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Hybridizing harmony search algorithm with cuckoo search for global numerical optimization

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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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References

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

    Book  Google Scholar 

  • 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

    Article  Google Scholar 

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

    Book  MATH  Google Scholar 

  • 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

    Article  MathSciNet  MATH  Google Scholar 

  • 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

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  MathSciNet  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  MathSciNet  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

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

    MATH  Google Scholar 

  • 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

    Article  MathSciNet  MATH  Google Scholar 

  • 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

    Article  MATH  Google Scholar 

  • 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

    Google Scholar 

  • 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):330–343

    MATH  Google Scholar 

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

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by Research Fund for the Doctoral Program of Jiangsu Normal University (no. 13XLR041) and National Natural Science Foundation of China (no. 61272297 and no. 61402207).

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Correspondence to Gai-Ge Wang.

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Communicated by V. Loia.

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Wang, GG., Gandomi, A.H., Zhao, X. et al. Hybridizing harmony search algorithm with cuckoo search for global numerical optimization. Soft Comput 20, 273–285 (2016). https://doi.org/10.1007/s00500-014-1502-7

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