Abstract
Harmony search algorithm is a meta-heuristic optimization method imitating the music improvisation process, where musicians improvise their instruments’ pitches searching for a perfect state of harmony. First, an improved harmony search algorithm is presented using the concept of swarm intelligence. Next, it is employed for training feedforward neural networks for three benchmark classification problems. Then, the performance of the proposed algorithm is compared with that of three methods. Simulation results demonstrate the effectiveness of the proposed algorithm.
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References
Ceylan H, HaIdenbilen S et al (2008) Transport energy modelling with meta-heuristic harmony search algorithm, an application to Turkey. Energ Policy 36(7):2527–2535
Chronopoulos AT, Sarangapani J (2002) A distributed discrete time neural network architecture for pattern allocation and control. In: Proceedings of the international parallel and distributed processing symposium (IPDPS’02), FL, USA, pp 204–211
Curry B, Morgan P (1997) Neural networks: a need for caution, OMEGA. Int J Manag Sci 25:123–133
Degertekin SO (2008a) Harmony search algorithm for optimum design of steel frame structures: a comparative study with other optimization methods. Struct Eng Mech 29(4):391–410
Degertekin SO (2008b) Optimum design of steel frames using harmony search algorithm. Struct Multidiscip Optim 36(4):393–401
Fausett L (1994) Fundamentals of neural networks architectures, algorithms, and applications. Prentice Hall, New Jersey
Fesanghary M, Mahdavi M, Minary-Jolandan M, Alizadeh Y (2008) Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Comput Method Appl Mech Eng 197(33–40):3080–3091
Forsati R, Haghighat AT, Mahdavi M (2008) Harmony search based algorithms for bandwidth-delay-constrained least-cost multicast routing. Comput Commun 31(10):2505–2519
Garro BA, Sossa H, Vázquez RA (2009) Design of artificial neural networks using a modified particle swarm optimization algorithm. International joint conference on neural networks, 2009 (IJCNN 2009), pp 938–945
Garro BA, Sossa H, Vázquez RA (2010) Design of artificial neural networks using differential evolution algorithm. In: ICONIP’10 Proceedings of the 17th international conference on neural information processing: models and applications, vol Part II, pp 201–208
Garro BA, Sossa H, Vázquez RA (2011) Evolving neural networks: a comparison between differential evolution and particle swarm optimization. ICSI 2011:447–454
Geem ZW (2006) Optimal cost design of water distribution networks using harmony search. Eng Optim 38:259–280
Geem ZW (2009a) Harmony search optimization to the pump-included water distribution network design. Civ Eng Environ Syst 26(3):211–221
Geem ZW (2009b) Particle-swarm harmony search for water network design. Eng Optim 41(4):297–311
Geem ZW, Kim J, Loganathan G (2002) Harmony search optimization: application to pipe network design. Int J Model Simul 22(2):125–133
Geem ZW, Lee KS, Park YJ (2005) Application of harmony search to vehicle routing. Am J Appl Sci 2(12):1552–1557
Gupta JND, Sexton RS (1999) Comparing backpropagation with a genetic algorithm for neural network training. Omega Int J Manag Sci 27:679–684
Hassoun MH (1995) Fundamentals of artificial neural networks. MIT Press, Massachusetts
Karaboga D, Ozturk C (2009) Neural networks training by artificial bee colony algorithm on pattern classification. Neural Netw World 19:279–292
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, pp 1942–1948
Kim JH, Geem ZW, Kim ES (2001) Parameter estimation of the nonlinear Muskingum model using harmony search. J Am Water Resour Assoc 37:1131–1138
Kim D, Kim H, Chung D (2005) A modified genetic algorithm for fast training neural networks. In: Advances in neural networks—ISNN 2005, vol 3496/2005. Springer, Berlin, pp 660–665
Kiranyaz S, Ince T, Yildirim A, Gabbouj M (2009) Evolutionary artificial neural networks by multi-dimensional particle swarm optimization. Neural Netw 22(10):1448–1462
Lee KS, Geem ZW (2004) A new structural optimization method based on the harmony search algorithm. Comput Struct 82:781–798
Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Method Appl Mech Eng 194:3902–3933
Lee KS, Geem ZW, Lee SH, Bae KW (2005) The harmony search heuristic algorithm for discrete structural optimization. Eng Optim 37:663–684
Salchenberger LM, Cinar EM, Lash NA (1992) Neural networks: a new tool for predicting thrift failures. Decis Sci 23:899–916
Sexton RS, Dorsey RE (2000) Reliable classification using neural networks: a genetic algorithm and back propagation comparison. Decis Support Syst 30:11–22
Sexton RS, Dorsey RE, Johnson JD (1998) Toward global optimization of neural networks: a comparison of the genetic algorithm and backpropagation. Decis Support Syst 22:171–186
Slowik A, Bialko M (2008) Training of artificial neural networks using differential evolution algorithm. In: Proceedings of the IEEE conference on human system interaction, Sracow, Poland, pp 60–65
Socha K, Blum C (2007) An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training. Neural Comput Appl 16:235–247
Vasebi A, Fesanghary M, Bathaee SMT (2007) Combined heat and power economic dispatch by harmony search algorithm. Int J Electr Power 29:713–719
Wang F et al (1999) Neural network structures and training algorithms for microwave applications. Int J RF Microw C E 9:216–240
Yu J, Wang S, Xi L (2008) Evolving artificial neural networks using an improved PSO and DPSO. Neurocomputing 71:1054–1060
Zamani M, Sadeghian A (2010) A variation of particle swarm optimization for training of artificial neural networks. In: Al-Dahoud Ali (ed) Computational intelligence and modern heuristics, INTECH. ISBN: 978-953-7619-28-2
Zhang QJ, Gupta KC (2003) Neural networks for RF and microwave design—from theory to practice. IEEE Trans Microw Theory 51(4):1339–1350
Zou DX, Gao L, Wu J, Li S (2010) Novel global harmony search algorithm for unconstrained problems. Neurocomputing 73:3308–3318
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Tavakoli, S., Valian, E. & Mohanna, S. Feedforward neural network training using intelligent global harmony search. Evolving Systems 3, 125–131 (2012). https://doi.org/10.1007/s12530-012-9054-5
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DOI: https://doi.org/10.1007/s12530-012-9054-5