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Bayesian Optimization algorithm has become a promising approach for nonlinearglobal optimization problems and many machine learning applications. Over thepast few years, improvements and enhancements have been brought forward andthey have shown some promising results in solving the complex dynamic problems,systems of ordinary differential equations where the objective functions arecomputationally expensive to evaluate. Besides, the straightforwardimplementation of the Bayesian Optimization algorithm performs well merely foroptimization problems with 10-20 dimensions. The study presented in this paperproposes a new high dimensional Bayesian Optimization algorithm combiningRecurrent neural networks, which is expected to predict the optimal solutionfor the global optimization problems with high dimensional or time seriesdecision models. The proposed RNN-BO algorithm can solve the optimal controlproblems in the lower dimension space and then learn from the historical datausing the recurrent neural network to learn the historical optimal solutiondata and predict the optimal control strategy for any new initial system valuesetting. In addition, accurately and quickly providing the optimal controlstrategy is essential to effectively and efficiently control the epidemicspread while minimizing the associated financial costs. Therefore, to verifythe effectiveness of the proposed algorithm, computational experiments arecarried out on a deterministic SEIR epidemic model and a stochastic SIS optimalcontrol model. Finally, we also discuss the impacts of different numbers of theRNN layers and training epochs on the trade-off between solution quality andrelated computational efforts.

High-dimensional Bayesian Optimization Algorithm with Recurrent Neural Network for Disease Control Models in Time Series