Structural optimization of aircraft components using chaotic pangolin optimizer

This study introduces an enhanced chaotic pangolin optimizer (MCPO), a nature-inspired metaheuristic algorithm integrated with chaotic maps to solve complex constrained and multimodal engineering design problems. The MCPO simulates the luring and predation behavior of the pangolin in a two-stage framework, enhanced with chaos-based dynamics to improve the balance between exploration and exploitation. Aircraft components and systems are designed and optimized to be lightweight and high-load-carrying. Accordingly, the aircraft component attached to the wing is optimized for stress-carrying capacity and weight. The stress distribution and volume reduction are studied to maintain the system's overall dynamics and balance. The MCPO achieved effective results in terms of the component's overall weight and cumulative stresses. The MCPO realized a 1997 g weight of the component, which was 8.6 %, 8.5 %, and 7.8 % lower than the Ship rescue algorithm, Hiking algorithm, and Starfish algorithm, respectively. Furthermore, the weight of 585 g (from 2,582 g to 1,997 g) is reduced compared to the initial design. These results affirm the algorithm's robustness, precision, and versatility across diverse real-world design domains.