Adaptable catch fish optimization algorithm: A multi-strategy metaheuristic approach for engineering design optimization
Journal of Engineering Research (Kuwait), cilt.14, sa.2, ss.1710-1734, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 14 Sayı: 2
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.jer.2026.04.040
- Dergi Adı: Journal of Engineering Research (Kuwait)
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Arab World Research Source, Directory of Open Access Journals, Academic Search Ultimate (EBSCO)
- Sayfa Sayıları: ss.1710-1734
- Anahtar Kelimeler: Metaheuristic optimization, Catch fish optimization, Engineering design optimization, Multi-strategy search, Customer service optimization
- Kırklareli Üniversitesi Adresli: Evet
Özet
In this study, an enhanced version of the Catch Fish Optimization Algorithm (CFOA), named the Adaptable Catch Fish Optimization Algorithm (A-CFOA), is proposed. A-CFOA integrates a multi-strategy search approach, adaptive parameter control, and advanced diversity preservation mechanisms to improve the exploration–exploitation balance of the original algorithm. The algorithm divides the population into layers based on the median fitness value and applies specialized strategies: Coordinate-Based Local Search (CBLS) and Levy Flight-Based Search (LFBS) for high-quality solutions, and Differential Evolution-Style Mutation (DESM) for low-quality ones. Additionally, Opposition-Based Learning (10% probability) and periodic restart mechanisms strengthen long-term performance. The performance of A-CFOA was evaluated on the CEC 2022 benchmark functions and compared with eight metaheuristic algorithms (EO, GWO, HGSO, OOA, PFA, POA, SCSO, and CFOA). The results indicate that A-CFOA achieved competitive overall performance across the 12 test functions. In particular, for the hybrid functions (F6–F8), A-CFOA showed strong performance relative to the compared algorithms. A-CFOA was also tested on five real-world engineering design problems alongside eight algorithms (ARO, CoatiOA, FOX, HBA, TSO, WOA, PSO, and CFOA). In the industrial cooling system design problem, where most algorithms showed considerable difficulty, A-CFOA achieved the best average performance, with an average value of 0.033856. Moreover, in the Customer Service Response Optimization problem based on real-world data, A-CFOA attained the best result of 0.1124 and the highest average value of 0.1021. Overall comparisons involving 15 different algorithms indicate that A-CFOA is a competitive optimization algorithm within the tested benchmark and application settings.