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Surrogate-Assisted Many-Objective Optimization With Estimate Error Preference
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DOI：10.1109/TSMC.2025.3629284.png)
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Abstract
En 中文
Surrogate-assisted evolutionary algorithms are frequently applied to solve time-consuming, resource-intensive, and black-box multiobjective optimization problems. Multiple approximation effectively identifies an approximate optimal solution set within finite exact function evaluations, which may need significant training time for the surrogate models. This article offers a model training method guided by estimation errors to assist the evolutionary algorithm in the search for optimal solutions. In model training, we dynamically use the Gaussian process (GP) and radial basis function (RBF) models following the adjacent generation discrepancy of estimation errors to reduce computational time. They are updated if only the current estimation error exceeds the previous, where the estimation error combines the minimum distance in the decision space and the prediction error of all test samples. In the model-assisted search, an autonomous function estimation method is proposed based on the preference for approximate model errors. The selection of the updated GP or RBF approximation is via a lower model estimation error; in contrast, the average is considered the function value of an individual. In infill sampling, the solution is selected based on the nondominated sorting of function estimation with the maximum angle. The uncertainty-based sampling method is to replenish when these models are not updated. The experiment investigates the effectiveness of the error preference-guided approximation method. The results of two classic benchmark problems and one practice problem show the superiority of the proposed algorithm compared to even well-performed optimization algorithms.
Keywords:
Approximate uncertainty
estimation error preference
expensive multiobjective/many-objective optimization problems (MOPs or MaOPs)
infill sampling
multisurrogate assisted search strategy
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