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Learning Sparrow Algorithm With Non-Uniform Search for Global Optimization
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Author(s): Yifu Chen (School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China), Jun Li (School of Artificial Intelligence, Jiangxi University of Applied Science, Nanchang, China)and Lin Zhang (School of Software Engineering, Jiangxi University of Science and Technology, Nanchang, China)
Copyright: 2023
Volume: 14
Issue: 1
Pages: 31
Source title:
International Journal of Swarm Intelligence Research (IJSIR)
Editor(s)-in-Chief: Yuhui Shi (Southern University of Science and Technology (SUSTech), China)
DOI: 10.4018/IJSIR.315636
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Abstract
Sparrow Algorithm as a New Swarm Intelligence Search Algorithm, the sparrow algorithm has good optimization ability, but in complex environments, it still has certain limitations, such as weak learning ability. Therefore, this paper proposes a learning sparrow search algorithm for non-uniform search(Sparrow search algorithm with non-uniform search, NSSSA). A learning behavior selection strategy is proposed, and saltation learning and a random walk learning are introduced respectively.To a certain extent, the algorithm avoided alling into the local optimum, and a non-uniform variable spiral search is proposed to balance the development and search capabilities of the algorithm. In the experimental simulation, the effectiveness of the NSSSA algorithm is verified by using the benchmark function, and it is tested on the CEC 2013 test set. Compared with the algorithms with better performance in recent years, the results show that the NSSSA algorithm has better universality . Finally, the NSSSA algorithm is applied to the WSN coverage optimization problem. The results show that NSSSA achieves more than 90% and 96% coverage on the two models of 50×50 and 100×100, respectively, which verifies the practicability of the algorithm.
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