Bayesian optimization methods are promising for the optimization of black-box functions that are expensive to evaluate. In this paper, a novel batch Bayesian optimization approach is proposed. The parallelization is realized via a multi-objective ensemble of multiple acquisition functions. In each iteration, the multi-objective optimization of the multiple acquisition functions is performed to search for the Pareto front of the acquisition functions. The batch of inputs are then selected from the Pareto front. The Pareto front represents the best trade-off between the multiple acquisition functions. Such a policy for batch Bayesian optimization can significantly improve the efficiency of optimization. The proposed method is compared with several state-of-the-art batch Bayesian optimization algorithms using analytical benchmark functions and real-world analog integrated circuits. The experimental results show that the proposed method is competitive compared with the state-of-the-art algorithms.
Wenlong Lyu (Fudan University)
Fan Yang (Fudan University)
Dian Zhou (Department of Electrical Engineering The University of Texas at Dallas Richardso)
Xuan Zeng (Fudan University)
Related Events (a corresponding poster, oral, or spotlight)
2018 Oral: Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design »
Thu Jul 12th 12:10 -- 12:20 PM Room A3