Empirical study of evolutionary computation-based multi-objective Bayesian optimization for materials discovery

Multi-objective Bayesian optimization (MOBO) is broadly used for applications with high cost observations such as materials discovery. In BO, a derivative-free optimization algorithm is generally employed to maximize the acquisition function. In this study, we present a method for acquisition function maximization based on a (1 + 1)-evolutionary strategy in MOBO for materials discovery, which is a simple and easy-to-use approach with low computational complexity compared to conventional algorithms. In MOBO, weight vectors are used for scalarizing MO functions, typically employed to convert MO optimization into single-objective optimization. The weight vectors at each round of MOBO are generally obtained using either stochastic (random sampling) or deterministic methods based on searched results. To clarify the effect of both the scalarizing methods on MOBO, we examine the effectiveness of random sampling methods versus two deterministic methods: reference-vector-based and self-organizing map-based decomposition methods. Experimental results from four test functions and a hydrogen storage material database as a concrete application show the effectiveness of the proposed method and the random sampling method. These results implied that the proposed method was useful for real-world MOBO experiments in materials discovery.