Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 10.54216/JAIM https://www.americaspg.com/journals/show/4115 2022 2022 Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt Ahmed Ahmed Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt Hala B. Nafea Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt; Faculty of Artificial Intelligence and Informatics, Horus University, New Damietta, 34517, Egypt Hossam El-Din Moustafa Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt; Applied Science Research Center, Applied Science Private University, Amman, Jordan El-Sayed M. El El-Kenawy The growing complexity of engineering optimization problems has revealed significant limitations in traditional mathematical programming approaches, necessitating the development of innovative metaheuristic algorithms capable of handling high-dimensional, multi-modal, and discontinuous objective functions. This paper presents the Somersaulting Spider Optimizer (SSO), a novel bio-inspired metaheuristic algorithm that draws inspiration from the extraordinary locomotion mechanisms of Somersaulting Spider, a desert-dwelling arachnid species renowned for its acrobatic somersaulting capabilities. The proposed algorithm incorporates dual movement mechanisms that effectively balance global exploration through somersaulting behavior and local exploitation via controlled rolling movements. A distinctive feature of SSO lies in its adaptive energy management system, which dynamically regulates exploration-exploitation transitions based on solution improvement patterns and stagnation detection. The algorithm employs complementary adaptive parameters that ensure perfect balance between global search and local refinement throughout the optimization process. Comprehensive experimental evaluation was conducted on four challenging benchmark engineering design problems: pressure vessel design, welded beam optimization, three-bar truss design, and cantilever beam optimization. A comparison with known metaheuristic algorithms, such as the Genetic Algorithm, Whale Optimization Algorithm, Harris Hawks Optimization, and Bat Algorithm, shows that SSO outperforms all of them on the test problems. ANOVA and Wilcoxon signed-rank tests statistically validate the significance of performance improvement, and SSO has the lowest optimization cost without compromising the high-performance consistency. The results confirm that SSO is an effective and powerful optimization method for complex engineering design problems, and that the method shows significant improvements in solution quality, convergence stability, and computational efficiency. 2025 2025 91 120 10.54216/JAIM.100105 https://www.americaspg.com/articleinfo/28/show/4115