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 databases and moderate search budgets. Moreover, a significant improvement of the solutions is obtained for the two TBTC problems tackled.

This paper presents a comparison between two surrogate-assisted optimization methods dealing with two-stage stochastic programming. The Efficient Global Optimization (EGO) framework is challenging a method coupling Genetic Algorithm (GA) and offline-learnt kriging model for the lower stage optimization. The objective is to prove the good behavior of bayesian optimization (and in particular EGO) applied to a real-world two-stage problem with strong dependencies between the stages. The problem consists in determining the optimal strategy of an electricity market player participating in reserve (first stage) as well as day-ahead energy and real-time markets (second stage). The decisions optimized at the first stage induce constraints on the second stage so that both stages can not be dissociated. One additional difficulty is the stochastic aspect due to uncertainties of several parameters (e.g. renewable energybased generation) that requires more computational power to be handled. Surrogate models are introduced to deal with that additional computational burden. Experiments show that the EGO-based approach gives better results than GA with offline kriging model using smaller budget.

The application of artificial intelligence and increasing high-speed computational performance is still not fully explored in the field of numerical modeling and simulation of machining processes. The efficiency of the numerical model to predict the observables depends on various inputs. The most important and challenging inputs are the material behavior of the work material and the friction conditions during the cutting operation. The parameters of the material model and the friction model have a decisive impact on the simulated results. To reduce the expensive experimentation cost that gives limited data for the parameters, an inverse methodology to identify the parameter values of those inputs is suggested to potentially have data of better quality. This paper introduces a novel approach for the inverse identification of model parameters by implementing the Efficient Global Optimization algorithm. In this work, a method relying on a complete automated Finite Element simulation-based optimization algorithm is implemented to inversely identify the value of the Johnson–Cook (JC) parameters and Coulomb’s friction coefficient correlatively, where the objective function is defined as minimizing the error difference between experimental and numerical results. The Ti6Al4V Grade 5 alloy material is considered as a work material, and the identified parameters sets are validated by comparing the simulated results with experimental results. The developed automation process reduces the computation time and eliminating human errors. The identified model parameters value predicts the cutting force as 169 N/mm (2% deviation from experiments), feed force as 55 N/mm (7% deviation from experiments), and chip thickness as 0.150 mm (11% deviation from experiments). Overall, the identified model parameters set improves the prediction accuracy of the finite element model by 32% compared with the best-identified parameters set in the literature.