Abstract
Nature-inspired optimization algorithms are widely used in various mathematical and engineering problems because of their usability and applicability. However, these optimization algorithms show different performances depending on the characteristics of the problem applied. There have been various effort to solve this problem by developing a new algorithm, applying other heuristics, changing parameters, etc. The deep learning-based self-adaptive harmony search (DLSaHS) developed in this study is another effort to tackle the problem by controlling the probability of heuristics by using recurrent neural network (RNN) and the parameter called checkpoint (CP). DLSaHS contains the heuristics obtained from harmony search (HS), genetic algorithm (GA), particle swarm optimization (PSO), and copycat harmony search (CcHS). DLSaHS was applied to the ten mathematical benchmark problems obtained from IEEE CEC 2021. The performance of DLSaHS is compared to the HS which showed better performance. Also, the optimal CP value obtained when applied to low-dimensional problems and the probability of heuristics according to the CP were derived to efficiently apply the DLSaHS, and it is confirmed that the error and standard deviation of the result, and computation time can be considerably reduced by applying them to high-dimensional problems.
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References
Chaurasia SN, Kim JH (2019) An evolutionary algorithm based hyper-heuristic framework for the set packing problem. Inf Sci 505:1–31
Cowling P, Kendall G, Soubeiga E (2001) A hyperheuristic approach for scheduling a sales summit. In: Selected papers of the third international conference on the practice and theory of automated timetabling, PATAT 2000. Lecture notes in computer science
Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS'95. Proceedings of the sixth international symposium on micro machine and human science. IEEE, pp 39–43
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68
Holland J (1975) Adaptation in natural and artificial systems: an introductory analysis with application to biology. In: Control and artificial intelligence
Rice JR (1976) The algorithm selection problem. In: Advances in computers, vol 15. Elsevier, pp 65–118
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1):67–82 https://doi.org/10.1109/4235.585893
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Kim, T., Jung, H.W., Kim, J.H. (2022). Development of Deep Learning-based Self-adaptive Harmony Search. In: Kim, J.H., Deep, K., Geem, Z.W., Sadollah, A., Yadav, A. (eds) Proceedings of 7th International Conference on Harmony Search, Soft Computing and Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 140. Springer, Singapore. https://doi.org/10.1007/978-981-19-2948-9_33
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DOI: https://doi.org/10.1007/978-981-19-2948-9_33
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