A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger design
This work addresses the computational bottleneck in MOBO for expensive black-box problems, enabling more efficient parallel optimization in applications like heat exchanger design, though it is an incremental improvement over existing batch methods.
The authors tackled the inefficiency of existing multi-objective Bayesian optimization (MOBO) methods in parallel processing by introducing HIPPO, a batch acquisition function that encourages diversity through penalization, resulting in an order of magnitude lower computational overhead and scalability to larger batch sizes than current alternatives.
We present HIghly Parallelisable Pareto Optimisation (HIPPO) -- a batch acquisition function that enables multi-objective Bayesian optimisation methods to efficiently exploit parallel processing resources. Multi-Objective Bayesian Optimisation (MOBO) is a very efficient tool for tackling expensive black-box problems. However, most MOBO algorithms are designed as purely sequential strategies, and existing batch approaches are prohibitively expensive for all but the smallest of batch sizes. We show that by encouraging batch diversity through penalising evaluations with similar predicted objective values, HIPPO is able to cheaply build large batches of informative points. Our extensive experimental validation demonstrates that HIPPO is at least as efficient as existing alternatives whilst incurring an order of magnitude lower computational overhead and scaling easily to batch sizes considerably higher than currently supported in the literature. Additionally, we demonstrate the application of HIPPO to a challenging heat exchanger design problem, stressing the real-world utility of our highly parallelisable approach to MOBO.