A surrogate-assisted evolutionary algorithm for expensive many-objective optimization in the refining process

Han, Dong; Du, Wenli; Wang, Xinjie; Du, Wei

doi:10.1016/j.swevo.2021.100988

Cited by 21 publications

(14 citation statements)

References 51 publications

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“…k (x (1) , x (2) ) := Cov{f (x (1) ), f (x (2) ) | ψ} = k(x (1) , x (2) ) − k(x (1) ) K −1 k(x (2) ), (2)(3)(4)(5) where k is additional information and is essential to design a multiple-point infill criterion (a.k.a. acquisition function) for parallel techniques in BGO algorithms, e.g., the multi-point expected improvement (q-EI) [31].…”

Section: Gaussian Processmentioning

confidence: 99%

“…Once the surrogate models M are constructed, MOBGO enters the main loop until a stopping criterion is fulfilled 3 , as shown in Alg. 1 from line 6 to line 12.…”

Section: Structure Of Mobgomentioning

confidence: 99%

“…Example 3.1 In Fig. 1, a Pareto-front approximation is composed of y (1) = (3, 1), y (2) = (2, 1.5) and y (3) = (1, 2.5) and a batch Ŷ = {ŷ (1) , ŷ(2) }, of which parameters µ and s are:…”

Section: Cov(smentioning

confidence: 99%

“…In the following, we present five different q-PoIs under concern. Definition 3.4 Given m × q multivariate normal distributions Ŷ = {ŷ (1) , ŷ(2) , • • • , ŷ(q) } with a mean matrix M = [µ (1) , • • • , µ (q) ] and covariance matrices y (2) y (3) µ (1) µ (2) ξ(y…”

Section: Definitions Of Five Proposed Q-pois In Different Casesmentioning

confidence: 99%

See 3 more Smart Citations

A Parallel Technique for Multi-objective Bayesian Global Optimization: Using a Batch Selection of Probability of Improvement

Yang¹,

Dong²,

Affenzeller³

2022

Preprint

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Bayesian global optimization (BGO) is an efficient surrogate-assisted technique for problems involving expensive evaluations. A parallel technique can be used to parallelly evaluate the true-expensive objective functions in one iteration to boost the execution time. An effective and straightforward approach is to design an acquisition function that can evaluate the performance of a bath of multiple solutions, instead of a single point/solution, in one iteration. This paper proposes five alternatives of Probability of Improvement (PoI) with multiple points in a batch (q-PoI) for multi-objective Bayesian global optimization (MOBGO), taking the covariance among multiple points into account. Both exact computational formulas and the Monte Carlo approximation algorithms for all proposed q-PoIs are provided. Based on the distribution of the multiple points relevant to the Pareto-front, the position-dependent behavior of the five q-PoIs is investigated. Moreover, the five q-PoIs are compared with the other nine state-of-the-art and recently proposed batch MOBGO algorithms on twenty bio-objective benchmarks. The empirical experiments on different variety of benchmarks are conducted to demonstrate the effectiveness of two greedy q-PoIs (q-PoI best and q-PoI all ) on lowdimensional problems and the effectiveness of two explorative q-PoIs (q-PoI one and q-PoI worst ) on high-dimensional problems with difficult-to-approximate Pareto front boundaries.

show abstract

Section: Gaussian Processmentioning

confidence: 99%

“…Once the surrogate models M are constructed, MOBGO enters the main loop until a stopping criterion is fulfilled 3 , as shown in Alg. 1 from line 6 to line 12.…”

Section: Structure Of Mobgomentioning

confidence: 99%

Section: Cov(smentioning

confidence: 99%

Section: Definitions Of Five Proposed Q-pois In Different Casesmentioning

confidence: 99%

See 2 more Smart Citations

A Parallel Technique for Multi-objective Bayesian Global Optimization: Using a Batch Selection of Probability of Improvement

Yang¹,

Dong²,

Affenzeller³

2022

Preprint

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show abstract

“…The algorithms used in the paper are only a good sample of single-and multi-objective optimization algorithms. We think the optimization research community and interdisciplinary research community mentioned above will benefit as many more optimization algorithms, including many-objective optimization algorithms (Liang et al, 2021, Han et al, 2022, Rivera et al, 2022, that can be studied using the methodology proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Assessing ranking and effectiveness of evolutionary algorithm hyperparameters using global sensitivity analysis methodologies

Ojha

Timmis²,

Nicosia³

2022

Swarm and Evolutionary Computation

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Deep Surrogate Network Enhancement of Non-dominated Sorting Genetic Algorithms for Improved Multi-objective Optimization

Ibarayev,

Omarov,

Amanov

et al. 2024

Mechanisms and Machine Science

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A surrogate-assisted evolutionary algorithm for expensive many-objective optimization in the refining process

Cited by 21 publications

References 51 publications

A Parallel Technique for Multi-objective Bayesian Global Optimization: Using a Batch Selection of Probability of Improvement

A Parallel Technique for Multi-objective Bayesian Global Optimization: Using a Batch Selection of Probability of Improvement

Assessing ranking and effectiveness of evolutionary algorithm hyperparameters using global sensitivity analysis methodologies

Deep Surrogate Network Enhancement of Non-dominated Sorting Genetic Algorithms for Improved Multi-objective Optimization

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