Top

Neural Computing and Applications

Published in:

03-01-2021 | Original Article

A novel statistical approach to numerical and multidisciplinary design optimization problems using pattern search inspired Harris hawks optimizer

Authors: Ardhala Bala Krishna, Sobhit Saxena, Vikram Kumar Kamboj

Published in: Neural Computing and Applications | Issue 12/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Classical Harris Hawks optimiser (HHO) algorithm has a notable approach for global optimization. However, for constrained engineering optimization problems it is easily to get stuck in local search space. To step up the global search process of the current Harris hawks optimiser and hang it out of the local search space, the research framework purpose is to identify the discovery process of the current optimiser; the Harris hawks optimiser novel version was implemented using the pattern search algorithm named as the hybrid Harris hawks pattern search algorithm (hHHO-PS). The efficiency of approached optimiser has also been evaluated for different problems of non-convex, nonlinear and highly constrained engineering optimal complications. To confirm performance of suggested algorithm, consideration was given to 23 standard CEC2005 benchmark issues and nine multidisciplinary engineering design optimization problems. After testing, the efficacy of approaching hHHO-PS optimization algorithm has been found to be much stronger than the traditional Harris hawks optimiser, gray wolf optimiser, ant lion optimiser and moth flame optimization and other currently documented heuristics, metaheuristics and hybrid form optimization approaches, and the suggested methodology endorses its efficacy in problems of multidisciplinary nature and engineering optimization.

previous article CSCF: a chaotic sine cosine firefly algorithm for practical application problems

next article Machine learning models for ecological footprint prediction based on energy parameters

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Abbassi R, Abbassi A, Asghar A, Mirjalili S (2019) An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy Convers Manag 179:362–372. https://doi.org/10.1016/j.enconman.2018.10.069CrossRef

Faris H et al (2019) An intelligent system for spam detection and identification of the most relevant features based on evolutionary Random Weight Networks. Inf Fusion 48:67–83. https://doi.org/10.1016/j.inffus.2018.08.002CrossRef

Torczon V (1997) On the convergence of pattern search algorithms. SIAM J Optim 7(1):1–25. https://doi.org/10.1137/S1052623493250780MathSciNetCrossRefMATH

Wu G (2016) Across neighborhood search for numerical optimization. Inf Sci (NY). https://doi.org/10.1016/j.ins.2015.09.051CrossRefMATH

McCarthy JF (1989) Block-conjugate-gradient method. Phys Rev D 40(6):2149–2152. https://doi.org/10.1103/PhysRevD.40.2149CrossRef

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

Mafarja M et al (2018) Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowl Based Syst 145:25–45. https://doi.org/10.1016/j.knosys.2017.12.037CrossRef

Asghar A, Rahim H, Abbaspour A, Rezaee A (2015) An efficient chaotic water cycle algorithm for optimization tasks. https://doi.org/10.1007/s00521-015-2037-2

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

10.

Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Futur Gener Comput Syst 111:300–323. https://doi.org/10.1016/j.future.2020.03.055CrossRef

11.

Eiben AE, Smith JE (2003) Evolutionary programming. In: Introduction to evolutionary computing. Natural computing series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-05094-1_5CrossRefMATH

12.

Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513. https://doi.org/10.1007/s00521-015-1870-7CrossRef

13.

Glover Fred (1989) Tabu search—part I. Orsa J Comput 1(3):190–206CrossRef

14.

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

15.

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

16.

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

17.

Rao RV, Savsani VJ, Vakharia DP (2012) Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci (NY) 183(1):1–15. https://doi.org/10.1016/j.ins.2011.08.006MathSciNetCrossRef

18.

Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–72. https://doi.org/10.1038/scientificamerican0792-66CrossRef

19.

Liu Y, Li R (2020) PSA: a photon search algorithm. J Inf Process Syst 16(2):478–493

20.

Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim. https://doi.org/10.1007/s10898-007-9149-xMathSciNetCrossRefMATH

21.

Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68. https://doi.org/10.1177/003754970107600201CrossRef

22.

Koza JR, Rice JP (1992) Automatic programming of robots using genetic programming. In: Proceeding of AAAI-92, SanJose, CA, pp 194–201

23.

Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech. https://doi.org/10.1007/s00707-009-0270-4CrossRefMATH

24.

Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26:69–74. https://doi.org/10.1016/j.knosys.2011.07.001CrossRef

25.

He S, Wu QH, Saunders JR (2009) Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evol Comput. https://doi.org/10.1109/TEVC.2009.2011992CrossRef

26.

Hansen N, Müller SD, Koumoutsakos P (2003) Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol Comput. https://doi.org/10.1162/106365603321828970CrossRef

27.

Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci (NY) 179(13):2232–2248. https://doi.org/10.1016/j.ins.2009.03.004CrossRefMATH

28.

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

29.

Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713. https://doi.org/10.1109/TEVC.2008.919004CrossRef

30.

Abedinpourshotorban H, Mariyam Shamsuddin S, Beheshti Z, Jawawi DNA (2016) Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evol Comput. https://doi.org/10.1016/j.swevo.2015.07.002CrossRef

31.

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

32.

Formato RA (2007) Central force optimization: a new metaheuristic with applications in applied electromagnetics. Prog Electromagn Res. https://doi.org/10.2528/PIER07082403CrossRef

33.

Kennedy J, Eberhart R (1995) Particle swarm optimization

34.

Tabari A, Ahmad A (2017) A new optimization method: electro-search algorithm. Comput Chem Eng. https://doi.org/10.1016/j.compchemeng.2017.01.046CrossRef

35.

Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39. https://doi.org/10.1109/MCI.2006.329691CrossRef

36.

Xu Z et al (2020) Orthogonally-designed adapted grasshopper optimization: a comprehensive analysis. Expert Syst Appl 150:113282. https://doi.org/10.1016/j.eswa.2020.113282CrossRef

37.

Banerjee N, Mukhopadhyay S (2019) HC-PSOGWO: hybrid crossover oriented PSO and GWO based co-evolution for global optimization. In: 2019 IEEE region 10 symposium (TENSYMP), June 2019, pp 162–167. https://doi.org/10.1109/tensymp46218.2019.8971231

38.

Shahrouzi M, Salehi A (2020) Imperialist competitive learner-based optimization: a hybrid method to solve engineering problems. Int J Optim Civ Eng 10(1):155–180

39.

Sulaiman MH, Mustaffa Z, Saari MM, Daniyal H, Musirin I, Daud MR (2018) Barnacles mating optimizer: an evolutionary algorithm for solving optimization. In: 2018 IEEE international conference on automatic control and intelligent systems (I2CACIS), October 2018, pp 99–104. https://doi.org/10.1109/i2cacis.2018.8603703

40.

Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2020) Equilibrium optimizer: a novel optimization algorithm. Knowl Based Syst 191:105190. https://doi.org/10.1016/j.knosys.2019.105190CrossRef

41.

Muhammed DA, Saeed SAM, Rashid TA (2020) Improved fitness-dependent optimizer algorithm. IEEE Access 8:19074–19088. https://doi.org/10.1109/ACCESS.2020.2968064CrossRef

42.

Mostafa Bozorgi S, Yazdani S (2019) IWOA: an improved whale optimization algorithm for optimization problems. J Comput Des Eng 6(3):243–259. https://doi.org/10.1016/j.jcde.2019.02.002CrossRef

43.

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.2019.124872MathSciNetCrossRefMATH

44.

Yimit A, Iigura K, Hagihara Y (2020) Refined selfish herd optimizer for global optimization problems. Expert Syst Appl 139:112838. https://doi.org/10.1016/j.eswa.2019.112838CrossRef

45.

Zhao W, Wang L, Zhang Z (2019) Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04452-xCrossRef

46.

Seyyedabbasi A, Kiani F (2019) I-GWO and Ex-GWO: improved algorithms of the grey wolf optimizer to solve global optimization problems. Eng Comput 10:45. https://doi.org/10.1007/s00366-019-00837-7CrossRef

47.

Khatri A, Gaba A, Rana KPS, Kumar V (2019) A novel life choice-based optimizer. Soft Comput. https://doi.org/10.1007/s00500-019-04443-zCrossRef

48.

Tejani GG, Kumar S, Gandomi AH (2019) Multi-objective heat transfer search algorithm for truss optimization. Eng Comput. https://doi.org/10.1007/s00366-019-00846-6CrossRef

49.

Xiao B, Wang R, Xu Y, Wang J, Song W, Deng Y (2019) Simplified salp swarm algorithm. In 2019 IEEE international conference on artificial intelligence and computer applications (ICAICA), March 2019, pp 226–230. https://doi.org/10.1109/icaica.2019.8873515

50.

Chen X, Tianfield H, Li K (2019) Self-adaptive differential artificial bee colony algorithm for global optimization problems. Swarm Evol Comput 45:70–91. https://doi.org/10.1016/j.swevo.2019.01.003CrossRef

51.

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

52.

Dhawale D, Kamboj VK (2020) hHHO-IGWO: a new hybrid Harris Hawks Optimizer for solving global optimization problems. In: 2020 international conference on computation, automation and knowledge management (ICCAKM), January 2020, pp 52–57. https://doi.org/10.1109/iccakm46823.2020.9051509

53.

Bui DT et al (2019) A novel swarm intelligence-Harris Hawks optimization for spatial assessment of landslide susceptibility. Sensors (Basel) 19(16):3590. https://doi.org/10.3390/s19163590CrossRef

54.

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

55.

Jia H, Lang C, Oliva D, Song W, Peng X (2019) Dynamic Harris Hawks optimization with mutation mechanism for satellite image segmentation. Remote Sens. https://doi.org/10.3390/rs11121421CrossRef

56.

Too J, Abdullah AR, Saad NM (2019) A new quadratic binary Harris Hawk optimization for feature selection. Electron. https://doi.org/10.3390/electronics8101130CrossRef

57.

Ghafil HN, Jármai K (2020) Dynamic differential annealed optimization: new metaheuristic optimization algorithm for engineering applications. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2020.106392CrossRefMATH

58.

Hussien AG, Hassanien AE, Houssein EH, Azar AT (2019) New binary whale optimization algorithm for discrete optimization problems. Eng Optim. https://doi.org/10.1080/0305215X.2019.1624740CrossRef

59.

Hans R, Kaur H (2020) Opposition-based enhanced grey wolf optimization algorithm for feature selection in breast density classification. 10(3). https://doi.org/10.18178/ijmlc.2020.10.3.957

60.

Hashim FA, Houssein EH, Mabrouk MS, Al-atabany W (2019) Henry gas solubility optimization: a novel physics-based algorithm. Future Gener Comput Syst 101:646–667. https://doi.org/10.1016/j.future.2019.07.015CrossRef

61.

Le-Duc T, Nguyen QH, Nguyen-Xuan H (2020) Balancing composite motion optimization. Inf Sci (NY) 520:250–270. https://doi.org/10.1016/j.ins.2020.02.013MathSciNetCrossRef

62.

Khalilpourazari S, Khalilpourazary S (2017) An efficient hybrid algorithm based on water cycle and moth-flame optimization algorithms for solving numerical and constrained engineering optimization problems. Soft Comput. https://doi.org/10.1007/s00500-017-2894-yCrossRef

63.

Kamboj VK (2015) A novel hybrid PSO—GWO approach for unit commitment problem. Neural Comput Appl. https://doi.org/10.1007/s00521-015-1962-4CrossRef

64.

Kamboj VK, Bhadoria A, Gupta N (2018) A novel hybrid GWO-PS algorithm for standard benchmark optimization problems. INAE Lett 3(4):217–241. https://doi.org/10.1007/s41403-018-0051-2CrossRef

65.

Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70. https://doi.org/10.1016/j.advengsoft.2017.05.014CrossRef

66.

Dong W, Kang L, Zhang W (2017) Opposition-based particle swarm optimization with adaptive mutation strategy. Soft Comput 21(17):5081–5090. https://doi.org/10.1007/s00500-016-2102-5CrossRef

67.

Bui DT et al (2019) A novel swarm intelligence—Harris Hawks. Sensors 19(16):3590. https://doi.org/10.3390/s19163590CrossRef

68.

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

69.

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. https://doi.org/10.1007/s00521-015-1920-1MathSciNetCrossRef

70.

Bayraktar Z, Komurcu M, Werner DH (2010) Wind driven optimization (WDO): a novel nature-inspired optimization algorithm and its application to electromagnetics. In: 2010 IEEE international symposium on antennas and propagation and CNC-USNC/URSI radio science meeting-leading the wave, AP-S/URSI 2010, no. 1, pp 0–3, 2010. https://doi.org/10.1109/aps.2010.5562213

71.

Chaohua D, Weirong C, Yunfang Z (2006) Seeker optimization algorithm. In: 2006 international conference on computational intelligence and security, vol 1, pp 225–229, 2006. https://doi.org/10.1109/iccias.2006.294126

72.

Gandomi AH (2014) Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans 53(4):1168–1183. https://doi.org/10.1016/j.isatra.2014.03.018CrossRef

73.

Li X, Zhang J, Yin M (2014) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Appl 24(7–8):1867–1877. https://doi.org/10.1007/s00521-013-1433-8CrossRef

74.

Li MD, Zhao H, Weng XW, Han T (2016) A novel nature-inspired algorithm for optimization: virus colony search. Adv Eng Softw 92:65–88. https://doi.org/10.1016/j.advengsoft.2015.11.004CrossRef

75.

Meng XB, Gao XZ, Lu L, Liu Y, Zhang H (2016) A new bio-inspired optimisation algorithm: bird swarm algorithm. J Exp Theor Artif Intell 28(4):673–687. https://doi.org/10.1080/0952813X.2015.1042530CrossRef

76.

Merrikh-Bayat F (2015) The runner-root algorithm: a metaheuristic for solving unimodal and multimodal optimization problems inspired by runners and roots of plants in nature. Appl Soft Comput J 33:292–303. https://doi.org/10.1016/j.asoc.2015.04.048CrossRef

77.

Nakamura RYM, Pereira LAM, Costa KA, Rodrigues D, Papa JP, Yang XS (2012) BBA: a binary bat algorithm for feature selection. In: Brazilian symposium on computer graphics and image processing, pp 291–297, 2012. https://doi.org/10.1109/sibgrapi.2012.47

78.

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(5):2592–2612. https://doi.org/10.1016/j.asoc.2012.11.026CrossRef

79.

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

80.

Wang GG, Deb S, Gao XZ, Dos Santos Coelho L (2016) A new metaheuristic optimisation algorithm motivated by elephant herding behaviour. Int J Bio Inspired Comput 8(6):394–409. https://doi.org/10.1504/ijbic.2016.081335CrossRef

81.

Yang XS (2010) A new metaheuristic bat-inspired algorithm. Stud Comput Intell 284:65–74. https://doi.org/10.1007/978-3-642-12538-6_6CrossRefMATH

82.

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

83.

Li M, Zhao H, Weng X, Han T (2016) Cognitive behavior optimization algorithm for solving optimization problems. Appl Soft Comput J 39:199–222. https://doi.org/10.1016/j.asoc.2015.11.015CrossRef

84.

Mareli M, Twala B (2018) An adaptive Cuckoo search algorithm for optimisation. Appl Comput Inform 14(2):107–115. https://doi.org/10.1016/j.aci.2017.09.001CrossRef

85.

Ghaemi M, Feizi-Derakhshi MR (2014) Forest optimization algorithm. Expert Syst Appl 41(15):6676–6687. https://doi.org/10.1016/j.eswa.2014.05.009CrossRef

86.

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. https://doi.org/10.1016/j.future.2020.04.008CrossRef

87.

Hussain K, Zhu W, Mohd Salleh MN (2019) Long-term memory Harris’ Hawk optimization for high dimensional and optimal power flow problems. IEEE Access 7:147596–147616. https://doi.org/10.1109/access.2019.2946664CrossRef

88.

Houssein EH, Saad MR, Hussain K, Zhu W, Shaban H, Hassaballah M (2020) Optimal sink node placement in large scale wireless sensor networks based on Harris’ Hawk optimization algorithm. IEEE Access 8:19381–19397. https://doi.org/10.1109/ACCESS.2020.2968981CrossRef

89.

Wei Y et al (2020) Predicting entrepreneurial intention of students: an extreme learning machine with gaussian Barebone Harris Hawks optimizer. IEEE Access. https://doi.org/10.1109/access.2020.2982796CrossRef

90.

Yu Z, Shi X, Zhou J, Chen X, Qiu X (2020) Effective assessment of blast-induced ground vibration using an optimized random forest model based on a Harris Hawks optimization algorithm. Appl Sci. https://doi.org/10.3390/app10041403CrossRef

91.

Attiya I, Abd Elaziz M, Xiong S (2020) Job scheduling in cloud computing using a modified Harris Hawks optimization and simulated annealing algorithm. Comput Intell Neurosci. https://doi.org/10.1155/2020/3504642CrossRef

92.

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

93.

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

94.

Singh S, Jain A, Mahla SK (2020) An extended artificial neural network assisted hybrid Harris Hawks and whale optimizer to find optimal solution for engineering design problems. 6:4843–4855. https://doi.org/10.35940/ijrte.f8189.038620

95.

Wang G, Deb S, Cui Z (2015) Monarch butterfly optimization. Neural Comput Appl. https://doi.org/10.1007/s00521-015-1923-yCrossRef

96.

Arora S, Anand P (2018) Chaotic grasshopper optimization algorithm for global optimization. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3343-2CrossRef

97.

Jain M, Singh V, Rani A (2017) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol Comput. https://doi.org/10.1016/j.swevo.2018.02.013CrossRef

98.

Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82. https://doi.org/10.1109/4235.585893CrossRef

99.

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

100.

Sims DW et al (2008) Scaling laws of marine predator search behaviour. https://doi.org/10.1038/nature06518

101.

Gautestad AO, Mysterud I (2006) Complex animal distribution and abundance from memory-dependent kinetics. 3:44–55. https://doi.org/10.1016/j.ecocom.2005.05.007

102.

Viswanathan GM, Afanasyev V, Buldyrev SV, Havlin S, da Luz MGE, Raposo EP, Stanley HE (2000) Lévy ights in random searches. Phys A Stat Mech Appl 282:1–12CrossRef

103.

Yang X-S (2010) Nature-inspired metaheuristic algorithms. Luniver Press

104.

Digalakis JG, Margaritis KG (2001) On benchmarking functions for genetic algorithms. Int J Comput Math 77(4):481–506. https://doi.org/10.1080/00207160108805080MathSciNetCrossRefMATH

105.

Kamboj VK (2019) GWO-SA: a novel hybrid grey wolf optimizer-simulated annealing algorithm for multidisciplinary design optimization problems. Int J Rec Technol Eng 8(4):1279–1299. https://doi.org/10.35940/ijrte.c6735.118419CrossRef

106.

Abd Elaziz M, Oliva D, Xiong S (2017) An improved opposition-based sine cosine algorithm for global optimization. Expert Syst Appl 90:484–500. https://doi.org/10.1016/j.eswa.2017.07.043CrossRef

107.

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. https://doi.org/10.1016/j.advengsoft.2017.07.002CrossRef

108.

Qais MH, Hasanien HM, Alghuwainem S (2020) Transient search optimization: a new meta-heuristic optimization algorithm. Appl Intell. https://doi.org/10.1007/s10489-020-01727-yCrossRef

109.

Debnath S, Arif W, Baishya S (2020) Buyer inspired meta-heuristic optimization algorithm. Open Comput Sci 10:194–219. https://doi.org/10.1515/comp-2020-0101CrossRef

110.

Chen H, Heidari AA, Zhao X, Zhang L, Chen H (2020) Advanced orthogonal learning-driven multi-swarm sine cosine optimization: framework and case studies. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2019.113113CrossRef

111.

Rahkar Farshi T (2020) Battle Royale optimization algorithm. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05004-4CrossRef

112.

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

113.

Gandomi AH, Yang XS (2011) Benchmark problems in structural optimization. Stud Comput Intell. https://doi.org/10.1007/978-3-642-20859-1_12CrossRefMATH

114.

Mezura-Montes E, Coello Coello CA (2005) Useful infeasible solutions in engineering optimization with evolutionary algorithms. https://doi.org/10.1007/11579427_66

115.

Hameed IA, Bye RT, Osen OL (2016) Grey wolf optimizer (GWO) for automated offshore crane design. In: 2016 IEEE symposium series on computational intelligence (SSCI), December 2016, pp 1–6. https://doi.org/10.1109/ssci.2016.7849998

116.

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

117.

Ariables V (2015) The Butterfly-Particle Swarm Optimization (Butterfly-PSO/BF-PSO) technique and ITS 4(3):23–39

118.

Cagnina L, Esquivel S, Coello C (2008) Solving engineering optimization problems with the simple constrained particle swarm optimizer. Informatica (Slovenia) 32:319–326MATH

119.

Raglend IJ, Kumar R, Karthikeyan SP Deregulated environment

120.

Virmani S, Adrian EC, Imhof K, Mukherjee S (1989) Implementation of a Lagrangian relaxation based unit commitment problem. IEEE Trans Power Syst 4(4):1373–1380. https://doi.org/10.1109/59.41687CrossRef

121.

Cohen AI, Yoshimura M (1983) A branch-and-bound algorithm for unit commitment. IEEE Trans Power Appar Syst 102(2):444–451CrossRef

122.

Bhadoria A, Kamboj VK (2019) Optimal generation scheduling and dispatch of thermal generating units considering impact of wind penetration using hGWO-RES algorithm. Appl Intell. https://doi.org/10.1007/s10489-018-1325-9CrossRef

123.

Abderazek H, Ferhat D, Ivana A (2017) Adaptive mixed differential evolution algorithm for bi-objective tooth profile spur gear optimization. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-016-9523-2CrossRef

124.

Gupta S, Deep K, Mirjalili S, Kim JH (2020) A modified sine cosine algorithm with novel transition parameter and mutation operator for global optimization. Expert Syst Appl 154:113395. https://doi.org/10.1016/j.eswa.2020.113395CrossRef

125.

Deb K (1996) A combined genetic adaptive search (GeneAS) for engineering design. 26:30–45

126.

Cuevas E, Echavarría A, Ramirez-Ortegon M (2013) An optimization algorithm inspired by the states of matter that improves the balance between exploration and exploitation. Appl Intell. https://doi.org/10.1007/s10489-013-0458-0CrossRef

127.

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

128.

Shankar K, Eswaran P (2016) RGB-based secure share creation in visual cryptography using optimal elliptic curve cryptography technique. J Circuits Syst Comput 25(11):1650138. https://doi.org/10.1142/s0218126616501383CrossRef

129.

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

130.

Deb K, Goyal M (1996) A combined genetic adaptive search (GeneAS) for engineering design. Comput Sci Inf. citeulike-article-id:9625478

131.

Coello Coello CA, Christiansen AD (1999) Moses: a multiobjective optimization tool for engineering design. Eng Optim 31(1–3):337–368. https://doi.org/10.1080/03052159908941377CrossRef

132.

Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35. https://doi.org/10.1007/s00366-011-0241-yCrossRef

Title: A novel statistical approach to numerical and multidisciplinary design optimization problems using pattern search inspired Harris hawks optimizer
Authors: Ardhala Bala Krishna
Sobhit Saxena
Vikram Kumar Kamboj
Publication date: 03-01-2021
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 12/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05475-5

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 12/2021

The Mode-Fisher pooling for time complexity optimization in deep convolutional neural networks

Hybrid crow search and uniform crossover algorithm-based clustering for top-N recommendation system

Multiple delay-dependent finite-time boundedness and input–output finite-time mean square stabilization of uncertain semi-Markovian jump systems with input constraint

Robust and high-order correlation alignment for unsupervised domain adaptation

Applications of metaheuristic algorithms for optimal operation of cascaded hydropower plants

Improved coral reefs optimization with adaptive -hill climbing for feature selection

Premium Partner