Hybridizing harmony search algorithm with cuckoo search for global numerical optimization
Tóm tắt
Từ khóa
Tài liệu tham khảo
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
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
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
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
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68. doi: 10.1177/003754970107600201
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
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
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
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
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
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
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
Mirjalili S, Mirjalili SM, Yang X-S (2013) Binary bat algorithm. Neural Comput Appl. doi: 10.1007/s00521-013-1525-5
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
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
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):330–343