Abstract
Harmony search (HS) can be applied to various optimization problems and easy to implement. In this paper, we try to improve HS by change the reference probability distribution of harmony memory. Zipf distribution is used to balance the intensification and diversification. In addition, we propose the adaptive mechanism to avoid setting the new parameter. Experimental results show that the improvement is effective on the high dimensional numerical function optimization problem.
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References
Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Comput. Surv. 35(3), 268–308 (2003)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc. (1989)
Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics-Part B 26, 29–41 (1996)
Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: Harmony search. Simulation 76(2), 60–68 (2001)
Geem, Z.W.: Harmony search algorithm for solving sudoku. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part I. LNCS (LNAI), vol. 4692, pp. 371–378. Springer, Heidelberg (2007)
Zhang, R., Hanzo, L.: Iterative multiuser detection and channel decoding for ds-cdma using harmony search. Signal Processing Letters 16, 917–920 (2009)
Ayvaz, M.T.: Simultaneous determination of aquifer parameters and zone structures with fuzzy c-means clustering and meta-heuristic harmony search algorithm. Advances in Water Resources 30, 2326–2338 (2007)
Mahdavi, M., Chehreghani, M.H., Abolhassani, H., Forsati, R.: Novel meta-heuristic algorithms for clustering web documents. Applied Mathematics and Computation 201(1-2), 441–451 (2008)
Geem, Z.W., Sim, K.B.: Parameter-setting-free harmony search algorithm. Applied Mathematics and Computation 217(8), 3881–3889 (2010)
Tseng, S.P., Lin, W.W.: Improving harmony search by zipf distribution. In: 2012 Sixth International Conference on Genetic and Evolutionary Computing (ICGEC), pp. 115–118 (August 2012)
Zipf, G.K.: Selected Studies of the Principle of Relative Frequency in Language. Harvard University Press (1932)
Zipf, G.K.: Human Behavior and the Principle of Least Effort. Addison-Wesley, Reading (1949)
Adamic, L.A., Huberman, B.A.: Zipf’s law and the Internet. Glottometrics 3, 143–150 (2002)
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Tseng, SP., Wu, JS. (2014). An Adaptive Harmony Search Algorithm with Zipf Distribution. In: Pan, JS., Snasel, V., Corchado, E., Abraham, A., Wang, SL. (eds) Intelligent Data analysis and its Applications, Volume II. Advances in Intelligent Systems and Computing, vol 298. Springer, Cham. https://doi.org/10.1007/978-3-319-07773-4_16
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DOI: https://doi.org/10.1007/978-3-319-07773-4_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07772-7
Online ISBN: 978-3-319-07773-4
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