Improving surrogate-assisted variable fidelity multi-objective optimization using a clustering algorithm |
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Affiliation: | 1. Mechanical, Maritime and Materials Engineering Faculty, Delft University of Technology, The Netherlands;2. Department of Aerospace Engineering, San Diego State University, USA;3. Institute for Mechanical Systems, ETH Zürich, Switzerland;4. Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Spain;1. Department of Computing, Glyndwr University, UK;2. Engineering Optimization & Modeling Center, Reykjavik University, Iceland;3. Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Poland;4. School of Computer Science & Electronic Engineering, University of Essex, UK;1. The State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science & Technology, Wuhan, PR China;2. School of Aerospace Engineering, Huazhong University of Science & Technology, 430074 Wuhan, PR China |
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Abstract: | Surrogate-assisted evolutionary optimization has proved to be effective in reducing optimization time, as surrogates, or meta-models can approximate expensive fitness functions in the optimization run. While this is a successful strategy to improve optimization efficiency, challenges arise when constructing surrogate models in higher dimensional function space, where the trade space between multiple conflicting objectives is increasingly complex. This complexity makes it difficult to ensure the accuracy of the surrogates. In this article, a new surrogate management strategy is presented to address this problem. A k-means clustering algorithm is employed to partition model data into local surrogate models. The variable fidelity optimization scheme proposed in the author's previous work is revised to incorporate this clustering algorithm for surrogate model construction. The applicability of the proposed algorithm is illustrated on six standard test problems. The presented algorithm is also examined in a three-objective stiffened panel optimization design problem to show its superiority in surrogate-assisted multi-objective optimization in higher dimensional objective function space. Performance metrics show that the proposed surrogate handling strategy clearly outperforms the single surrogate strategy as the surrogate size increases. |
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Keywords: | Clustering Evolutionary computation Surrogate model Kriging Optimization Variable fidelity optimization |
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