Structural optimization of aircraft noise landing gear using modified Tianji's horse racing optimizer

In this research paper, a Modified Tianji's Horse Optimization Algorithm (MTHOA) for constrained structural optimization of an aircraft nose landing gear fork is presented in this paper with the help of Dynamic Oppositional-Based Learning. Adaptive parameter control and dynamic oppositional learning are used to mathematically improve the exploration-exploitation balance and convergence stability of the original Tianji's Horse Racing Optimizer. The costly finite element-based objective function is approximated using a Kriging surrogate model. The objective function is to minimize the mass of the fork using the modified optimizer followed by topology optimization. The findings show that MTHOA outperforms Whale Optimization Algorithm (9,910g) and Salp Swarm Optimization Algorithm (9,890g) in achieving a minimum structural mass of 9,800g while meeting all stress constraints. The suggested method produces a 17.4% mass reduction with better stress sustainability when compared to the provisional design (11,864g). The results validate MTHOA's robustness, convergence efficiency, and suitability for challenging, nonlinear, multidisciplinary engineering optimization problems.