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An Opposition-Based Learning-Based Search Mechanism for Flying Foxes Optimization Algorithm

Chen Zhang1, Liming Liu1, Yufei Yang1, Yu Sun1, Jiaxu Ning2, Yu Zhang3, Changsheng Zhang1,4,*, Ying Guo4

1 Software College, Northeastern University, Shenyang, 110169, China
2 School of Information Science and Engineering, Shenyang Ligong University, Shenyang, 110159, China
3 China Telecom Digital Intelligence Technology Co., Ltd., Beijing, 100035, China
4 College of Computer Science and Engineering, Ningxia Institute of Science and Technology, Shizuishan, 753000, China

* Corresponding Author: Changsheng Zhang. Email: email

(This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)

Computers, Materials & Continua 2024, 79(3), 5201-5223. https://doi.org/10.32604/cmc.2024.050863

Abstract

The flying foxes optimization (FFO) algorithm, as a newly introduced metaheuristic algorithm, is inspired by the survival tactics of flying foxes in heat wave environments. FFO preferentially selects the best-performing individuals. This tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search area. To address this issue, the paper introduces an opposition-based learning-based search mechanism for FFO algorithm (IFFO). Firstly, this paper introduces niching techniques to improve the survival list method, which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search capability. Secondly, an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its quality. Finally, to verify the superiority of the improved search mechanism, IFFO, FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test functions. The results prove that compared with other algorithms, IFFO is characterized by its rapid convergence, precise results and robust stability.

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APA Style
Zhang, C., Liu, L., Yang, Y., Sun, Y., Ning, J. et al. (2024). An opposition-based learning-based search mechanism for flying foxes optimization algorithm. Computers, Materials & Continua, 79(3), 5201-5223. https://doi.org/10.32604/cmc.2024.050863
Vancouver Style
Zhang C, Liu L, Yang Y, Sun Y, Ning J, Zhang Y, et al. An opposition-based learning-based search mechanism for flying foxes optimization algorithm. Comput Mater Contin. 2024;79(3):5201-5223 https://doi.org/10.32604/cmc.2024.050863
IEEE Style
C. Zhang et al., "An Opposition-Based Learning-Based Search Mechanism for Flying Foxes Optimization Algorithm," Comput. Mater. Contin., vol. 79, no. 3, pp. 5201-5223. 2024. https://doi.org/10.32604/cmc.2024.050863



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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