Projects per year
Abstract
Balancing exploration and exploitation according to evolutionary states is crucial to meta-heuristic search (M-HS) algorithms. Owing to its simplicity in theory and effectiveness in global optimization, gravitational search algorithm (GSA) has attracted increasing attention in recent years. However, the tradeoff between exploration and exploitation in GSA is achieved mainly by adjusting the size of an archive, named Kbest, which stores those superior agents after fitness sorting in each iteration. Since the global property of Kbest remains unchanged in the whole evolutionary process, GSA emphasizes exploitation over exploration and suffers from rapid loss of diversity and premature convergence. To address these problems, in this paper, we propose a dynamic neighborhood learning (DNL) strategy to replace the Kbest model and thereby present a DNL-based GSA (DNLGSA). The method incorporates the local and global neighborhood topologies for enhancing the exploration and obtaining adaptive balance between exploration and exploitation. The local neighborhoods are dynamically formed based on evolutionary states. To delineate the evolutionary states, two convergence criteria named limit value and population diversity, are introduced. Moreover, a mutation operator is designed for escaping from the local optima on the basis of evolutionary states. The proposed algorithm was evaluated on 27 benchmark problems with different characteristic and various difficulties. The results reveal that DNLGSA exhibits competitive performances when compared with a variety of state-of-the-art M-HS algorithms. Moreover, the incorporation of local neighborhood topology reduces the numbers of calculations of gravitational force and thus alleviates the high computational cost of GSA.
Original language | English |
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Pages (from-to) | 436-447 |
Number of pages | 12 |
Journal | IEEE Transactions on Cybernetics |
Volume | 48 |
Issue number | 1 |
Early online date | 30 Dec 2016 |
DOIs | |
Publication status | Published - 30 Jan 2018 |
Keywords
- convergence criterion
- dynamic neighborhood
- evolutionary states
- gravitational search algorithm (GSA)
- topology
Fingerprint
Dive into the research topics of 'A dynamic neighborhood learning-based gravitational search algorithm'. Together they form a unique fingerprint.Projects
- 2 Finished
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VIP-STB Farm
Ren, J.
STFC Science and Technology Facilities Council
1/08/18 → 31/03/19
Project: Research
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KTP - Innovent
Ren, J. & Marshall, S.
KTP Govt (TSB), Innovent Technology Limited
8/01/18 → 31/01/21
Project: Research
Research output
- 72 Citations
- 5 Article
-
MIMR-DGSA: unsupervised hyperspectral band selection based on information theory and a modified discrete gravitational search algorithm
Tschannerl, J., Ren, J., Yuen, P., Sun, G., Zhao, H., Yang, Z., Wang, Z. & Marshall, S., 1 Nov 2019, In: Information Fusion . 51, p. 189-200 12 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile57 Citations (Scopus)11 Downloads (Pure) -
Coastal wetland mapping with Sentinel-2 MSI imagery based on gravitational optimized multilayer perceptron and morphological attribute profiles
Zhang, A., Sun, G., Ma, P., Jia, X., Ren, J., Huang, H. & Zhang, X., 20 Apr 2019, In: Remote Sensing. 11, 8, 23 p., 952.Research output: Contribution to journal › Article › peer-review
Open AccessFile18 Citations (Scopus)18 Downloads (Pure) -
Hyperspectral band selection using crossover based gravitational search algorithm
Zabalza, J., Zhang, A., Ma, P., Liu, S., Sun, G., Huang, H., Wang, Z. & Lin, C., 17 Aug 2018, (E-pub ahead of print) In: IET Image Processing.Research output: Contribution to journal › Article › peer-review
Open AccessFile15 Citations (Scopus)38 Downloads (Pure)
Activities
- 1 Hosting an academic visitor
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Genyun Sun
Jinchang Ren (Host)
Dec 2016 → Dec 2017Activity: Hosting a visitor types › Hosting an academic visitor