Abstract
Recently, Harmony Search Algorithm (HSA) is gaining prominence in solving real-world optimization problems. Like most of the evolutionary algorithms, finding optimal solution to a given numerical problem using HSA involves several evaluations of the original function and is prohibitively expensive. This problem can be resolved by amalgamating HSA with surrogate models that approximate the output behavior of complex systems based on a limited set of computational expensive simulations. Though, the use of surrogate models can reduce the original functional evaluations, the optimization based on the surrogate model can lead to erroneous results. In addition, the computational effort needed to build a surrogate model to better approximate the actual function can be an overhead. In this paper, we present a novel method in which HSA is integrated with an ensemble of low quality surrogate models. The proposed algorithm is referred to as HSAES and is tested on a set of 10 bound-constrained problems and is compared with conventional HSA.
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Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001). doi:10.1177/003754970107600201
Geem, Z.W., Kim, J.H., Loganathan, G.V.: Harmony search optimization: application to pipe network design. Int. J. Model. Simul. 22(2), 125–133 (2002)
Geem, Z.W., Tseng, C.L.: Engineering Applications of Harmony Search. In: GECCO Late Breaking Papers, pp. 169–173, July 2002
Geem, Z.W., Tseng, C.L.: New Methodology, Harmony Search, its Robustness. In: GECCO Late Breaking Papers, pp. 174–178, July 2002
Paik, K.R., Jeong, J.H., Kim, J.H.: Use of a harmony search for optimal design of coffer dam drainage pipes. J. KSCE 21(2-B), 119–128 (2001)
Jin, Y.: A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing 9(1), 3–12 (2005)
Zhang, J., Sanderson, A.C.: DE-AEC: a differential evolution algorithm based on adaptive evolution control. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 3824–3830, September 2007
Díaz-Manríquez, A., Toscano-Pulido, G., Gómez-Flores, W.: On the selection of surrogate models in evolutionary optimization algorithms. In: 2011 IEEE Congress on Evolutionary Computation (CEC), pp. 2155–2162, June 2011
Diao, R., Shen, Q.: Feature selection with harmony search. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42(6), 1509–1523 (2012)
Manjarres, D., Landa-Torres, I., Gil-Lopez, S., Del Ser, J., Bilbao, M.N., Salcedo-Sanz, S., Geem, Z.W.: A survey on applications of the harmony search algorithm. Eng. Appl. Artif. Intell. 26(8), 1818–1831 (2013)
Moh’d Alia, O., Mandava, R.: The variants of the harmony search algorithm: an overview. Artif. Intell. 36(1), 49–68 (2011)
Forrester, A.I., Keane, A.J.: Recent advances in surrogate-based optimization. Prog. Aerosp. Sci. 45(1), 50–79 (2009)
Giunta, A.A., Watson, L.T., Koehler, J.: A comparison of approximation modeling techniques: polynomial versus interpolating models. AIAA paper, 98–4758 (1998)
Daberkow, D.D., Mavris, D.N.: New approaches to conceptual and preliminary aircraft design: A comparative assessment of a neural network formulation and a response surface methodology (1998)
Jin, R., Chen, W., Simpson, T.W.: Comparative studies of metamodelling techniques under multiple modelling criteria. Struct. Multidiscip. Optim. 23(1), 1–13 (2001)
Quinn, G.P., Keough, M.J.: Experimental design and data analysis for biologists. Cambridge University Press (2002)
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Mohanarangam, K., Mallipeddi, R. (2016). Harmony Search Algorithm with Ensemble of Surrogate Models. In: Kim, J., Geem, Z. (eds) Harmony Search Algorithm. Advances in Intelligent Systems and Computing, vol 382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47926-1_3
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DOI: https://doi.org/10.1007/978-3-662-47926-1_3
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