The Expected R2-Indicator Improvement for Multi-objective Bayesian Optimization

Deutz, André H.; Emmerich, Michael; Yang, Kaifeng

doi:10.1007/978-3-030-12598-1_29

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Deutz [43] recently proposed an IPC, also called EIR2, which is however very different from ours, proposed in this article. e main difference is that the R2 indicator is calculated in different spaces.…”

Section: Comparison With Other Ipcsmentioning

confidence: 75%

A Kriging Model-Based Expensive Multiobjective Optimization Algorithm Using R2 Indicator of Expectation Improvement

Ding

Zheng

2020

Mathematical Problems in Engineering

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Most of the multiobjective optimization problems in engineering involve the evaluation of expensive objectives and constraint functions, for which an approximate model-based multiobjective optimization algorithm is usually employed, but requires a large amount of function evaluation. Aiming at effectively reducing the computation cost, a novel infilling point criterion EIR2 is proposed, whose basic idea is mapping a point in objective space into a set in expectation improvement space and utilizing the R2 indicator of the set to quantify the fitness of the point being selected as an infilling point. This criterion has an analytic form regardless of the number of objectives and demands lower calculation resources. Combining the Kriging model, optimal Latin hypercube sampling, and particle swarm optimization, an algorithm, EIR2-MOEA, is developed for solving expensive multiobjective optimization problems and applied to three sets of standard test functions of varying difficulty and comparing with two other competitive infill point criteria. Results show that EIR2 has higher resource utilization efficiency, and the resulting nondominated solution set possesses good convergence and diversity. By coupling with the average probability of feasibility, the EIR2 criterion is capable of dealing with expensive constrained multiobjective optimization problems and its efficiency is successfully validated in the optimal design of energy storage flywheel.

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Section: Comparison With Other Ipcsmentioning

confidence: 75%

A Kriging Model-Based Expensive Multiobjective Optimization Algorithm Using R2 Indicator of Expectation Improvement

Ding

Zheng

2020

Mathematical Problems in Engineering

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“…Another popular class of multi-objective acquisition functions are improvement-based. These strategies focus on improving a performance metric over sets, for example the hypervolume indicator [18,19,26,93] or the R2 indicator [24]. The main drawback of these approaches is that the performance of these methods can be biased towards a single performance metric, which can be inadequate to assess the multi-objective aspects of the problem [98].…”

Section: Introductionmentioning

confidence: 99%

Joint Entropy Search for Multi-objective Bayesian Optimization

Ben¹,

Kantas²,

Shafei³

2022

Preprint

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Many real-world problems can be phrased as a multi-objective optimization problem, where the goal is to identify the best set of compromises between the competing objectives. Multi-objective Bayesian optimization (BO) is a sample efficient strategy that can be deployed to solve these vector-valued optimization problems where access is limited to a number of noisy objective function evaluations. In this paper, we propose a novel information-theoretic acquisition function for BO called Joint Entropy Search (JES), which considers the joint information gain for the optimal set of inputs and outputs. We present several analytical approximations to the JES acquisition function and also introduce an extension to the batch setting. We showcase the effectiveness of this new approach on a range of synthetic and real-world problems in terms of the hypervolume and its weighted variants.36th Conference on Neural Information Processing Systems (NeurIPS 2022).

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Bayesian Optimization

Wang¹,

Yang

2023

Natural Computing Series

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The Expected R2-Indicator Improvement for Multi-objective Bayesian Optimization

Cited by 3 publications

References 6 publications

A Kriging Model-Based Expensive Multiobjective Optimization Algorithm Using R2 Indicator of Expectation Improvement

A Kriging Model-Based Expensive Multiobjective Optimization Algorithm Using R2 Indicator of Expectation Improvement

Joint Entropy Search for Multi-objective Bayesian Optimization

Bayesian Optimization

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