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21.09.2021 | Original Article

An improved Chaotic Harris Hawks Optimizer for solving numerical and engineering optimization problems

verfasst von: Dinesh Dhawale, Vikram Kumar Kamboj, Priyanka Anand

Erschienen in: Engineering with Computers | Ausgabe 2/2023

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Abstract

Harris Hawk’s Optimizer (HHO) is a recently developed meta-heuristics search algorithm with inherent capability to explore global minima and maxima. However, the local search of the basic HHO algorithm is sluggish and has slow convergence rate due to its poor exploitation capability. In the present work, exploration and exploitation phase of HHO have been improved using a chaotic variant of the present optimizer. The proposed chaotic variant has been simulated and tested for 23 standard test functions and 10 different engineering design optimization problems of real life. To check the efficacy of the proposed algorithm, the test results of the proposed CHHO algorithm have been compared with others recently developed and well-known classical optimizers, such as PSO, DE, SSA, MVO, GWO, DE, MFO, SCA, CS, TSA, PSO-DE, GA, HS, Ray and Sain, MBA, ACO, MMA, etc. The experimental results reveal that the suggested method outperforms on most of the test functions and engineering design challenges with superior convergence.

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Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007CrossRef

Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1:28–39. https://doi.org/10.1109/MCI.2006.329691CrossRef

Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Perth A (ed) Proceedings of IEEE international conference of neural network. Springer, Cham, pp 1942–1948CrossRef

Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42:21–57. https://doi.org/10.1007/s10462-012-9328-0CrossRef

Yang XS (2010) A new metaheuristic bat-inspired algorithm. Nature inspired ooperative strategies for optimization (NICSO 2010). Springer, Cham, p 65CrossRef

Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl -Based Syst 89:228–249. https://doi.org/10.1016/j.knosys.2015.07.006CrossRef

Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17:4831–4845. https://doi.org/10.1016/j.cnsns.2012.05.010MathSciNetCrossRefMATH

Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm: a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110–111:151–166. https://doi.org/10.1016/j.compstruc.2012.07.010CrossRef

Li X, Zhang J, Yin M (2014) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Appl 24:1867–1877. https://doi.org/10.1007/s00521-013-1433-8CrossRef

10.

Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition, 2007 IEEE Congr. Evol Comput CEC 2007:4661–4667. https://doi.org/10.1109/CEC.2007.4425083CrossRef

11.

Cohen AI, Yoshimura M (1983) A branch-and-bound algorithm for unit commitment. IEEE Trans Power Appar Syst 102:444–451CrossRef

12.

Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris Hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849–872. https://doi.org/10.1016/j.future.2019.02.028CrossRef

13.

Kazarlis SA, Bakirtzis AG, Petridis V (1996) A genetic algorithm solution to the unit commitment problem. IEEE Trans Power Syst 11:83–92. https://doi.org/10.1109/59.485989CrossRef

14.

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

15.

Hamdani H, Radi B, El Hami A (2019) Optimization of solder joints in embedded mechatronic systems via Kriging-assisted CMA-ES algorithm. Int J Simul Multidiscip Des Optim 10:A3. https://doi.org/10.1051/smdo/2019002CrossRef

16.

Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci (Ny) 179:2232–2248. https://doi.org/10.1016/j.ins.2009.03.004CrossRefMATH

17.

Kaveh A (2016) Advances in metaheuristic algorithms for optimal design of structures, 2nd edn. Springer, Cham. https://doi.org/10.1007/978-3-319-46173-1CrossRefMATH

18.

Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495–513. https://doi.org/10.1007/s00521-015-1870-7CrossRef

19.

Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133. https://doi.org/10.1016/j.knosys.2015.12.022CrossRef

20.

Glover F, Melián B (2003) Tabu search. Intel Artif 7:43–57. https://doi.org/10.4114/ia.v7i19.714CrossRef

21.

Satapathy SC, Naik A, Parvathi K (2013) A teaching learning based optimization based on orthogonal design for solving global optimization problems. Springerplus 2:1–12. https://doi.org/10.1186/2193-1801-2-130CrossRef

22.

Mirjalili S, Jangir P, Saremi S (2017) Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems. Appl Intell 46:79–95. https://doi.org/10.1007/s10489-016-0825-8CrossRef

23.

Saremi S, Mirjalili S, Lewis A (2014) Biogeography-based optimisation with chaos. Neural Comput Appl 25:1077–1097. https://doi.org/10.1007/s00521-014-1597-xCrossRef

24.

Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609. https://doi.org/10.1016/j.cma.2020.113609MathSciNetCrossRefMATH

25.

Hashim FA, Hussain K, Houssein EH, Mabrouk MS, Al-Atabany W (2021) Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Appl Intell 51:1531–1551. https://doi.org/10.1007/s10489-020-01893-zCrossRefMATH

26.

Hu K, Jiang H, Ji CG, Pan Z (2021) A modified butterfly optimization algorithm: an adaptive algorithm for global optimization and the support vector machine. Expert Syst 38:1–18. https://doi.org/10.1111/exsy.12642CrossRef

27.

Bala Krishna A, Saxena S, V.K, (2021) Kamboj, hSMA-PS: a novel memetic approach for numerical and engineering design challenges. Springer, London. https://doi.org/10.1007/s00366-021-01371-1CrossRef

28.

Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-qaness MAA, Gandomi AH (2021) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250. https://doi.org/10.1016/j.cie.2021.107250CrossRef

29.

Xu Z, Gui W, Heidari AA, Liang G, Chen H, Wu C, Turabieh H, Mafarja M (2021) Spiral motion mode embedded grasshopper optimization algorithm: design and analysis. IEEE Access 9:71104–71132. https://doi.org/10.1109/access.2021.3077616CrossRef

30.

Neshat M, Nezhad MM, Abbasnejad E, Mirjalili S, Groppi D, Heydari A, Tjernberg LB, Astiaso Garcia D, Alexander B, Shi Q, Wagner M (2021) Wind turbine power output prediction using a new hybrid neuro-evolutionary method. Energy 229:120617. https://doi.org/10.1016/j.energy.2021.120617CrossRef

31.

Kaur A, Singh L, Dhillon JS (2021) Modified Krill Herd algorithm for constrained economic load dispatch problem. Int J Ambient Energy. https://doi.org/10.1080/01430750.2021.1888798CrossRef

32.

Nandi A, Kamboj VK (2021) A meliorated Harris Hawks Optimizer for combinatorial unit commitment problem with photovoltaic applications. J Electr Syst Inf Technol. https://doi.org/10.1186/s43067-020-00026-3CrossRef

33.

Yang Y, Chen H, Heidari AA, Gandomi AH (2021) Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Syst Appl 177:114864. https://doi.org/10.1016/j.eswa.2021.114864CrossRef

34.

Osaba E, Yang X-S (2021) Soccer-inspired metaheuristics: systematic review of recent research and applications. Appl Optim Swarm Intell. https://doi.org/10.1007/978-981-16-0662-5_5CrossRef

35.

Barshandeh S, Piri F, Sangani SR (2020) HMPA: an innovative hybrid multi-population algorithm based on artificial ecosystem-based and Harris Hawks optimization algorithms for engineering problems. Springer, London. https://doi.org/10.1007/s00366-020-01120-wCrossRef

36.

Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2020.03.055CrossRef

37.

Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine predators algorithm: a nature-inspired metaheuristic. Expert Syst Appl 152:113377. https://doi.org/10.1016/j.eswa.2020.113377CrossRef

38.

Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris Hawks optimization: algorithm and applications. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2019.02.028CrossRef

39.

Chen X, Tianfield H, Li K, SC, (2019) Self-adaptive differential artificial bee colony algorithm for global optimization problems. Swarm Evol Comput. https://doi.org/10.1016/j.swevo.2019.01.003CrossRef

40.

Shadravan S, Naji HR, Bardsiri VK (2019) The Sailfish Optimizer: a novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Eng Appl Artif Intell 80:20–34. https://doi.org/10.1016/j.engappai.2019.01.001CrossRef

41.

Pierezan J (2018) Coyote optimization algorithm : a new metaheuristic for global optimization problems. In: 2018 IEEE Congress on Evolutionary Computation, pp 1–8

42.

Sulaiman MH, Mustaffa Z, Saari MM, Daniyal H, Mohamad AJ, Othman MR (2019) Barnacles mating optimizer algorithm for optimization mohd. Springer, Singapore. https://doi.org/10.1007/978-981-13-3708-6CrossRef

43.

Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002CrossRef

44.

Tabari A, Ahmad A (2017) Accept e us cr t. Comput Chem Eng. https://doi.org/10.1016/j.compchemeng.2017.01.046CrossRef

45.

Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47. https://doi.org/10.1016/j.advengsoft.2017.01.004CrossRef

46.

Singh N, Singh SB (2017) A novel hybrid GWO-SCA approach for optimization problems. Eng Sci Technol an Int J 20:1586–1601. https://doi.org/10.1016/j.jestch.2017.11.001CrossRef

47.

Gohil NB, Dwivedi VV (2017) A review on lion optimization : nature inspired evolutionary algorithm. Int J Adv Manag Technol Eng Sci 7:340–352

48.

Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008CrossRef

49.

Gray B (2015) W. optimization, author’ s accepted manuscript binary gray wolf optimization approaches for feature selection. Neurocomputing. https://doi.org/10.1016/j.neucom.2015.06.083CrossRef

50.

Shahriar MS, Rana MJ, Asif MA, Hasan MM, Hawlader MM (2015) Optimization of Unit Commitment Problem for wind-thermal generation using Fuzzy optimization technique. In 2015 International conference on advances in electrical engineering (ICAEE). IEEE, pp 88–92

51.

Huang L, Ding S, Yu S, Wang J, Lu K (2016) Chaos-enhanced Cuckoo search optimization algorithms for global optimization. Appl Math Model 40:3860–3875. https://doi.org/10.1016/j.apm.2015.10.052MathSciNetCrossRefMATH

52.

Ghasemi M, Ghavidel S, Akbari E, Vahed AA (2014) Solving non-linear, non-smooth and non-convex optimal power flow problems using chaotic invasive weed optimization algorithms based on chaos. Energy 73:340–353. https://doi.org/10.1016/j.energy.2014.06.026CrossRef

53.

Cheng MY, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112. https://doi.org/10.1016/j.compstruc.2014.03.007CrossRef

54.

Wang GG, Guo L, Gandomi AH, Hao GS, Wang H (2014) Chaotic Krill Herd algorithm. Inf Sci (Ny) 274:17–34. https://doi.org/10.1016/j.ins.2014.02.123MathSciNetCrossRef

55.

Gandomi AH (2014) Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans 53:1168–1183. https://doi.org/10.1016/j.isatra.2014.03.018CrossRef

56.

Mohseni S, Gholami R, Zarei N, Zadeh AR (2014) Competition over resources: a new optimization algorithm based on animals behavioral ecology. Proc Int Conf Intell Netw Collab Syst. https://doi.org/10.1109/INCoS.2014.55CrossRef

57.

Ghaemi M, Feizi-Derakhshi MR (2014) Forest optimization algorithm. Expert Syst Appl 41:6676–6687. https://doi.org/10.1016/j.eswa.2014.05.009CrossRef

58.

Salimi H (2015) Stochastic fractal search: a powerful metaheuristic algorithm. Knowl-Based Syst 75:1–18. https://doi.org/10.1016/j.knosys.2014.07.025CrossRef

59.

Kuo HC, Lin CH (2013) Cultural evolution algorithm for global optimizations and its applications. J Appl Res Technol 11:510–522. https://doi.org/10.1016/S1665-6423(13)71558-XCrossRef

60.

Alabool HM, Alarabiat D, Abualigah L, Heidari AA (2021) Harris hawks optimization: a comprehensive review of recent variants and applications. Springer, London. https://doi.org/10.1007/s00521-021-05720-5CrossRef

61.

Yıldız AR, Yıldız BS, Sait SM, Li X (2019) The Harris hawks, grasshopper and multi-verse optimization algorithms for the selection of optimal machining parameters in manufacturing operations. Mater Test 61:725–733. https://doi.org/10.3139/120.111377CrossRef

62.

Abbasi A, Firouzi B, Sendur P (2021) On the application of Harris Hawks Optimization (HHO) algorithm to the design of microchannel heat sinks. Eng Comput 37:1409–1428. https://doi.org/10.1007/s00366-019-00892-0CrossRef

63.

Moayedi H, Osouli A, Nguyen H, Rashid ASA (2021) A novel Harris hawks’ Optimization and k-fold cross-validation predicting slope stability. Eng Comput 37:369–379. https://doi.org/10.1007/s00366-019-00828-8CrossRef

64.

Chen H, Heidari AA, Chen H, Wang M, Pan Z, Gandomi AH (2020) Multi-population differential evolution-assisted Harris hawks Optimization: framework and case studies. Futur Gener Comput Syst 111:175–198. https://doi.org/10.1016/j.future.2020.04.008CrossRef

65.

Firouzi B, Abbasi A, Sendur P (2021) Improvement of the computational efficiency of metaheuristic algorithms for Improvement of the computational efficiency of metaheuristic algorithms for the crack detection of cantilever beams using hybrid methods. Eng Optim. https://doi.org/10.1080/0305215X.2021.1919887CrossRef

66.

Wei Y, Lv H, Chen M, Wang M, Heidari AA, Chen H, Li C (2020) Predicting entrepreneurial intention of students: an extreme learning machine with gaussian barebone Harris Hawks optimizer. IEEE Access 8:76841–76855. https://doi.org/10.1109/ACCESS.2020.2982796CrossRef

67.

Qu C, He W, Peng X, Peng X (2020) Harris Hawks optimization with information exchange. Appl Math Model 84:52–75. https://doi.org/10.1016/j.apm.2020.03.024MathSciNetCrossRefMATH

68.

Elkadeem MR, Abd Elaziz M, Ullah Z, Wang S, Sharshir SW (2019) Optimal planning of renewable energy-integrated distribution system considering uncertainties. IEEE Access. 7:164887–164907. https://doi.org/10.1109/ACCESS.2019.2947308CrossRef

69.

Ebrahimzadeh R, Jampour M (2013) Chaotic genetic algorithm based on lorenz chaotic system for optimization problems. Int J Intell Syst Appl 5:19–24. https://doi.org/10.5815/ijisa.2013.05.03CrossRef

70.

Ji Y, Tu J, Zhou H, Gui W, Liang G, Chen H, Wang M (2020) An adaptive chaotic sine cosine algorithm for constrained and unconstrained optimization. Complexity. https://doi.org/10.1155/2020/6084917CrossRef

71.

Afrabandpey H, Ghaffari M, Mirzaei A, Safayani M (2014) A novel Bat Algorithm based on chaos for optimization tasks, 2014 Iran. Conf Intell Syst ICIS. https://doi.org/10.1109/IranianCIS.2014.6802527CrossRef

72.

Kohli M, Arora S (2018) Chaotic grey wolf optimization algorithm for constrained optimization problems. J Comput Des Eng 5:458–472. https://doi.org/10.1016/j.jcde.2017.02.005CrossRef

73.

Chuang LY, Hsiao CJ, Yang CH (2011) Chaotic particle swarm optimization for data clustering. Expert Syst Appl 38:14555–14563. https://doi.org/10.1016/j.eswa.2011.05.027CrossRef

74.

Kaur G, Arora S (2018) Chaotic whale optimization algorithm. J Comput Des Eng 5:275–284. https://doi.org/10.1016/j.jcde.2017.12.006CrossRef

75.

Ewees AA, Elaziz MA (2020) Performance analysis of Chaotic Multi-Verse Harris Hawks Optimization: a case study on solving engineering problems. Eng Appl Artif Intell 88:103370. https://doi.org/10.1016/j.engappai.2019.103370CrossRef

76.

Barshandeh S, Haghzadeh M (2020) A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems. Springer, London. https://doi.org/10.1007/s00366-020-00994-0CrossRef

77.

Dhawale D, Kamboj VK (2020) HHHO-IGWO: A new hybrid harris hawks optimizer for solving global optimization problems. Proc Int Conf Comput Autom Knowl Manag. https://doi.org/10.1109/ICCAKM46823.2020.9051509CrossRef

78.

Fu W, Shao K, Tan J, Wang K (2020) Fault diagnosis for rolling bearings based on composite multiscale fine-sorted dispersion entropy and SVM with hybrid mutation SCA-HHO algorithm optimization. IEEE Access 8:13086–13104. https://doi.org/10.1109/ACCESS.2020.2966582CrossRef

79.

Kamboj VK, Nandi A, Bhadoria A, Sehgal S (2020) An intensify Harris Hawks Optimizer for numerical and engineering optimization problems. Appl Soft Comput J 89:106018. https://doi.org/10.1016/j.asoc.2019.106018CrossRef

80.

Ridha HM, Heidari AA, Wang M, Chen H (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.2020.112660CrossRef

81.

Hu H, Ao Y, Bai Y, Cheng R, Xu T (2020) An improved Harris’s Hawks Optimization for SAR target recognition and stock market index prediction. IEEE Access 8:65891–65910. https://doi.org/10.1109/ACCESS.2020.2985596CrossRef

82.

Selim A, Kamel S, Alghamdi AS, Jurado F (2020) Optimal placement of DGs in distribution system using an improved harris hawks optimizer based on single- and multi-objective approaches. IEEE Access 8:52815–52829. https://doi.org/10.1109/ACCESS.2020.2980245CrossRef

83.

Jiao S, Chong G, Huang C, Hu H, Wang M, Heidari AA, Chen H, Zhao X (2020) Orthogonally adapted Harris Hawks Optimization for parameter estimation of photovoltaic models. Energy 203:117804. https://doi.org/10.1016/j.energy.2020.117804CrossRef

84.

Zhong C, Wang M, Dang C, Ke W, Guo S (2020) First-order reliability method based on Harris Hawks Optimization for high-dimensional reliability analysis. Struct Multidiscip Optim 62:1951–1968. https://doi.org/10.1007/s00158-020-02587-3MathSciNetCrossRef

85.

Fan Q, Chen Z, Xia Z (2020) A novel quasi-reflected Harris hawks optimization algorithm for global optimization problems. Soft Comput 24:14825–14843. https://doi.org/10.1007/s00500-020-04834-7CrossRef

86.

Essa FA, Abd Elaziz M, Elsheikh AH (2020) An enhanced productivity prediction model of active solar still using artificial neural network and Harris Hawks optimizer. Appl Therm Eng 170:115020. https://doi.org/10.1016/j.applthermaleng.2020.115020CrossRef

87.

Menesy AS, Sultan HM, Selim A, Ashmawy MG, Kamel S (2020) Developing and applying Chaotic Harris Hawks Optimization technique for extracting parameters of several proton exchange membrane fuel cell stacks. IEEE Access 8:1. https://doi.org/10.1109/ACCESS.2019.2961811CrossRef

88.

Yin Q, Cao B, Li X, Wang, B, Zhang, Q, Wei X (2020) An intelligent optimization algorithm for constructing a DNA storage code: NOL-HHO. Int J Mol Sci 21(6):2191

89.

Li C, Li J, Chen H (2020) A meta-heuristic-based approach for Qos-aware service composition. IEEE Access 8:69579–69592. https://doi.org/10.1109/ACCESS.2020.2987078CrossRef

90.

Shehabeldeen TA, Elaziz MA, Elsheikh AH, Zhou J (2019) Modeling of friction stir welding process using adaptive neuro-fuzzy inference system integrated with Harris Hawks Optimizer. J Mater Res Technol 8:5882–5892. https://doi.org/10.1016/j.jmrt.2019.09.060CrossRef

91.

Birogul S (2019) Hybrid harris hawk optimization based on differential evolution (HHODE) algorithm for optimal power flow problem. IEEE Access 7:184468–184488. https://doi.org/10.1109/ACCESS.2019.2958279CrossRef

92.

Moayedi H, Abdullahi MM, Nguyen H, Rashid ASA (2021) Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils. Eng Comput 37:437–447. https://doi.org/10.1007/s00366-019-00834-wCrossRef

93.

Rezaie H, Kazemi-Rahbar MH, Vahidi B, Rastegar H (2019) Solution of combined economic and emission dispatch problem using a novel chaotic improved harmony search algorithm. J Comput Des Eng 6:447–467. https://doi.org/10.1016/j.jcde.2018.08.001CrossRef

94.

Saxena A, Shekhawat S, Kumar R (2018) Application and development of enhanced chaotic grasshopper optimization algorithms. Model Simul Eng. https://doi.org/10.1155/2018/4945157CrossRef

95.

Chen H, Jiao S, Wang M, Heidari AA, Zhao X (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.2019.118778CrossRef

96.

Gao ZM, Zhao J, Hu YR, Chen HF (2019) The improved harris hawk optimization algorithm with the tent map. IEEE Int Conf Electron Inf Technol Comput Eng. https://doi.org/10.1109/EITCE47263.2019.9095091CrossRef

97.

Bednarz JC (1988) Cooperative hunting in Harris’ Hawks (Parabuteo unicinctus). Science (80-) 239:1525–1527. https://doi.org/10.1126/science.239.4847.1525CrossRef

98.

Wang J, Wang D (2008) Particle swarm optimization with a leader and followers. Prog Nat Sci 18:1437–1443. https://doi.org/10.1016/j.pnsc.2008.03.029CrossRef

99.

Xie J, Zhou YQ, Chen H (2013) A bat algorithm based on Lévy flights trajectory, Moshi Shibie Yu Rengong Zhineng/Pattern Recognit. Artif Intell 26:829–837

100.

Yang XS (2010) Firefly algorithm. In: Ch M (ed) Engineering optimization: an introduction with metaheuristic applications. John Wiley and Sons Inc, Hoboken, p 221CrossRef

101.

Kazarlis SA (1996) A genetic algorithm solution to the unit commitment problem. IEEE Trans Power Syst 11:83–92CrossRef

102.

Cuevas E, Echavarría A, Ramírez-Ortegón MA (2014) An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation. Appl Intell 40:256–272. https://doi.org/10.1007/s10489-013-0458-0CrossRef

103.

Yang XS, Karamanoglu M, He X (2014) Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim 46(9):1222–1237

104.

Jagodziński D, Arabas J (2017) A differential evolution strategy. In 2017 IEEE Congress on Evolutionary Computation (CEC), pp 1872–1876

105.

Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010CrossRef

106.

Dhawale D, Kamboj VK, Anand P (2021) An effective solution to numerical and multi-disciplinary design optimization problems using chaotic slime mold algorithm, Springer. London. https://doi.org/10.1007/s00366-021-01409-4CrossRef

107.

Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27:1053–1073. https://doi.org/10.1007/s00521-015-1920-1CrossRef

108.

Nezamabadi-pour H, Rostami-sharbabaki M, Maghfoori-Farsangi M (2008) Binary particle swarm optimization: challenges and new solutions. J Comput Soc Iran 6:21–32

109.

Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232CrossRefMATH

110.

John H (1992) Holland, adaptation in natural and artificial systems. MIT Press, Cambridge

111.

Nakamura RYM, Pereira LAM, Costa KA, Rodrigues D, Papa JP, Yang XS (2012) BBA: A binary bat algorithm for feature selection Brazilian Symp. Comput Graph Image Process. https://doi.org/10.1109/SIBGRAPI.2012.47CrossRef

112.

Storn R, Price K (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359. https://doi.org/10.1023/A:1008202821328MathSciNetCrossRefMATH

113.

Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29:17–35. https://doi.org/10.1007/s00366-011-0241-yCrossRef

114.

Ray T, Saini P (2001) Engineering design optimization using a swarm with an intelligent information sharing among individuals. Eng Optim 33:735–748. https://doi.org/10.1080/03052150108940941CrossRef

115.

Tsai JFA (2005) Global optimization of nonlinear fractional programming problems in engineering design. Eng Optim 37:399–409. https://doi.org/10.1080/03052150500066737MathSciNetCrossRef

116.

Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput J 13:2592–2612. https://doi.org/10.1016/j.asoc.2012.11.026CrossRef

117.

Zhao W, Zhang Z, Wang L (2020) Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications. Eng Appl Artif Intell 87:103300. https://doi.org/10.1016/j.engappai.2019.103300CrossRef

118.

Niu B, Li L (2008) A novel PSO-DE-Based hybrid algorithm for global optimization. Lect Notes Comput Sci. https://doi.org/10.1007/978-3-540-85984-0_20CrossRef

119.

Hameed IA, Bye RT, Osen OL (2016) Grey wolf optimizer (GWO) for automated offshore crane design. IEEE Symp Ser Comput Intell. https://doi.org/10.1109/SSCI.2016.7849998CrossRef

120.

Deb K, Goyal M (1996) A combined genetic adaptive search (GeneAS) for engineering design. Comput Sci Inf 26:30–45

121.

Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13:398–417. https://doi.org/10.1109/TEVC.2008.927706CrossRef

122.

Chickermane H, Gea HC (2002) Structural optimization using a new local approximation method. Int J Numer Methods Eng 39:829–846. https://doi.org/10.1002/(sici)1097-0207(19960315)39:5%3c829::aid-nme884%3e3.0.co;2-uMathSciNetCrossRefMATH

123.

He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20:89–99. https://doi.org/10.1016/j.engappai.2006.03.003CrossRef

124.

Mezura-Montes E, Coello Coello CA (2005) A simple multimembered evolution strategy to solve constrained optimization problems, IEEE Trans. Evol Comput 9:1–17. https://doi.org/10.1109/TEVC.2004.836819CrossRefMATH

125.

Deb K (1990) Optimal design of a class of welded structures via genetic algorithms. Collect Tech Pap AIAA/ASME/ASCE/AHS/ASC Struct Dyn Mater Conf. https://doi.org/10.2514/6.1990-1179CrossRef

126.

Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188:1567–1579. https://doi.org/10.1016/j.amc.2006.11.033MathSciNetCrossRefMATH

127.

Wu G, Pedrycz W, Suganthan PN, Mallipeddi R (2015) A variable reduction strategy for evolutionary algorithms handling equality constraints. Appl Soft Comput J 37:774–786. https://doi.org/10.1016/j.asoc.2015.09.007CrossRef

128.

Sadollah A, Eskandar H, Bahreininejad A, Kim JH (2015) Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems. Appl Soft Comput J 30:58–71. https://doi.org/10.1016/j.asoc.2015.01.050CrossRef

129.

Kamboj VK, Bhadoria A, Gupta N (2018) A novel hybrid GWO-PS algorithm for standard benchmark optimization problems. Ina Lett 3:217–241. https://doi.org/10.1007/s41403-018-0051-2CrossRef

130.

Lee KS, Geem ZW (2004) A new structural optimization method based on the harmony search algorithm. Comput Struct 82:781–798. https://doi.org/10.1016/j.compstruc.2004.01.002CrossRef

131.

Ragsdell KM, Phillips DT (1976) Optimal design of a class of welded structures using geometric programming. J Manuf Sci Eng Trans ASME 98:1021–1025. https://doi.org/10.1115/1.3438995CrossRef

132.

Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. CAD Comput Aided Des 43:303–315. https://doi.org/10.1016/j.cad.2010.12.015CrossRef

133.

Savsani P, Savsani V (2016) Passing vehicle search (PVS): a novel metaheuristic algorithm. Appl Math Model 40:3951–3978. https://doi.org/10.1016/j.apm.2015.10.040CrossRef

Titel: An improved Chaotic Harris Hawks Optimizer for solving numerical and engineering optimization problems
verfasst von: Dinesh Dhawale
Vikram Kumar Kamboj
Priyanka Anand
Publikationsdatum: 21.09.2021
Verlag: Springer London
Erschienen in: Engineering with Computers / Ausgabe 2/2023
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI: https://doi.org/10.1007/s00366-021-01487-4

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