PipeBO pipelines the sequential stages of an experiment and updates later-stage parameters with newly completed results, cutting the number of optimization steps to about 56% (K=2), 50% (K=3), and 38% (K=5) of sequential Bayesian optimization on BBOB benchmarks.
Using genetic algorithm (GA) and particle swarm optimization (PSO) methodsfordeterminationofinteractionparametersinmulticomponentsystemsofliquid–liquidequilibria
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Asynchronous Batch Bayesian Optimization with Pipelining Evaluations for Experimental Resource$\unicode{x2013}$constrained Conditions
PipeBO pipelines the sequential stages of an experiment and updates later-stage parameters with newly completed results, cutting the number of optimization steps to about 56% (K=2), 50% (K=3), and 38% (K=5) of sequential Bayesian optimization on BBOB benchmarks.