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CSS–A cheap-surrogate-based selection operator for multi-objective optimization

Kong, L. ORCID:, Kumar, A. ORCID:, Snášel, V. ORCID:, Das, S. ORCID:, Krömer, P. ORCID: and Ojha, V. ORCID: (2022) CSS–A cheap-surrogate-based selection operator for multi-objective optimization. In: Mernik, M., Eftimov, T. and Črepinšek, M. (eds.) Bioinspired Optimization Methods and Their Applications: 10th International Conference, BIOMA 2022, Maribor, Slovenia, November 17–18, 2022, Proceedings. Lecture Notes in Computer Science, 13627. Springer, Cham, pp. 54-68, X, 277. ISBN 9783031210938

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To link to this item DOI: 10.1007/978-3-031-21094-5_5


Due to the complex topology of the search space, expensive multi-objective evolutionary algorithms (EMOEAs) emphasize enhancing the exploration capability. Many algorithms use ensembles of surrogate models to boost the performance. Generally, the surrogate-based model either works out the solution’s fitness by approximating the evaluation function or selects the solution by weighting the uncertainty degree of candidate solutions. This paper proposes a selection operator called Cheap surrogate selection (CSS) for multi-objective problems by utilizing the density probability on a k-dimensional tree. As opposed to the first type of surrogate models, which approximate the objective function, the proposed CSS only estimates the uncertainty of the candidate solutions. As a result, CSS does not require extensive sampling or training. Besides, CSS makes use of neighbors’ density and builds the tree with low computational complexity, resulting in an accelerated surrogate process. Moreover, a new EMOEA is proposed by integrating spherical search as the core optimizer with the proposed selection scheme. Over a wide variety of benchmark problems, we show that the proposed method outperforms several state-of-the-art EMOEAs.

Item Type:Book or Report Section
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:110370

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