DG2: a faster and more accurate differential grouping for large-scale black-box optimization

Omidvar, M, Yang, M, Mei, Y, Li, X and Yao, X 2017, 'DG2: a faster and more accurate differential grouping for large-scale black-box optimization', IEEE Transactions on Evolutionary Computation, vol. 21, no. 6, pp. 929-942.


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Collection: Journal Articles

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Title DG2: a faster and more accurate differential grouping for large-scale black-box optimization
Author(s) Omidvar, M
Yang, M
Mei, Y
Li, X
Yao, X
Year 2017
Journal name IEEE Transactions on Evolutionary Computation
Volume number 21
Issue number 6
Start page 929
End page 942
Total pages 14
Publisher IEEE
Abstract Identification of variable interaction is essential for an efficient implementation of a divide-and-conquer algorithm for large-scale black-box optimization. In this paper, we propose an improved variant of the differential grouping (DG) algorithm, which has a better efficiency and grouping accuracy. The proposed algorithm, DG2, finds a reliable threshold value by estimating the magnitude of roundoff errors. With respect to efficiency, DG2 reuses the sample points that are generated for detecting interactions and saves up to half of the computational resources on fully separable functions. We mathematically show that the new sampling technique achieves the lower bound with respect to the number of function evaluations. Unlike its predecessor, DG2 checks all possible pairs of variables for interactions and has the capacity to identify overlapping components of an objective function. On the accuracy aspect, DG2 outperforms the state-of-the-art decomposition methods on the latest large-scale continuous optimization benchmark suites. DG2 also performs reliably in the presence of imbalance among contribution of components in an objective function. Another major advantage of DG2 is the automatic calculation of its threshold parameter ( ϵ ), which makes it parameter-free. Finally, the experimental results show that when DG2 is used within a cooperative co-evolutionary framework, it can generate competitive results as compared to several state-of-the-art algorithms.
Subject Neural, Evolutionary and Fuzzy Computation
Keyword(s) Cooperative co-evolution
differential grouping (DG)
large-scale global optimization
problem decomposition
DOI - identifier 10.1109/TEVC.2017.2694221
Copyright notice © 2017 IEEE This work is licensed under a Creative Commons Attribution 3.0 License.
ISSN 1089-778X
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