A Batched Scalable Multi-Objective Bayesian Optimization Algorithm
This addresses multi-objective optimization problems for researchers and practitioners dealing with high-dimensional, expensive-to-evaluate functions, though it is incremental as it builds on existing Bayesian optimization methods.
The paper tackled the scalability, gradient incorporation, and batch optimization limitations of surrogate-assisted multi-objective optimization algorithms by proposing a batched scalable multi-objective Bayesian optimization algorithm, which demonstrated efficiency on benchmark problems and a real-world application.
The surrogate-assisted optimization algorithm is a promising approach for solving expensive multi-objective optimization problems. However, most existing surrogate-assisted multi-objective optimization algorithms have three main drawbacks: 1) cannot scale well for solving problems with high dimensional decision space, 2) cannot incorporate available gradient information, and 3) do not support batch optimization. These drawbacks prevent their use for solving many real-world large scale optimization problems. This paper proposes a batched scalable multi-objective Bayesian optimization algorithm to tackle these issues. The proposed algorithm uses the Bayesian neural network as the scalable surrogate model. Powered with Monte Carlo dropout and Sobolov training, the model can be easily trained and can incorporate available gradient information. We also propose a novel batch hypervolume upper confidence bound acquisition function to support batch optimization. Experimental results on various benchmark problems and a real-world application demonstrate the efficiency of the proposed algorithm.