Şahin balığı algoritması: Çift uygunluk fonksiyonlu yeni bir matematiksel optimizasyon algoritması

Inspired by the gender shift behaviour shown in Hawkfish, this thesis contributes a new concept in optimization algorithm modelling by using this natural phenomenon as a basis for solving optimization problems through mathematical modelling and imitation. To evaluate the performance of HFOA, it is initially subjected to benchmarking against various test problems, including the CEC-06 (2019) benchmark suite, to quantitatively and qualitatively validate its efficacy. Subsequently, HFOA is deployed to identify the optimal configurations for the welded beam design problem and the tension/compression spring (TCS) design problem, showcasing its practical utility. The outcomes reveal that the proposed HFOA algorithm consistently outperforms well-established and recent algorithms documented in the literature. Furthermore, the results obtained in real-world applications underscore the effectiveness of HFOA in tackling problems characterized by unknown search spaces.