Research article Special Issues

Improved Harris Hawks Optimization algorithm based on quantum correction and Nelder-Mead simplex method


  • Received: 20 February 2022 Revised: 23 April 2022 Accepted: 10 May 2022 Published: 23 May 2022
  • Harris Hawks Optimization (HHO) algorithm is a kind of intelligent algorithm that simulates the predation behavior of hawks. It suffers several shortcomings, such as low calculation accuracy, easy to fall into local optima and difficult to balance exploration and exploitation. In view of the above problems, this paper proposes an improved HHO algorithm named as QC-HHO. Firstly, the initial population is generated by Hénon Chaotic Map to enhance the randomness and ergodicity. Secondly, the quantum correction mechanism is introduced in the local search phase to improve optimization accuracy and population diversity. Thirdly, the Nelder-Mead simplex method is used to improve the search performance and breadth. Fourthly, group communication factors describing the relationship between individuals is taken into consideration. Finally, the energy consumption law is integrated into the renewal process of escape energy factor E and jump distance J to balance exploration and exploitation. The QC-HHO is tested on 10 classical benchmark functions and 30 CEC2014 benchmark functions. The results show that it is superior to original HHO algorithm and other improved HHO algorithms. At the same time, the improved algorithm studied in this paper is applied to gas leakage source localization by wireless sensor networks. The experimental results indicate that the accuracy of position and gas release rate are excellent, which verifies the feasibility for application of QC-HHO in practice.

    Citation: Cheng Zhu, Yong Zhang, Xuhua Pan, Qi Chen, Qingyu Fu. Improved Harris Hawks Optimization algorithm based on quantum correction and Nelder-Mead simplex method[J]. Mathematical Biosciences and Engineering, 2022, 19(8): 7606-7648. doi: 10.3934/mbe.2022358

    Related Papers:

  • Harris Hawks Optimization (HHO) algorithm is a kind of intelligent algorithm that simulates the predation behavior of hawks. It suffers several shortcomings, such as low calculation accuracy, easy to fall into local optima and difficult to balance exploration and exploitation. In view of the above problems, this paper proposes an improved HHO algorithm named as QC-HHO. Firstly, the initial population is generated by Hénon Chaotic Map to enhance the randomness and ergodicity. Secondly, the quantum correction mechanism is introduced in the local search phase to improve optimization accuracy and population diversity. Thirdly, the Nelder-Mead simplex method is used to improve the search performance and breadth. Fourthly, group communication factors describing the relationship between individuals is taken into consideration. Finally, the energy consumption law is integrated into the renewal process of escape energy factor E and jump distance J to balance exploration and exploitation. The QC-HHO is tested on 10 classical benchmark functions and 30 CEC2014 benchmark functions. The results show that it is superior to original HHO algorithm and other improved HHO algorithms. At the same time, the improved algorithm studied in this paper is applied to gas leakage source localization by wireless sensor networks. The experimental results indicate that the accuracy of position and gas release rate are excellent, which verifies the feasibility for application of QC-HHO in practice.



    加载中


    [1] K. M. Passino, Bacterial Foraging Optimization, Int. J. Swarm Intell. Res. (IJSIR), 1 (2010), 1–16. https://doi.org/10.4018/jsir.2010010101 doi: 10.4018/jsir.2010010101
    [2] J. Kennedy, R. C. Eberhart, Particle swarm optimization, in Proceedings of the 1995 International Conference on Neural Networks, (1995), 1942–1948. https://doi.org/10.1007/s11721-007-0002-0
    [3] R. Storn, K. Price, Differential evolution − A simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim., 11 (1997), 341–359. https://doi.org/10.1023/A:1008202821328 doi: 10.1023/A:1008202821328
    [4] S. Mirjalili, A. Lewis, The whale optimization algorithm, Adv. Eng. Software, 95 (2016), 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008 doi: 10.1016/j.advengsoft.2016.01.008
    [5] M. Dorigo, M. Birattari, T. Stützle, Ant colony optimization, in IEEE Computational Intelligence Magazine, 1 (2006), 28–39. https://doi.org/10.1109/MCI.2006.329691
    [6] B. S. Yıldız, S. Kumar, N. Pholdee, S. Bureerat, S. M. Sait, A. R. Yildiz, A new chaotic Lévy flight distribution optimization algorithm for solving constrained engineering problems, Expert Syst., 2022. https://doi.org/10.1111/exsy.12992 doi: 10.1111/exsy.12992
    [7] K. Wansasueb, S. Bureerat, S. Kumar, Ensemble of four metaheuristic using a weighted sum technique for aircraft wing design, Eng. Appl. Sci. Res., 48 (2021), 385–396. https://doi.org/10.14456/easr.2021.41 doi: 10.14456/easr.2021.41
    [8] S. Mirjalili, S. M. Mirjalili, A. Lewis, Grey Wolf Optimizer, Adv. Eng. Software, 69 (2014), 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007 doi: 10.1016/j.advengsoft.2013.12.007
    [9] E. Hopper, B. Turton, A genetic algorithm for a 2D industrial packing problem, Comput. Ind. Eng., 37 (1999), 375–378. https://doi.org/10.1016/S0360-8352(99)00097-2 doi: 10.1016/S0360-8352(99)00097-2
    [10] S. Baluja, Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning, Carnegie-Mellon Univ Pittsburgh Pa Dept of Computer Science, 1994. Available from: https://dl.acm.org/doi/book/10.5555/865123.
    [11] H. Eskandar, A. Sadollah, A. Bahreininejad, M. Hamdi, Water cycle algorithm − A novel metaheuristic optimization method for solving constrained engineering optimization problems, Comput. Struct., 110 (2012), 151–166. https://doi.org/10.1016/j.compstruc.2012.07.010 doi: 10.1016/j.compstruc.2012.07.010
    [12] S. Winyangkul, K. Wansaseub, S. Sleesongsom, N. Panagant, S. Kumar, S. Bureerat, et al., Ground structures-based topology optimization of a morphing wing using a metaheuristic algorithm, Metals, 11 (2021), 1311. https://doi.org/10.3390/met11081311 doi: 10.3390/met11081311
    [13] S. Kumar, G. G. Tejani, N. Pholdee, S. Bureerat, Improved metaheuristics through migration-based search and an acceptance probability for truss optimization, Asian J. Civ. Eng., 21 (2020), 1217–1237. https://doi.org/10.1007/s42107-020-00271-x doi: 10.1007/s42107-020-00271-x
    [14] A. Fathy, T. M. Alanazi, H. Rezk, D. Yousri, Optimal energy management of micro-grid using sparrow search algorithm, Energy Rep., 8 (2022), 758–773. https://doi.org/10.1016/j.egyr.2021.12.022 doi: 10.1016/j.egyr.2021.12.022
    [15] W. Long, J. J. Jiao, X. M. Liang, M. Xu, M. Z. Tang, S. H. Cai, Parameters estimation of photovoltaic models using a novel hybrid seagull optimization algorithm, Energy, 249 (2022), 123760. https://doi.org/10.1016/j.energy.2022.123760 doi: 10.1016/j.energy.2022.123760
    [16] A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. L. Chen, Harris Hawks Optimization: algorithm and applications, Future Gener. Comput. Syst., 97 (2019), 849–872. https://doi.org/10.1016/j.future.2019.02.028 doi: 10.1016/j.future.2019.02.028
    [17] A. Tang, T. Han, D. W. Xu, Chaos elite Harris Hawks Optimization algorithm, J. Com-put. Appl., 41 (2021), 2265–2272. Available from: https://kns.cnki.net/kcms/detail/51.1307.TP.20210114.0947.032.html.
    [18] Q. Yin, B. Cao, X. Li, B. Wang, Q. Zhang, X. P. Wei, An intelligent optimization algorithm for constructing a DNA storage code: NOL-HHO, Int. J. Mol. Sci., 21 (2020), 2191. https://doi.org/10.3390/ijms21062191 doi: 10.3390/ijms21062191
    [19] I. Attiya, M. A. Elaziz, S. W. Xiong, Job scheduling in cloud computing using a modified Harris Hawks Optimization and simulated annealing algorithm, Comput. Intell. Neurosci., 3 (2020), 1–16. https://doi.org/10.1155/2020/3504642 doi: 10.1155/2020/3504642
    [20] O. M. Ismael, O. S. Qasim, Z. Algamal, Improving Harris Hawks Optimization algorithm for hyperparameters estimation and feature selection in v-support vector regression based on opposition-based learning, Chemometrics, 34 (2020), 429–449. https://doi.org/10.1002/cem.3311 doi: 10.1002/cem.3311
    [21] C. W. Qu, W. He, X. N. Peng, X. N. Peng, Harris Hawks Optimization with information exchange, Appl. Math. Modell., 84 (2020), 52–75. https://doi.org/10.1016/j.apm.2020.03.024 doi: 10.1016/j.apm.2020.03.024
    [22] Y. M. Ma, Z. D. Shi, K. Zhao, C. L. Gong, L. H. Shan, TDOA localization based on imp-roved Harris Hawks Optimization algorithm, Comput. Eng., 46 (2020), 179–184. http://doi.org/10.19678/j.issn.1000-3428.0056965 doi: 10.19678/j.issn.1000-3428.0056965
    [23] H. Turabieh, S. A. Azwari, M. Rokaya, W. Alosaimi, A. Alharbi, W. Alhakami, et al., Enhanced Harris Hawks Optimization as a feature selection for the prediction of student performance, Computing, 103 (2021), 1417–1438. https://doi.org/10.1007/s00607-020-00894-7 doi: 10.1007/s00607-020-00894-7
    [24] S. K. ElSayed, E. E. Elattar, Hybrid Harris Hawks Optimization with sequential quadratic programming for optimal coordination of directional overcurrent relays incorporating distributed generation, Alexandria Eng. J., 60 (2021), 2421–2433. https://doi.org/10.1016/j.aej.2020.12.028 doi: 10.1016/j.aej.2020.12.028
    [25] S. M. Song, P. J. Wang, A. A. Heidari, X. H. Zhao, H. L. Chen, Adaptive Harris Hawks Optimization with persistent trigonometric differences for photovoltaic model parameter extraction, Eng. Appl. Artif. Intell., 109 (2022), 104608. https://doi.org/10.1016/j.engappai.2021.104608 doi: 10.1016/j.engappai.2021.104608
    [26] C. T. Zhong, G. Li, Comprehensive learning Harris Hawks-equilibrium Optimization with terminal replacement mechanism for constrained optimization problems, Expert Syst. Appl., 192 (2022), 116432. https://doi.org/10.1016/j.eswa.2021.116432 doi: 10.1016/j.eswa.2021.116432
    [27] J. Hu, Z. Y. Han, A. A. Heidari, Y. Q. Shou, H. Ye, L. X. Wang, et al., Detection of COVID-19 severity using blood gas analysis parameters and Harris Hawks Optimized extreme learning machine, Comput. Biol. Med., 142 (2022), 105166. https://doi.org/10.1016/j.compbiomed.2021.105166 doi: 10.1016/j.compbiomed.2021.105166
    [28] J. F. Liu, X. G. Liu, Y. Wu, Z. Yang, J. Xu, Dynamic multi-swarm differential learning Harris Hawks Optimizer and its application to optimal dispatch problem of cascade hydropower stations, Knowledge-Based Syst., 242 (2022), 108281. https://doi.org/10.1016/j.knosys.2022.108281 doi: 10.1016/j.knosys.2022.108281
    [29] Z. Z. Luo, S. Jin, Z. Y. Li, H. Huang, L. Xiao, H. L. Chen, et al., Hierarchical Harris Hawks Optimization for epileptic seizure classification, Comput. Biol. Med., 145 (2022), 105397. https://doi.org/10.1016/j.compbiomed.2022.105397 doi: 10.1016/j.compbiomed.2022.105397
    [30] A. Bardhan, N. Kardani, A. K. Alzo'ubi, B. Roy, P. Samui, A. H. Gandomi, Novel integration of extreme learning machine and improved Harris Hawks Optimization with particle swarm optimization-based mutation for predicting soil consolidation parameter, J. Rock Mech. Geotech. Eng., 2022. https://doi.org/10.1016/j.jrmge.2021.12.018 doi: 10.1016/j.jrmge.2021.12.018
    [31] Y. Choi, H. Nguyen, X. N. Bui, T. Nguyen-Thoi, Optimization of haulage-truck system performance for ore production in open-pit mines using big data and machine learning-based methods, Resour. Policy, 75 (2022), 102522. https://doi.org/10.1016/j.resourpol.2021.102522 doi: 10.1016/j.resourpol.2021.102522
    [32] E. M. Golafshani, M. Arashpour, A. Behnood, Predicting the compressive strength of green concretes using Harris Hawks Optimization-based data-driven methods, Constr. Build. Mater., 318 (2022), 125944. https://doi.org/10.1016/j.conbuildmat.2021.125944 doi: 10.1016/j.conbuildmat.2021.125944
    [33] F. Yu, X. Z. Xu, A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network, Appl. Energy, 134 (2014), 102–113. https://doi.org/10.1016/j.apenergy.2014.07.104 doi: 10.1016/j.apenergy.2014.07.104
    [34] D. Cai, X. Y. Ji, H. Shi, J. M. Pan, Method for improving piecewise Logistic chaotic map and its performance analysis, J. Nanjing Univ. (Nat. Sci.), 52 (2016), 809–815. Available from: https://jns.nju.edu.cn/CN/Y2016/V52/I5/809.
    [35] M. Hénon, A two-dimensional mapping with a strange attractor, Commun. Math. Phys., 50 (1976), 69–77. http://doi.org/10.1007/978-0-387-21830-4_8
    [36] J. Nelder, R. Mead, A simplex method for function minimization, Comput. J., 7 (1965), 308–313. https://doi.org/10.1093/comjnl/7.4.308 doi: 10.1093/comjnl/7.4.308
    [37] S. Gupta, K. Deep, A. A. Heidari, H. Moayedi, M. J. Wang, Opposition-based learning Harris Hawks Optimization with advanced transition rules: Principles and analysis, Expert Syst. Appl., 158 (2020), 113510. https://doi.org/10.1016/j.eswa.2020.113510 doi: 10.1016/j.eswa.2020.113510
  • Reader Comments
  • © 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2268) PDF downloads(133) Cited by(2)

Article outline

Figures and Tables

Figures(7)  /  Tables(15)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog