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Algorithms 2018, 11(4), 47; https://doi.org/10.3390/a11040047

A Novel Dynamic Generalized Opposition-Based Grey Wolf Optimization Algorithm

1,2,3,* , 1,2
and
1,2
1
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
2
Key Laboratory of Technology for Autonomous Underwater Vehicles, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Received: 12 March 2018 / Revised: 9 April 2018 / Accepted: 9 April 2018 / Published: 13 April 2018

Abstract

To enhance the convergence speed and calculation precision of the grey wolf optimization algorithm (GWO), this paper proposes a dynamic generalized opposition-based grey wolf optimization algorithm (DOGWO). A dynamic generalized opposition-based learning strategy enhances the diversity of search populations and increases the potential of finding better solutions which can accelerate the convergence speed, improve the calculation precision, and avoid local optima to some extent. Furthermore, 23 benchmark functions were employed to evaluate the DOGWO algorithm. Experimental results show that the proposed DOGWO algorithm could provide very competitive results compared with other analyzed algorithms, with a faster convergence speed, higher calculation precision, and stronger stability. View Full-Text
Keywords: grey wolf optimizer; generalized opposition-based learning; function optimization; heuristic algorithm; meta-heuristic grey wolf optimizer; generalized opposition-based learning; function optimization; heuristic algorithm; meta-heuristic
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Xing, Y.; Wang, D.; Wang, L. A Novel Dynamic Generalized Opposition-Based Grey Wolf Optimization Algorithm. Algorithms 2018, 11, 47.

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