English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present HIghly Parallelisable Pareto Optimisation (HIPPO) -- a batchacquisition function that enables multi-objective Bayesian optimisation methodsto efficiently exploit parallel processing resources. Multi-Objective BayesianOptimisation (MOBO) is a very efficient tool for tackling expensive black-boxproblems. However, most MOBO algorithms are designed as purely sequentialstrategies, and existing batch approaches are prohibitively expensive for allbut the smallest of batch sizes. We show that by encouraging batch diversitythrough penalising evaluations with similar predicted objective values, HIPPOis able to cheaply build large batches of informative points. Our extensiveexperimental validation demonstrates that HIPPO is at least as efficient asexisting alternatives whilst incurring an order of magnitude lowercomputational overhead and scaling easily to batch sizes considerably higherthan currently supported in the literature. Additionally, we demonstrate theapplication of HIPPO to a challenging heat exchanger design problem, stressingthe real-world utility of our highly parallelisable approach to MOBO.

A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger design