Open Access

ARTICLE

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 Zhang^1,4,*, 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:

(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

Received 20 February 2024; Accepted 17 May 2024; Issue published 20 June 2024

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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Keywords

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