Abstract
Surrogate-model based optimization algorithms can be applied to solve expensive black-box function optimization problem. With the introduction of ensemble model, surrogate-model based algorithms can be automatically adjusted to adapt to various specific problems with different parameter spaces and no need for manual design of surrogate model. However, introduction of ensemble model significantly increases the computational load of surrogate-model based algorithms for training and updating of ensemble model. In this article, parallel computing technology is utilized to speed up the weight updating related computation for the ensemble surrogate model built by Dempster-Shafer theory, and a novel parallel sampling mechanism based on stochastic response surface method is developed to implement asynchronous parameter optimization, based on witch an asynchronous parallel global optimization algorithm is proposed. Furthermore, the parallel algorithm proposed is applied to quantitative trading strategy tuning in financial market and shows both feasibility and effectiveness in actual application. Experiments demonstrates that, the algorithms can achieve high speedup ratio and scalability with no degradation of optimization performance.
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References
LI, J., MING, Z., QIU, M., et al. (2011). Resource allocation robustness in multi-core embedded systems with inaccurate information [J]. Journal of Systems Architecture, 57(9), 840–849.
ZILI, S., XUE, C., ZHUGE, Q., et al. (2006). Security protection and checking for embedded system integration against buffer overflow attacks via hardware/software [J]. IEEE Transactions on Computers, 55(4), 443–453.
LI, J, QIU, M, NIU, J, et al 2010. Feedback Dynamic Algorithms for Preemptable Job Scheduling in Cloud Systems [C]; proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, F 31 Aug.-3 Sept. 2010, 2010.
QIU, H., QIU, M., LIU, M., et al. (2020). Lightweight selective encryption for social data protection based on EBCOT coding [J]. IEEE Transactions on Computational Social Systems, 7(1), 205–214.
QIU H, NOURA H, QIU M, et al. 2019 A User-Centric Data Protection Method for Cloud Storage Based on Invertible DWT [J]. IEEE Transactions on Cloud Computing, 1-.
MENGISTU, T., & GHALY, W. (2008). Aerodynamic optimization of turbomachinery blades using evolutionary methods and ANN-based surrogate models [J]. Optimization and Engineering, 9(3), 239–255.
Poloczek, J, Kramer, O. 2013 Local SVM Constraint Surrogate Models for Self-adaptive Evolution Strategies, Berlin, Heidelberg, F, [C]. Springer Berlin Heidelberg.
Peter T. Using Deep Learning as a surrogate model in Multi-objective Evolutionary Algorithms [J].
Klein, A, Falkner, S, Bartels, S, et al 2016. Fast Bayesian optimization of machine learning hyperparameters on large datasets [J]. arXiv preprint arXiv:160507079
SNOEK J, LAROCHELLE H, ADAMS R P 2012. Practical bayesian optimization of machine learning algorithms; proceedings of the Advances in neural information processing systems, F, [C].
GOEL, T., HAFTKA, R. T., SHYY, W., et al. (2007). Ensemble of surrogates [J]. Structural and Multidisciplinary Optimization, 33(3), 199–216.
MüLLER, J., & PICHé, R. (2011). Mixture surrogate models based on Dempster-Shafer theory for global optimization problems [J]. Journal of Global Optimization, 51(1), 79–104.
Regis, R. G., & Shoemaker, C. A. (2007). A stochastic radial basis function method for the global optimization of expensive functions [J]. INFORMS Journal on Computing, 19(4), 497–509.
ILIEVSKI I, AKHTAR T, FENG J, et al. 2017 Efficient Hyperparameter Optimization for Deep Learning Algorithms Using Deterministic RBF Surrogates, F, [C].
Asher, M. J., Croke, B. F., Jakeman, A. J., et al. (2015). A review of surrogate models and their application to groundwater modeling [J]. Water Resources Research, 51(8), 5957–5973.
MüLLER, J., & SHOEMAKER, C. A. (2014). Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for computationally expensive black-box global optimization problems [J]. Journal of Global Optimization, 60(2), 123–144.
Regis, R. G., & Shoemaker, C. A. (2009). Parallel stochastic global optimization using radial basis functions [J]. INFORMS Journal on Computing, 21(3), 411–426.
KRITYAKIERNE, T., AKHTAR, T., & SHOEMAKER, C. A. (2016). SOP: Parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems [J]. Journal of Global Optimization, 66(3), 417–437.
GINSBOURGER D, LE RICHE R, CARRARO L. Kriging is well-suited to parallelize optimization [M]. Computational intelligence in expensive optimization problems. Springer. 2010: 131–62.
GINSBOURGER D, JANUSEVSKIS J, LE RICHE R2011. Dealing with asynchronicity in parallel Gaussian process based global optimization; proceedings of the 4th International Conference of the ERCIM WG on computing & statistics (ERCIM'11), F, [C].
HUTTER F, HOOS H H, LEYTON-BROWN K 2012. Parallel algorithm configuration [M]. Learning and Intelligent Optimization. Springer.: 55–70
SUN Y, WANG J, LU, Z. 2019 Asynchronous Parallel Surrogate Optimization Algorithm Based on Ensemble Surrogating Model and Stochastic Response Surface Method; proceedings of the 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), F 27–29 May 2019, [C].
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Sun, Y., Du, S. & Lu, Z. Asynchronous Parallel Surrogate Optimization Algorithm for Quantitative Strategy Parameter Tuning. J Sign Process Syst 93, 309–321 (2021). https://doi.org/10.1007/s11265-020-01540-3
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DOI: https://doi.org/10.1007/s11265-020-01540-3