nach oben

Engineering with Computers

Erschienen in:

23.04.2021 | Original Article

hSMA-PS: a novel memetic approach for numerical and engineering design challenges

verfasst von: Ardhala Bala Krishna, Sobhit Saxena, Vikram Kumar Kamboj

Erschienen in: Engineering with Computers | Ausgabe 4/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Recent work trend is to hybridize two and more variants in order to recognize better quality of functional and remedies to the global challenges of optimisation. The newly formed Slime Mould Algorithm (SMA) is premised upon naturally occurring slime mould oscillation feature. There is an effort to build a more effective way to accomplish exploration through process of exploitation. In a comprehensive collection of tests, the proposed hybrid Slime Mould Algorithm (SMA) – Pattern Search Algorithm (PS) (hSMA-PS) has been evaluated by comparing against accurate and reliable meta-heuristics for accuracy testing. In addition, nine classical engineering based optimization problems with design are used to guesstimate the algorithm’s efficacy in engineering based optimization challenges. The experiments demonstrate that the suggested algorithm enjoys efficiency, sometimes amazing result on sophisticated search landscapes.

Vorheriger Artikel VAE-TALSTM: a temporal attention and variational autoencoder-based long short-term memory framework for dam displacement prediction

Nächster Artikel A comparative study on the metaheuristic-based optimization of skew composite laminates

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren

Talbi ME (2009) Metaheuristics : from Design to Implementation Single solution-based metaheuristics, vol. 2009, no. 479

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

Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science. https://doi.org/10.1126/science.220.4598.671MathSciNetCrossRefMATH

Luo J, Chen H, Zhang Q, Xu Y, Huang H, Zhao X (2018) An improved grasshopper optimization algorithm with application to financial stress prediction. Appl Math Model 64(February 2019):654–668. https://doi.org/10.1016/j.apm.2018.07.044MathSciNetCrossRefMATH

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(February 2018):69–84. https://doi.org/10.1016/j.neucom.2017.04.060CrossRef

Zhang Q, Chen H, Luo J, Xu Y, Wu C, Li C (2018) Chaos enhanced bacterial foraging optimization for global optimization. IEEE Access 6:64905–64919. https://doi.org/10.1109/ACCESS.2018.2876996CrossRef

Mafarja M et al (2018) Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems. Knowledge-Based Syst 145:25–45. https://doi.org/10.1016/j.knosys.2017.12.037CrossRef

Mafarja M et al (2018) Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowledge-Based Syst. https://doi.org/10.1016/j.knosys.2018.08.003CrossRef

Aljarah I, Mafarja M, Heidari AA, Faris H, Zhang Y, Mirjalili S (2018) Asynchronous accelerating multi-leader salp chains for feature selection. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2018.07.040CrossRef

10.

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

11.

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

12.

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

13.

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

14.

Dhiman G, Garg M, Nagar A, Kumar V, Dehghani M (2020) A novel algorithm for global optimization: rat Swarm Optimizer. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02580-0CrossRef

15.

Askari Q, Younas I, Saeed M (2020) Political Optimizer: a novel socio-inspired meta-heuristic for global optimization. Knowledge-Based Syst. https://doi.org/10.1016/j.knosys.2020.105709CrossRef

16.

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

17.

Houssein EH, Saad MR, Hashim FA, Shaban H, Hassaballah M (2020) Lévy flight distribution: a new metaheuristic algorithm for solving engineering optimization problems. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2020.103731CrossRef

18.

Kaur S, Awasthi LK, Sangal AL, Dhiman G (2020) Tunicate Swarm Algorithm: a new bio-inspired based metaheuristic paradigm for global optimization. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2020.103541CrossRef

19.

Fouad MM, El-Desouky AI, Al-Hajj R, El-Kenawy ESM (2020) Dynamic group-based cooperative optimization algorithm. IEEE Access 8:148378–148403. https://doi.org/10.1109/ACCESS.2020.3015892CrossRef

20.

Kaveh A, Khanzadi M, Moghaddam MR (2020) Billiards-inspired optimization algorithm; a new meta-heuristic method. Structures 27:1722–1739. https://doi.org/10.1016/j.istruc.2020.07.058CrossRef

21.

Ahmadianfar I, Bozorg-Haddad O, Chu X (2020) Gradient-based optimizer: a new metaheuristic optimization algorithm. Inf Sci (Ny) 540:131–159. https://doi.org/10.1016/j.ins.2020.06.037MathSciNetCrossRefMATH

22.

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

23.

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

24.

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

25.

Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Computat 3(2):82–102CrossRef

26.

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

27.

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

28.

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

29.

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

30.

Mirjalili S (2016) SCA: A Sine Cosine Algorithm for solving optimization problems. Knowledge-Based Syst 96:120–133. https://doi.org/10.1016/j.knosys.2015.12.022CrossRef

31.

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

32.

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

33.

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

34.

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

35.

Pan WT (2012) A new Fruit Fly Optimization Algorithm: taking the financial distress model as an example. Knowledge-Based Syst 26:69–74. https://doi.org/10.1016/j.knosys.2011.07.001CrossRef

36.

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

37.

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

38.

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

39.

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

40.

Abedinpourshotorban H, Shamsuddin SM, 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

41.

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

42.

Kennedy J, Eberhart R (1995) Particle swarm optimization

43.

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

44.

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

45.

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

46.

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

47.

Yu JJQ, Li VOK (2015) A social spider algorithm for global optimization. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2015.02.014CrossRef

48.

Kiran MS (2015) TSA: tree-seed algorithm for continuous optimization. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2015.04.055CrossRef

49.

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

50.

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

51.

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), pp 162–167, https://doi.org/10.1109/TENSYMP46218.2019.8971231

52.

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

53.

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

54.

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), pp 226–230, https://doi.org/10.1109/ICAICA.2019.8873515.

55.

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

56.

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

57.

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

58.

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

59.

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

60.

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

61.

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

62.

Mohammadi-Balani A, Dehghan Nayeri M, Azar A, Taghizadeh-Yazdi M (2021) Golden eagle optimizer: a nature-inspired metaheuristic algorithm. Comput Ind Eng 152:1–45. https://doi.org/10.1016/j.cie.2020.107050CrossRef

63.

Debnath S, Arif W, Baishya S (2020) Buyer Inspired Meta-Heuristic Optimization Algorithm. Open Comput Sci 10(1):194–219CrossRef

64.

Zitouni F, Harous S, Maamri R (2020) The Solar System Algorithm: a novel metaheuristic method for global optimization. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3047912CrossRef

65.

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

66.

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

67.

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

68.

Kumar M, Kulkarni AJ, Satapathy SC (2018) Socio evolution and learning optimization algorithm: a socio-inspired optimization methodology. Futur Gener Comput Syst 81:252–272. https://doi.org/10.1016/j.future.2017.10.052CrossRef

69.

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

70.

Abdel-Basset M, Chang V, Mohamed R (2020) HSMA_WOA: a hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images. Appl Soft Comput J 95:106642. https://doi.org/10.1016/j.asoc.2020.106642CrossRef

71.

Naik MK, Panda R, Abraham A (2020) Normalized square difference based multilevel thresholding technique for multispectral images using leader slime mould algorithm. J King Saud Univ - Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2020.10.030CrossRef

72.

Zhao J, Gao Z, Sun W (2020) The improved slime mould algorithm with Levy flight. J Phys. https://doi.org/10.1088/1742-6596/1617/1/012033CrossRef

73.

Durmus A (2020) The optimal synthesis of thinned concentric circular antenna arrays using slime mold algorithm. Electromagnetics 40(8):541–553. https://doi.org/10.1080/02726343.2020.1838044CrossRef

74.

Ekinci S, Izci D, Zeynelgil HL, Orenc S (2020) An application of slime mould algorithm for optimizing parameters of power system stabilizer. pp 1–5, https://doi.org/10.1109/ismsit50672.2020.9254597.

75.

Gao Z, Zhao J, Yang Y, Tian X (2020) The hybrid grey wolf optimization-slime mould algorithm. J Phys. https://doi.org/10.1088/1742-6596/1617/1/012034CrossRef

76.

Gao Z, Zhao J, Li S (2020) The improved slime mould algorithm with cosine controlling parameters the improved slime mould algorithm with cosine controlling parameters. J Phys. https://doi.org/10.1088/1742-6596/1631/1/012083CrossRef

77.

Zhao J, Gao Z-M (2020) The chaotic slime mould algorithm with chebyshev map. J Phys Conf Ser 1631:012071. https://doi.org/10.1088/1742-6596/1631/1/012071CrossRef

78.

Wahid F et al (2020) An enhanced firefly algorithm using pattern search for solving optimization problems. IEEE Access 8:148264–148288. https://doi.org/10.1109/ACCESS.2020.3015206CrossRef

79.

Wahid F, Ghazali R (2019) Hybrid of firefly algorithm and pattern search for solving optimization problems. Evol Intell. https://doi.org/10.1007/s12065-018-0165-1CrossRef

80.

Vaz AIF, Vicente LN (2007) A particle swarm pattern search method for bound constrained global optimization. J Glob Optim 39(2):197–219. https://doi.org/10.1007/s10898-007-9133-5MathSciNetCrossRefMATH

81.

Kamboj VK, Bhadoria A, Gupta N (2018) A Novel Hybrid GWO-PS Algorithm for Standard Benchmark Optimization Problems. Ina Lett 3(4):217–241. https://doi.org/10.1007/s41403-018-0051-2CrossRef

82.

Krishna AB, Saxena S, Kamboj VK (2021) A novel statistical approach to numerical and multidisciplinary design optimization problems using pattern search inspired Harris hawks optimizer. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05475-5CrossRef

83.

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

84.

Howard FL (1931) The life history of Physarum polycephalum. Am J Bot 18(2):116–133. https://doi.org/10.1002/j.1537-2197.1931.tb09577.xCrossRef

85.

Kessler D (1982) Plasmodial structure and motility. In: Cell biology of Physarum and didymium

86.

Nakagaki T, Yamada H, Ueda T (2000) Interaction between cell shape and contraction pattern in the Physarum plasmodium. Biophys Chem. https://doi.org/10.1016/S0301-4622(00)00108-3CrossRef

87.

Kareiva P, Odell G (1987) Swarms of predators exhibit ‘preytaxis’ if individual predators use area-restricted search. Am Nat. https://doi.org/10.1086/284707CrossRef

88.

Latty T, Beekman M (2009) Food quality affects search strategy in the acellular slime mould, Physarum polycephalum. Behav Ecol 20(6):1160–1167. https://doi.org/10.1093/beheco/arp111CrossRef

89.

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

90.

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

91.

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

92.

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), pp 52–57, https://doi.org/10.1109/ICCAKM46823.2020.9051509

93.

Yang XS (2012) Flower pollination algorithm for global optimization. https://doi.org/10.1007/978-3-642-32894-7_27

94.

Yue X, Zhang H, Yu H (2020) A hybrid grasshopper optimization algorithm with invasive weed for global optimization. IEEE Access 8:5928–5960. https://doi.org/10.1109/ACCESS.2019.2963679CrossRef

95.

Yang XS (2010) Firefly algorithm, Lévy flights and global optimization. https://doi.org/10.1007/978-1-84882-983-1-15

96.

Wang M, Heidari AA, Chen M, Chen H, Zhao X, Cai X (2020) Exploratory differential ant lion-based optimization. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.113548CrossRef

97.

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

98.

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

99.

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

100.

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

101.

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

102.

Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2009.08.031CrossRef

103.

Wang Z, Luo Q, Zhou Y (2020) Hybrid metaheuristic algorithm using butterfly and flower pollination base on mutualism mechanism for global optimization problems, no. 0123456789. Springer, London

104.

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

105.

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

106.

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

107.

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

108.

Kaveh A, Talatahari S (2010) An improved ant colony optimization for constrained engineering design problems. Eng Comput (Swansea, Wales). https://doi.org/10.1108/02644401011008577CrossRefMATH

109.

Pelusi D, Mascella R, Tallini L, Nayak J, Naik B, Deng Y (2020) An Improved Moth-Flame Optimization algorithm with hybrid search phase. Knowledge-Based Syst. https://doi.org/10.1016/j.knosys.2019.105277CrossRef

110.

Rajeswara Rao B, Tiwari R (2007) Optimum design of rolling element bearings using genetic algorithms. Mech Mach Theory. https://doi.org/10.1016/j.mechmachtheory.2006.02.004CrossRefMATH

111.

Savsani P, Savsani V (2016) Passing vehicle search (PVS): A novel metaheuristic algorithm. Appl Math Model 40(5–6):3951–3978. https://doi.org/10.1016/j.apm.2015.10.040CrossRef

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.

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

114.

Zolghadr-Asli B, Bozorg-Haddad O, Chu X (2018) Crow search algorithm (CSA). In: Studies in Computational Intelligence

115.

Chlckermane H, Gea HC (1996) Structural optimization using a new local approximation method. Int J Numer Methods Eng. https://doi.org/10.1002/(sici)1097-0207(19960315)39:5%3c829::aid-nme884%3e3.0.co;2-uMathSciNetCrossRef

116.

Chen H, Xu Y, Wang M, Zhao X (2019) A balanced whale optimization algorithm for constrained engineering design problems. Appl Math Model 71:45–59. https://doi.org/10.1016/j.apm.2019.02.004MathSciNetCrossRefMATH

117.

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

118.

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

119.

Chegini SN, Bagheri A, Najafi F (2018) PSOSCALF: A new hybrid PSO based on Sine Cosine Algorithm and Levy flight for solving optimization problems. Appl Soft Comput J 73:697–726. https://doi.org/10.1016/j.asoc.2018.09.019CrossRef

Titel: hSMA-PS: a novel memetic approach for numerical and engineering design challenges
verfasst von: Ardhala Bala Krishna
Sobhit Saxena
Vikram Kumar Kamboj
Publikationsdatum: 23.04.2021
Verlag: Springer London
Erschienen in: Engineering with Computers / Ausgabe 4/2022
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI: https://doi.org/10.1007/s00366-021-01371-1

Neuer Inhalt

Bildnachweise

VDI-Icon, Profil Icon, inhalt2, Springer Professional Modul/© Springer Fachmedien Wiesbaden GmbH, Die Gewinner und Laudatoren des Sustainability Award in Automotive 2024/© Uli Regenscheit | ATZlive, Search Icon, Banner Hanser, Dr. Fabian Struck/© Forto Logistics SE & Co., Bau Immobilie/© Gina Sanders / Fotolia, Kundenpotenzial/© Andrii Yalanskyi / Getty Images / iStock, Zeitschrift Wissensmanagement Cover, PatentFit-Logo/© Springer Fachmedien Wiesbaden GmbH, ATZ-Webinar: Prototypenfreie Entwicklung durch Offline- und Driver-in-the-Loop-HiL-Tests /© (c) VI-grade, chassis.tech plus 2023/© [M] ATZlive / TÜV SÜD PRODUCT SERVICE GMBH, adäsion-Webinar-Matinee/© krystiannawrocki_ Getty Images

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 4/2022

Three novel truly-explicit time-marching procedures considering adaptive dissipation control

Voronoi-FDEM concept for modelling post-fracture response of progressively damaged structural glass

Optimization of weight and collapse energy of space structures using the multi-objective modified crow search algorithm

Effect of parametric enhancements on naked mole-rat algorithm for global optimization

Characteristics of melting heat transport of blood with time-dependent cross-nanofluid model using Keller–Box and BVP4C method

VAE-TALSTM: a temporal attention and variational autoencoder-based long short-term memory framework for dam displacement prediction

Neuer Inhalt

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.