TY  - JOUR
AU  - Zhang, Chen
AU  - Liu, Liming
AU  - Yang, Yufei
AU  - Sun, Yu
AU  - Ning, Jiaxu
AU  - Zhang, Yu
AU  - Zhang, Changsheng
AU  - Guo, Ying
PY  - 2024
TI  - An Opposition-Based Learning-Based Search Mechanism for Flying Foxes Optimization Algorithm
JO  - Computers, Materials & Continua
SN  - 1546-2218
SP  - 5201
EP  - 5223
VL  - 79
IS  - 3
AB  - 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.
UR  - https://doi.org/10.32604/cmc.2024.050863
DO  - 10.32604/cmc.2024.050863