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Soft Computing

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03.12.2014 | Methodologies and Application

Hybridizing harmony search algorithm with cuckoo search for global numerical optimization

verfasst von: Gai-Ge Wang, Amir H. Gandomi, Xiangjun Zhao, Hai Cheng Eric Chu

Erschienen in: Soft Computing | Ausgabe 1/2016

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Abstract

For the purpose of enhancing the search ability of the cuckoo search (CS) algorithm, an improved robust approach, called HS/CS, is put forward to address the optimization problems. In HS/CS method, the pitch adjustment operation in harmony search (HS) that can be considered as a mutation operator is added to the process of the cuckoo updating so as to speed up convergence. Several benchmarks are applied to verify the proposed method and it is demonstrated that, in most cases, HS/CS performs better than the standard CS and other comparative methods. The parameters used in HS/CS are also investigated by various simulations.

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Beyer H (2001) The theory of evolution strategies. Springer, New YorkCrossRef

Doǧan E, Saka MP (2012) Optimum design of unbraced steel frames to LRFD-AISC using particle swarm optimization. Adv Eng Softw 46(1):27–34. doi:10.1016/j.advengsoft.2011.05.008 CrossRef

Dorigo M, Stutzle T (2004) Ant colony optimization. MIT Press, CambridgeCrossRefMATH

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

Gandomi AH, Yang XS, Talatahari S, Alavi AH (2013a) Metaheuristic applications in structures and infrastructures. Elsevier, Waltham

Gandomi AH, Yun GJ, Yang X-S, Talatahari S (2013b) Chaos-enhanced accelerated particle swarm optimization. Commun Nonlinear Sci Numer Simulat 18(2):327–340. doi:10.1016/j.cnsns.2012.07.017

Gandomi AH, Yang X-S, Alavi AH, Talatahari S (2013c) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22(6):1239–1255. doi:10.1007/s00521-012-1028-9

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

Gandomi AH, Talatahari S, Yang X-S, Deb S (2013e) Design optimization of truss structures using cuckoo search algorithm. Struct Des Tall Spec Build 22(17):1330–1349. doi:10.1002/tal.1033

Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simulat 17(12):4831–4845. doi:10.1016/j.cnsns.2012.05.010 MathSciNetCrossRefMATH

Gandomi AH, Yang X-S, Talatahari S, Deb S (2012) Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization. Comput Math Appl 63(1):191–200. doi:10.1016/j.camwa.2011.11.010 MathSciNetCrossRefMATH

García-Martínez C, Lozano M (2010) Evaluating a local genetic algorithm as context-independent local search operator for metaheuristics. Soft Comput 14(10):1117–1139CrossRef

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

Goldberg DE (1998) Genetic algorithms in search optimization and machine learning. Addison-Wesley, New York

Guo L, Wang G-G, Gandomi AH, Alavi AH, Duan H (2014) A new improved krill herd algorithm for global numerical optimization. Neurocomputing. doi:10.1016/j.neucom.2014.01.023

Kennedy J, Eberhart R (1995) Particle swarm optimization. Paper presented at the Proceeding of the IEEE international conference on neural networks, Perth, Australia, 27 Nov–1 Dec

Khatib W, Fleming P (1998) The stud GA: a mini revolution? In: Eiben A, Back T, Schoenauer M, Schwefel H (eds) Proceedings of the 5th international conference on parallel problem solving from nature, New York, USA. Parallel problem solving from nature. Springer, London, pp 683–691

Li X, Wang J, Zhou J, Yin M (2011) A perturb biogeography based optimization with mutation for global numerical optimization. Appl Math Comput 218(2):598–609. doi:10.1016/j.amc.2011.05.110 MathSciNetCrossRefMATH

Li S, Chen S, Liu B (2012) Accelerating a recurrent neural network to finite-time convergence for solving time-varying Sylvester equation by Using a Sign-Bi-power Activation Function. Neural Process Lett 37(2):189–205. doi:10.1007/s11063-012-9241-1 CrossRef

Li X, Yin M (2014) Parameter estimation for chaotic systems by hybrid differential evolution algorithm and artificial bee colony algorithm. Nonlinear Dyn 77(1–2):61–71. doi:10.1007/s11071-014-1273-9 MathSciNetCrossRef

Li Y, Li S, Song Q, Liu H, Meng MQH (2014) Fast and robust data association using posterior based approximate joint compatibility test. IEEE Trans Ind Inform 10(1):331–339. doi:10.1109/TII.2013.2271506 CrossRef

Li S, Li Y (2014) Nonlinearly activated neural network for solving time-varying complex Sylvester equation. IEEE Trans Cybern 44(8):1397–1407. doi:10.1109/TCYB.2013.2285166

Li X, Wang J, Yin M (2013a) Enhancing the performance of cuckoo search algorithm using orthogonal learning method. Neural Comput Appl 24(6):1233–1247. doi:10.1007/s00521-013-1354-6

Li S, Liu B, Li Y (2013b) Selective positive–negative feedback produces the winner-take-all competition in recurrent neural networks. IEEE Trans Neural Netw Learn Syst 24(2):301–309. doi:10.1109/TNNLS.2012.2230451

Li S, Li Y, Wang Z (2013c) A class of finite-time dual neural networks for solving quadratic programming problems and its k-winners-take-all application. Neural Netw 39:27–39. doi:10.1016/j.neunet.2012.12.009

Li X, Zhang J, Yin M (2013d) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Appl 24(7–8):1867–1877. doi:10.1007/s00521-013-1433-8

Li X, Yin M (2012a) Application of differential evolution algorithm on self-potential data. PLoS ONE 7(12):e51199. doi:10.1371/journal.pone.0051199

Li X, Yin M (2012b) Self-adaptive constrained artificial bee colony for constrained numerical optimization. Neural Comput Appl 24(3–4):723–734. doi:10.1007/s00521-012-1285-7

Li X, Yin M (2013a) An opposition-based differential evolution algorithm for permutation flow shop scheduling based on diversity measure. Adv Eng Softw 55:10–31. doi:10.1016/j.advengsoft.2012.09.003

Li X, Yin M (2013b) Multiobjective binary biogeography based optimization for feature selection using gene expression data. IEEE Trans Nanobiosci 12(4):343–353. doi:10.1109/TNB.2013.2294716

Luna F, Estébanez C, León C, Chaves-González JM, Nebro AJ, Aler R, Segura C, Vega-Rodríguez MA, Alba E, Valls JM (2011) Optimization algorithms for large-scale real-world instances of the frequency assignment problem. Soft Comput 15(5):975–990. doi:10.1007/s00500-010-0653-4 CrossRef

Mirjalili S, Mohd Hashim SZ, Moradian Sardroudi H (2012) Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Appl Math Comput 218(22):11125–11137. doi:10.1016/j.amc.2012.04.069 MathSciNetCrossRefMATH

Mirjalili S, Lewis A (2013) S-Shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm Evolut Comput 9:1–14. doi:10.1016/j.swevo.2012.09.002 CrossRef

Mirjalili S, Mirjalili SM, Yang X-S (2013) Binary bat algorithm. Neural Comput Appl. doi:10.1007/s00521-013-1525-5 MATH

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

Mirjalili S, Mirjalili SM, Lewis A (2014b) Let a biogeography-based optimizer train your multi-layer perceptron. Inf Sci 269:188–209. doi:10.1016/j.ins.2014.01.038

Omran MGH, Mahdavi M (2008) Global-best harmony search. Appl Math Comput 198(2):643–656. doi:10.1016/j.amc.2007.09.004 MathSciNetCrossRefMATH

Rahimi-Vahed A, Mirzaei A (2008) Solving a bi-criteria permutation flow-shop problem using shuffled frog-leaping algorithm. Soft Comput 12(5):435–452. doi:10.1007/s00500-007-0210-y CrossRefMATH

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

Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. International Computer Science Institute, Berkley

Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359. doi:10.1023/A:1008202821328

Wang G-G, Gandomi AH, Alavi AH, Hao G-S (2013a) Hybrid krill herd algorithm with differential evolution for global numerical optimization. Neural Comput Appl 25(2):297–308. doi:10.1007/s00521-013-1485-9

Wang G-G, Gandomi AH, Alavi AH (2013b) An effective krill herd algorithm with migration operator in biogeography-based optimization. Appl Math Model 38(9–10):2454–2462. doi:10.1016/j.apm.2013.10.052

Wang G, Guo L, Duan H, Wang H, Liu L, Shao M (2013c) Hybridizing harmony search with biogeography based optimization for global numerical optimization. J Comput Theor Nanosci 10(10):2318–2328. doi:10.1166/jctn.2013.3207

Wang G-G, Guo L, Gandomi AH, Hao G-S, Wang H (2014a) Chaotic krill herd algorithm. Inf Sci 274:17–34. doi:10.1016/j.ins.2014.02.123

Wang G, Guo L, Wang H, Duan H, Liu L, Li J (2014b) Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Comput Appl 24(3–4):853–871. doi:10.1007/s00521-012-1304-8

Wang G-G, Gandomi AH, Alavi AH (2014c) Stud krill herd algorithm. Neurocomputing 128:363–370. doi:10.1016/j.neucom.2013.08.031

Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: Proceedings of world congress on nature and biologically inspired computing (NaBIC 2009), Coimbatore, India, December 2009. IEEE Publications, USA, pp 210–214

Yang XS (2010a) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), studies in computational intelligence, vol 284. Springer, pp 65–74. doi:10.1007/978-3-642-12538-6_6

Yang XS (2010b) Nature-inspired metaheuristic algorithms, 2nd edn. Luniver Press, Frome

Yang XS (2011) Optimization algorithms. In: Koziel S, Yang X-S (eds) Computational optimization, methods and algorithms. Studies in computational intelligence, vol 356. Springer, Berlin, Heidelberg, pp 13–31. doi:10.1007/978-3-642-20859-1_2

Yang XS, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 1(4):330–343MATH

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

Titel: Hybridizing harmony search algorithm with cuckoo search for global numerical optimization
verfasst von: Gai-Ge Wang
Amir H. Gandomi
Xiangjun Zhao
Hai Cheng Eric Chu
Publikationsdatum: 03.12.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 1/2016
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-014-1502-7

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