Journal
KNOWLEDGE-BASED SYSTEMS
Volume 187, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2019.06.023
Keywords
Expensive optimization; Infill criterion; Surrogate-assisted meta-heuristic algorithms
Categories
Funding
- National Natural Science Foundation of China [61472269, 61703297, 61876123]
- High-level research project cultivation Foundation of Shandong Women's university [2018GSPSJ07]
- Team Training Plan for Discipline Talents of Shandong Women's university
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Surrogate-assisted meta-heuristic algorithms have won more and more attention for solving computationally expensive problems over past decades. However, most existing surrogate-assisted meta-heuristic algorithms either require thousands of expensive exact function evaluations to obtain acceptable solutions, or focus on solving only low-dimensional expensive optimization problems. In this paper, we attempt to propose a new method to solve high-dimensional expensive optimization problems, in which the population will firstly be granulated into two subsets, i.e., coarse-grained individuals and fine-grained ones, then different approximation methods are proposed for each category, and finally a new infill criteria is adopted to select solutions that have maximum uncertainty among all coarse-grained individuals and that among all fine-grained individuals, and the solution that has minimal approximated fitness value, to be re-evaluated using the exact objective function. Experimental results comparing the proposed algorithm with a few state-of-the-art surrogate-assisted evolutionary algorithms on benchmark problems with 50 and 100 dimensions show that the proposed algorithm is able to achieve better results when solving high-dimensional multi-modal expensive problems with a limited budget on exact fitness evaluations. (C) 2019 Elsevier B.V. All rights reserved.
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