A New Bayesian Optimization Algorithm for Complex High-Dimensional Disease Epidemic Systems
This paper presents an Improved Bayesian Optimization (IBO) algorithm to solve complex high-dimensional epidemic models' optimal control solution. Evaluating the total objective function value for disease control models with hundreds of thousands of control time periods is a high computational cost. In this paper, we improve the conventional Bayesian Optimization (BO) approach from two parts. The existing BO methods optimize the minimizer step for once time during each acquisition function update process. To find a better solution for each acquisition function update, we do more local minimization steps to tune the algorithm. When the model is high dimensions, and the objective function is complicated, only some update iterations of the acquisition function may not find the global optimal solution. The IBO algorithm adds a series of Adam-based steps at the final stage of the algorithm to increase the solution's accuracy. Comparative simulation experiments using different kernel functions and acquisition functions have shown that the Improved Bayesian Optimization algorithm is effective and suitable for handing large-scale and complex epidemic models under study. The IBO algorithm is then compared with four other global optimization algorithms on three well-known synthetic test functions. The effectiveness and robustness of the IBO algorithm are also demonstrated through some simulation experiments to compare with the Particle Swarm Optimization algorithm and Random Search algorithm. With its reliable convergence behaviors and straightforward implementation, the IBO algorithm has a great potential to solve other complex optimal control problems with high dimensionality.