. Appl Math Model 73:109–123

    Article  MathSciNet  MATH  Google Scholar 

  20. Yu H et al (2020) Chaos-enhanced synchronized bat optimizer. Appl Math Model 77:1201–1215

    Article  Google Scholar 

  21. Chen H et al (2020) Efficient multi-population outpost fruit fly-driven optimizers: framework and advances in support vector machines. Expert Syst Appl 142:112999

    Article  Google Scholar 

  22. Chen H, Wang M, Zhao X (2020) A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems. Appl Math Comput 369:124872. https://doi.org/10.1016/j.amc.2019.124872

    Article  MathSciNet  MATH  Google Scholar 

  23. Zhang X et al (2020) Gaussian mutational chaotic fruit fly-built optimization and feature selection. Expert Syst Appl 141:112976

    Article  Google Scholar 

  24. Song S et al (2020) Dimension decided Harris hawks optimization with Gaussian mutation: balance analysis and diversity patterns. Knowl Based Syst. https://doi.org/10.1016/j.knosys.2020.106425

    Article  Google Scholar 

  25. Zhao D et al (2020) Ant colony optimization with horizontal and vertical crossover search: fundamental visions for multi-threshold image segmentation. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.114122

    Article  Google Scholar 

  26. Zhang Y et al (2020) Towards augmented kernel extreme learning models for bankruptcy prediction: algorithmic behavior and comprehensive analysis. Neurocomputing. https://doi.org/10.1016/j.neucom.2020.10.038

    Article  Google Scholar 

  27. Wang X et al (2020) Multi-population following behavior-driven fruit fly optimization: a Markov chain convergence proof and comprehensive analysis. Knowl Based Syst 210:106437. https://doi.org/10.1016/j.knosys.2020.106437

    Article  Google Scholar 

  28. Zhao D et al (2020) Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy. Knowl Based Syst. https://doi.org/10.1016/j.knosys.2020.106510

    Article  Google Scholar 

  29. Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–72

    Article  Google Scholar 

  30. Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344(2–3):243–278

    Article  MathSciNet  MATH  Google Scholar 

  31. James K, Gireesha OB (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4. https://doi.org/10.1109/ICNN.1995.488968 

  32. Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073

    Article  Google Scholar 

  33. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  34. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  MATH  Google Scholar 

  35. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  36. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Article  Google Scholar 

  37. Zhou H et al (2020) An improved Grasshopper optimizer for global tasks. Complexity 2020:4873501

    Article  Google Scholar 

  38. Xu Z et al (2020) Orthogonally-designed adapted grasshopper optimization: a comprehensive analysis. Expert Syst Appl 150:113282

    Article  Google Scholar 

  39. Tumuluru P, Ravi B (2017) GOA-based DBN: Grasshopper optimization algorithm-based deep belief neural networks for cancer classification. Int J Appl Eng Res 12(24):14218–14231

    Google Scholar 

  40. Zhao H, Zhao H, Guo S (2018) Short-term wind electric power forecasting using a novel multi-stage intelligent algorithm. Sustainability (Switzerland) 10(3):881

    Article  Google Scholar 

  41. Sultana U et al (2018) Placement and sizing of multiple distributed generation and battery swapping stations using grasshopper optimizer algorithm. Energy 165:408–421

    Article  Google Scholar 

  42. Liang H et al (2019) Modified grasshopper algorithm-based multilevel thresholding for color image segmentation. IEEE Access 7:11258–11295

    Article  Google Scholar 

  43. Omar AI et al (2019) An improved approach for robust control of dynamic voltage restorer and power quality enhancement using grasshopper optimization algorithm. ISA Trans 95:110–129

    Article  Google Scholar 

  44. Zhang X et al (2018) A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery. Mech Syst Signal Process 108:58–72

    Article  Google Scholar 

  45. Mafarja M et al (2018) Evolutionary population dynamics and Grasshopper Optimization approaches for feature selection problems. Knowl Based Syst 145:125–145

    Article  Google Scholar 

  46. Jumani TA et al (2018) Optimal voltage and frequency control of an islanded microgrid using grasshopper optimization algorithm. Energies 11(11):3191

    Article  Google Scholar 

  47. Ibrahim HT et al (2019) A grasshopper optimizer approach for feature selection and optimizing SVM parameters utilizing real biomedical data sets. Neural Comput Appl 31(10):5965–5974

    Article  Google Scholar 

  48. Liu J et al (2018) Coordinated operation of multi-integrated energy system based on linear weighted sum and Grasshopper optimization algorithm. IEEE Access 6:42186–42195

    Article  Google Scholar 

  49. Wu J et al (2017) Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by adaptive Grasshopper optimization algorithm. Aerosp Sci Technol 70:497–510

    Article  Google Scholar 

  50. Saxena A (2019) A comprehensive study of chaos embedded bridging mechanisms and crossover operators for grasshopper optimisation algorithm. Expert Syst Appl 132:166–188

    Article  Google Scholar 

  51. Luo J et al (2018) An improved grasshopper optimization algorithm with application to financial stress prediction. Appl Math Model 64:654–668

    Article  MathSciNet  MATH  Google Scholar 

  52. Jia H et al (2019) Hybrid grasshopper optimization algorithm and differential evolution for global optimization. J Intell Fuzzy Syst 37(5):6899–6910

    Article  Google Scholar 

  53. Zakeri A, Hokmabadi A (2019) Efficient feature selection method using real-valued grasshopper optimization algorithm. Expert Syst Appl 119:61–72

    Article  Google Scholar 

  54. Saxena A, Shekhawat S, Kumar R (2018) Application and development of enhanced chaotic Grasshopper optimization algorithms. Model Simul Eng 2018:1–14

    Article  Google Scholar 

  55. Jia H et al (2019) Hybrid Grasshopper optimization algorithm and differential evolution for multilevel satellite image segmentation. Remote Sens 11(9):1134

    Article  Google Scholar 

  56. Ewees AA, Abd Elaziz M, Houssein EH (2018) Improved grasshopper optimization algorithm using opposition-based learning. Expert Syst Appl 112:156–172

    Article  Google Scholar 

  57. Yue X, Zhang H (2019) Grasshopper optimization algorithm with principal component analysis for global optimization. J Supercomput 76:5609–5635

    Article  Google Scholar 

  58. Arora S, Anand P (2019) Chaotic grasshopper optimization algorithm for global optimization. Neural Comput Appl 31(8):4385–4405

    Article  Google Scholar 

  59. Ghulanavar R, Dama KK, Jagadeesh A (2020) Diagnosis of faulty gears by modified AlexNet and improved grasshopper optimization algorithm (IGOA). J Mech Sci Technol 34(10):4173–4182

    Article  Google Scholar 

  60. Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MathSciNet  MATH  Google Scholar 

  61. LaTorre A, Pena JM (2017) A comparison of three large-scale global optimizers on the CEC 2017 single objective real parameter numerical optimization benchmark. In: 2017 IEEE congress on evolutionary computation, CEC 2017—proceedings

  62. Alcalá-Fdez J et al (2009) KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13(3):307–318

    Article  Google Scholar 

  63. Huang GB et al (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 42(2):513–529

    Article  Google Scholar 

  64. Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science (New York NY) 220(4598):671–680

    Article  MathSciNet  MATH  Google Scholar 

  65. Chechkin AV et al (2008) Introduction to the theory of Lévy flights, in anomalous transport: foundations and applications. pp 129–162

  66. Bäck T, Schwefel H-P (1993) An overview of evolutionary algorithms for parameter optimization. Evol Comput 1(1):1–23

    Article  Google Scholar 

  67. Derrac J et al (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18

    Article  Google Scholar 

  68. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  69. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249

    Article  Google Scholar 

  70. Mirjalili S et al (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

  71. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Studies in computational intelligence. pp 65–74

  72. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133

    Article  Google Scholar 

  73. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513

    Article  Google Scholar 

  74. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98

    Article  Google Scholar 

  75. García-Martínez C et al (2008) Global and local real-coded genetic algorithms based on parent-centric crossover operators. Eur J Oper Res 185(3):1088–1113

    Article  MATH  Google Scholar 

  76. Liang JJ et al (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295

    Article  Google Scholar 

  77. Chen WN et al (2013) Particle swarm optimization with an aging leader and challengers. IEEE Trans Evol Comput 17(2):241–258

    Article  Google Scholar 

  78. Xu Y et al (2019) An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks. Expert Syst Appl 129:135–155

    Article  Google Scholar 

  79. Xu Y et al (2019) Enhanced moth-flame optimizer with mutation strategy for global optimization. Inf Sci. https://doi.org/10.1016/j.ins.2019.04.022

    Article  MathSciNet  Google Scholar 

  80. Liang H et al (2018) A hybrid bat algorithm for economic dispatch with random wind power. IEEE Trans Power Syst 33(5):5052–5261

    Article  Google Scholar 

  81. Adarsh BR et al (2016) Economic dispatch using chaotic bat algorithm. Energy 96:666–675

    Article  Google Scholar 

  82. Ling Y, Zhou Y, Luo Q (2017) Lévy flight trajectory-based whale optimization algorithm for global optimization. IEEE Access 5:6168–6186

    Article  Google Scholar 

  83. Heidari AA et al (2019) An enhanced associative learning-based exploratory whale optimizer for global optimization. Neural Comput Appl 32:5185–5211

    Article  Google Scholar 

  84. Coello Coello CA (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191(11–12):1245–1287

    Article  MathSciNet  MATH  Google Scholar 

  85. Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112–113:283–294

    Article  Google Scholar 

  86. Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194(36–38):3902–3933

    Article  MATH  Google Scholar 

  87. Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579

    MathSciNet  MATH  Google Scholar 

  88. Ragsdell KM, Phillips DT (1976) Optimal design of a class of welded structures using geometric programming. J Eng Ind 98(3):97–97

    Article  Google Scholar 

  89. Kannan BK, Kramer SN (1994) An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. J Mech Des Trans ASME 116(2):405–411

    Article  Google Scholar 

  90. He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20(1):89–99

    Article  Google Scholar 

  91. Deb K (1997) GeneAS: a robust optimal design technique for mechanical component design, vol 185

  92. Mezura-Montes E, Coello CAC (2008) An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. Int J Gen Syst 37(4):443–473

    Article  MathSciNet  MATH  Google Scholar 

  93. Eskandar H et al (2012) Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110–111:151–166

    Article  Google Scholar 

  94. Zhang M, Luo W, Wang X (2008) Differential evolution with dynamic stochastic selection for constrained optimization. Inf Sci 178(15):3043–3074

    Article  Google Scholar 

  95. Wang L, Li LP (2010) An effective differential evolution with level comparison for constrained engineering design. Struct Multidiscip Optim 41(6):947–963

    Article  Google Scholar 

  96. Wang Y et al (2009) Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique. Struct Multidiscip Optim 37(4):395–413

    Article  Google Scholar 

  97. Mezura-Montes E, Velázquez-Reyes J, Coello Coello CA (2006) Modified differential evolution for constrained optimization. In: 2006 IEEE congress on evolutionary computation, CEC 2006

  98. Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput J 10(2):629–640

    Article  Google Scholar 

  99. Zhao D et al (2017) An effective computational model for bankruptcy prediction using kernel extreme learning machine approach. Comput Econ 49(2):325–341

    Article  Google Scholar 

  100. Chen H et al (2020) An enhanced bacterial foraging optimization and its application for training kernel extreme learning machine. Appl Soft Comput 86:105884

    Article  Google Scholar 

  101. Wang M et al (2017) Grey wolf optimization evolving kernel extreme learning machine: application to bankruptcy prediction. Eng Appl Artif Intell 63:54–68

    Article  Google Scholar 

  102. Qiang L et al (2017) An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis. Comput Math Methods Med 2017:1–15

    Google Scholar 

  103. Liu T et al (2015) A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection. Int J Syst Sci 46(5):919–931

    Article  MATH  Google Scholar 

  104. Chen H et al (2015) Using blood indexes to predict overweight statuses: an extreme learning machine-based approach. PLoS ONE 10(11):e0143003

    Article  Google Scholar 

  105. Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27

    Article  MATH  Google Scholar 

  106. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536

    Article  MATH  Google Scholar 

  107. Kumar PR, Ravi V (2007) Bankruptcy prediction in banks and firms via statistical and intelligent techniques—a review. Eur J Oper Res 180(1):1–28

    Article  MATH  Google Scholar 

  108. Zhang X et al (2020) Top-k feature selection framework using robust 0–1 integer programming. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.3009209

    Article  Google Scholar 

  109. Zhang Y et al (2020) Boosted binary Harris hawks optimizer and feature selection. Eng Comput. https://doi.org/10.1007/s00366-020-01028-5

    Article  Google Scholar 

  110. Yang C et al (2018) Superpixel-based unsupervised band selection for classification of hyperspectral images. IEEE Trans Geosci Remote Sens 56(12):7230–7245

    Article  Google Scholar 

  111. Chen HL et al (2016) An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson’s disease. Neurocomputing 184:131–144

    Article  Google Scholar 

  112. Hu L et al (2015) An efficient machine learning approach for diagnosis of paraquat-poisoned patients. Comput Biol Med 59:116–124

    Article  Google Scholar 

  113. Xia J et al (2017) Ultrasound-based differentiation of malignant and benign thyroid Nodules: an extreme learning machine approach. Comput Methods Programs Biomed 147:37–49

    Article  Google Scholar 

  114. Zhang X et al (2020) Pyramid channel-based feature attention network for image dehazing. Comput Vis Image Understand. https://doi.org/10.1016/j.cviu.2020.103003

    Article  Google Scholar 

  115. Wang T et al (2020) Video deblurring via spatiotemporal pyramid network and adversarial gradient prior. Comput Vis Image Understand. https://doi.org/10.1016/j.cviu.2020.103135

    Article  Google Scholar 

  116. Li Y et al (2019) Epileptic seizure detection in EEG signals using sparse multiscale radial basis function networks and the Fisher vector approach. Knowl Based Syst 164:96–106

    Article  Google Scholar 

  117. Li Y et al (2020) Deep spatial-temporal feature fusion from adaptive dynamic functional connectivity for MCI identification. IEEE Trans Med Imaging 39(9):2818–2830

    Article  Google Scholar 

  118. Li Y et al (2020) Epileptic seizure detection in EEG signals using a unified temporal-spectral squeeze-and-excitation network. IEEE Trans Neural Syst Rehabil Eng 28(4):782–794

    Article  Google Scholar 

  119. Guan R et al (2020) Deep feature-based text clustering and its explanation. IEEE Trans Knowl Data Eng 14:1–1

    Article  Google Scholar 

  120. Fei X et al (2020) Projective parameter transfer based sparse multiple empirical kernel learning machine for diagnosis of brain disease. Neurocomputing 413:271–283

    Article  Google Scholar 

  121. Chen Z et al (2021) Information synergy entropy based multi-feature information fusion for the operating condition identification in aluminium electrolysis. Inf Sci 548:275–294

    Article  MathSciNet  Google Scholar 

  122. Xue X et al (2019) Social learning evolution (SLE): computational experiment-based modeling framework of social manufacturing. IEEE Trans Ind Inform 15(6):3343–3355

    Article  Google Scholar 

  123. Wang D et al (2018) A content-based recommender system for computer science publications. Knowl Based Syst 157:1–9

    Article  Google Scholar 

  124. Ridha HM et al (2021) Multi-objective optimization and multi-criteria decision-making methods for optimal design of standalone photovoltaic system: a comprehensive review. Renew Sustain Energy Rev 135:110202. https://doi.org/10.1016/j.rser.2020.110202

    Article  Google Scholar 

  125. Chen H et al (2020) Parameters identification of photovoltaic cells and modules using diversification-enriched Harris hawks optimization with chaotic drifts. J Clean Prod 244:118778. https://doi.org/10.1016/j.jclepro.2019.118778

    Article  Google Scholar 

  126. Chen H et al (2019) An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models. Energy Convers Manag 195:927–942

    Article  Google Scholar 

  127. Abbassi A et al (2020) Parameters identification of photovoltaic cell models using enhanced exploratory salp chains-based approach. Energy 198:117333. https://doi.org/10.1016/j.energy.2020.117333

    Article  Google Scholar 

  128. Ridha HM et al (2020) Boosted mutation-based Harris hawks optimizer for parameters identification of single-diode solar cell models. Energy Convers Manag 209:112660. https://doi.org/10.1016/j.enconman.2020.112660

    Article  Google Scholar 

  129. Zhang H et al (2020) Orthogonal Nelder-Mead moth flame method for parameters identification of photovoltaic modules. Energy Convers Manag 211:112764. https://doi.org/10.1016/j.enconman.2020.112764

    Article  Google Scholar 

  130. Jiao S et al (2020) Orthogonally adapted Harris hawks optimization for parameter estimation of photovoltaic models. Energy 203:117804. https://doi.org/10.1016/j.energy.2020.117804

    Article  Google Scholar 

  131. Liu Y et al (2020) Horizontal and vertical crossover of Harris hawk optimizer with Nelder-Mead simplex for parameter estimation of photovoltaic models. Energy Convers Manag 223:113211. https://doi.org/10.1016/j.enconman.2020.113211

    Article  Google Scholar 

  132. Wang M et al (2020) Evaluation of constraint in photovoltaic models by exploiting an enhanced ant lion optimizer. Sol Energy 211:503–521

    Article  Google Scholar 

  133. Sun Y, Yen GG, Yi Z (2019) IGD indicator-based evolutionary algorithm for many-objective optimization problems. IEEE Trans Evol Comput 23(2):173–187

    Article  Google Scholar 

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Acknowledgment

This research is supported by National Natural Science Foundation of China (62076185, 71803136, U1809209), the Ministry of Education of Humanities and Social Science Project of Wenzhou Business College (20YJA790090), the Characteristic Innovation Project of Guangdong Universities in 2020 (2020KTSCX302), Guangdong Natural Science Foundation (2018A030313339), Scientific Research Team Project of Shenzhen Institute of Information Technology (SZIIT2019KJ022).

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  1. Fangjun Kuang, Huiling Chen have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China

    Caiyang Yu & Huiling Chen

  2. Department of Information Technology, Wenzhou Vocational College of Science and Technology, Wenzhou, 325006, Zhejiang, China

    Mengxiang Chen

  3. State Grid Hebei Information & Telecommunication Branch, Shijiazhuang, 050021, China

    Kai Cheng

  4. School of Digital Media, Shenzhen Institute of Information Technology, Shenzhen, 518172, China

    Xuehua Zhao & Chao Ma

  5. School of Information Engineering, Wenzhou Business College, Wenzhou, 325035, China

    Fangjun Kuang

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Yu, C., Chen, M., Cheng, K. et al. SGOA: annealing-behaved grasshopper optimizer for global tasks. Engineering with Computers 38 (Suppl 5), 3761–3788 (2022). https://doi.org/10.1007/s00366-020-01234-1

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  • DOI: https://doi.org/10.1007/s00366-020-01234-1

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