sParEGO – A Hybrid Optimization Algorithm for Expensive Uncertain Multi-objective Optimization Problems

Duro, João A.; Purshouse, Robin C.; Oara, Daniel C.; Kadirkamanathan, Visakan; Fleming, Peter J.

doi:10.1007/978-3-030-12598-1_34

Cited by 3 publications

(2 citation statements)

References 11 publications

(12 reference statements)

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“…Its performance was tested with low-dimensional problems. Several papers have presented extensions of ParEGO, such as sParEGO [38], Adaptive Batch-ParEGO [39], and Modular ParEGO [40]. GS-MOMA [41] employs an ensemble approximation model for scalarization functions to conduct local search mechanisms on a memetic algorithm framework.…”

Section: A Related Workmentioning

confidence: 99%

Evolutionary Multiobjective Optimization Assisted by Scalarization Function Approximation for High-Dimensional Expensive Problems

Horaguchi¹,

Nishihara²,

Nakata³

2023

Preprint

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<p>Surrogate-assisted evolutionary algorithms (SAEAs) are a promising approach for solving expensive multiobjective optimization problems, but they often cannot address high-dimensional problems. Although one common approach to this challenge is to construct reliable surrogates, their accuracy inevitably deteriorates in a high-dimensional search space. Thus, this paper presents a novel SAEA based on scalarization function approximation, which is designed to strengthen its robustness against this deterioration. The proposed algorithm constructs an approximation model for each scalarization function defined in a decomposition-based framework. Each decomposed problem is then solved using multiple independent models trained for its neighbor problems. The intent is to decrease the risk of search performance degradations caused by unreliable approximations and retain the redundancy of the surrogate-assisted search to hedge the risk of over-fitting. Furthermore, each approximation model is adapted to a promising region of its corresponding decomposed problem to reduce the complexity of model fitting given a limited number of training samples. Experimental results show that the proposed algorithm is competitive with state-of-the-art SAEAs adapted for high-dimensional problems.</p>

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Section: A Related Workmentioning

confidence: 99%

Evolutionary Multiobjective Optimization Assisted by Scalarization Function Approximation for High-Dimensional Expensive Problems

Horaguchi¹,

Nishihara²,

Nakata³

2023

Preprint

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show abstract

“…via Monte Carlo methods). However methods are becoming available that attempt to estimate the variability without the necessity for repeated evaluations [12].…”

Section: Optimisationmentioning

confidence: 99%

Toward a unified framework for model calibration and optimisation in virtual engineering workflows

Oliver

Oakley

Purshouse

2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

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Many-Objective Quality Measures

Afşar

Fieldsend

Guerreiro

et al. 2023

Natural Computing Series

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sParEGO – A Hybrid Optimization Algorithm for Expensive Uncertain Multi-objective Optimization Problems

Abstract: This is a repository copy of sParEGO -A hybrid optimization algorithm for expensive uncertain multi-objective optimization problems.

Cited by 3 publications

References 11 publications

Evolutionary Multiobjective Optimization Assisted by Scalarization Function Approximation for High-Dimensional Expensive Problems

Evolutionary Multiobjective Optimization Assisted by Scalarization Function Approximation for High-Dimensional Expensive Problems

Toward a unified framework for model calibration and optimisation in virtual engineering workflows

Many-Objective Quality Measures

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