Surrogate-based optimization is widely used to deal with long-running black-box simulation-based objective functions. Actually, the use of a surrogate model such as Kriging or Artificial Neural Network allows to reduce the number of calls to the CPU time-intensive simulator. Bayesian optimization uses the ability of surrogates to provide useful information to help guiding effectively the optimization process. In this paper, the Efficient Global Optimization (EGO) reference framework is challenged by a Bayesian Neural Network-assisted Genetic Algorithm, namely BNN-GA. The Bayesian Neural Network (BNN) surrogate is chosen for its ability to provide an uncertainty measure of the prediction that allows to compute the Expected Improvement of a candidate solution in order to improve the exploration of the objective space. BNN is also more reliable than Kriging models for high-dimensional problems and faster to set up thanks to its incremental training. In addition, we propose a batch-based approach for the parallelization of BNN-GA that is challenged by a parallel version of EGO, called q-EGO. Parallel computing is a highly important complementary way (to surrogates) to deal with the computational burden of simulation-based optimization. The comparison of the two parallel approaches is experimentally performed through several benchmark functions and two real-world problems within the scope of Tuberculosis Transmission Control (TBTC). The study presented in this paper proves that parallel batched BNN-GA is a viable alternative to q-EGO approaches being more suitable for high-dimensional problems, parallelization impact, bigger data-bases and moderate search budgets. Moreover, a significant improvement of the solutions is obtained for the two TBTC problems tackled.

基于代理的优化被广泛用于处理长期运行的基于黑盒仿真的目标函数。实际上，使用替代模型（例如Kriging或人工神经网络）可以减少对CPU时间密集型模拟器的调用次数。贝叶斯优化使用代理的能力来提供有用的信息，以帮助有效地指导优化过程。在本文中，有效的全球优化（EGO）参考框架受到贝叶斯神经网络辅助遗传算法BNN-GA的挑战。选择贝叶斯神经网络（BNN）替代项是因为其能够提供预测的不确定性度量，从而可以计算候选解决方案的预期改进，从而改善对目标空间的探索。对于高维问题，BNN也比Kriging模型更可靠，并且由于其增量训练，BNN的建立速度更快。此外，我们提出了一种基于批处理的BNN-GA并行化方法，该方法受到称为q-EGO的并行EGO的挑战。并行计算是（替代）处理基于仿真的优化的计算负担的一种非常重要的补充方式。两种并行方法的比较是通过结核病传播控制（TBTC）范围内的几个基准功能和两个实际问题在实验上进行的。本文提出的研究证明，并行批处理BNN-GA是q-EGO方法的可行替代方法，它更适合于高维问题，并行化影响，更大的数据库和适度的搜索预算。