Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

The expected improvement (EI) algorithm is a very popular method for expensive
optimization problems. In the past twenty years, the EI criterion has been extended to deal …

In many real-world scenarios, decision makers seek to efficiently optimize multiple
competing objectives in a sample-efficient fashion. Multi-objective Bayesian optimization …

Many real world scientific and industrial applications require optimizing multiple competing
black-box objectives. When the objectives are expensive-to-evaluate, multi-objective …

We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for
computationally expensive optimization problems with more than three objectives. The …

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Optimization for structural crashworthiness and energy absorption has become an important
topic of research attributable to its proven benefits to public safety and social economy. This …

Accelerating the discovery of advanced materials is crucial for modern industries,
aerospace, biomedicine, and energy. Nevertheless, only a small fraction of materials are …

Closed-loop supply chains (CLSCs) are essential for maximising the value creation over the
entire life cycle of a product. The design of these networks is increasing due to growing …

Evolutionary algorithms are widely used for solving multiobjective optimization problems but
are often criticized because of a large number of function evaluations needed …