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Bayesian Co-evolutionary Optimization based entropy search for high-dimensional many-objective optimization
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DOI：10.1016/j.knosys.2023.110630.png)
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Abstract
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Bayesian evolutionary optimization algorithms have been widely employed to solve expensive many -objective optimization problems. However, the existing approaches are generally designed for low -dimensional problems. In high-dimensional problems, the accuracy of the prediction decreases. And the acquisition function becomes ineffective. The combination of these challenges renders existing approaches unsuitable for selecting potential individual solutions for high-dimensional many-objective optimization problems. To address these limitations, we propose a novel Entropy Search-based Bayesian Co-Evolutionary Optimization approach (ESB-CEO). With the co-evolutionary algorithm as the basic optimizer, it executes an adaptive acquisition function combining the Lp-norm and infor-mation entropy to efficiently solve computationally expensive many-objective optimization problems. Individual solutions that have a significant effect on different search stages can be effectively identified, which improves the convergence and diversity of the algorithm. Extensive experimental results based on a set of expensive multi/many-objective test problems demonstrate that the proposed approach significantly outperforms five state-of-the-art surrogate-assisted evolutionary algorithms.& COPY; 2023 Elsevier B.V. All rights reserved.
Keywords:
Expensive many-objective optimization
Bayesian optimization
Co-evolution
Adaptive acquisition function
Entropy search
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