Volume 10 • Issue 1 • PP: 37–47 • 2025
Somersaulting Spider Optimizer (SSO): A Nature-Inspired Metaheuristic Algorithm for Engineering Optimization Problems
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© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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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 the 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 is its adaptive energy-management system, which dynamically regulates exploration–exploitation transitions based on solution-improvement patterns and stagnation detection. Complementary adaptive parameters balance global search and local refinement throughout optimization. The algorithm was evaluated on four engineering design benchmarks: pressure vessel, welded beam, three-bar truss, and cantilever beam. Comparisons with the Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), Harris Hawks Optimization (HHO), and Bat Algorithm (BAT) report that SSO outperforms these methods on the test problems. ANOVA and Wilcoxon signed-rank tests assess the significance of the reported performance differences. The results identify SSO as an effective optimization method, with improvements in solution quality, convergence stability, and computational efficiency.
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References
[1] Z. Zhang, J. Zhu, and F. Nie, “A novel hybrid adaptive differential evolution for global optimization,” Scientific Reports, vol. 14, p. 19697, 2024.
[2] A. G. Hussien, A. Pop, S. Kumar, F. A. Hashim, and G. Hu, “A novel artificial electric field algorithm for solving global optimization and real-world engineering problems,” Biomimetics, vol. 9, no. 3, p. 186, 2024.
[3] V. Chandran and P. Mohapatra, “A novel multi-strategy ameliorated quasi-oppositional chaotic tunicate swarm algorithm for global optimization and constrained en-gineering applications,” Heliyon, vol. 10, no. 10, p. e30757, 2024.
[4] K. Kalita, J. V. N. Ramesh, L. Cepova, S. B. Pandya, P. Jangir, and L. Abualigah, “Multi-objective expo-nential distribution optimizer (moedo): A novel math-inspired multi-objective algorithm for global optimiza-tion and real-world engineering design problems,” Sci-entific Reports, vol. 14, p. 1816, 2024.
[5] P. Yan, J. Zhang, and T. Zhang, “Nature-inspired ap-proach: A novel rat optimization algorithm for global optimization,” Biomimetics, vol. 9, no. 12, p. 732, 2024.
[6] R. Liu, R. Fang, T. Zeng et al., “A novel adaptive sand cat swarm optimization algorithm for feature selection and global optimization,” Biomimetics, vol. 9, no. 11, p. 701, 2024.
[7] I. Priyadarshini, “Dendritic growth optimization: A novel nature-inspired algorithm for real-world optimiza-tion problems,” Biomimetics, vol. 9, no. 3, p. 130, 2024.
[8] M. H. Amiri, N. Mehrabi Hashjin, M. Montazeri, S. Mir-jalili, and N. Khodadadi, “Hippopotamus optimization algorithm: A novel nature-inspired optimization algo-rithm,” Scientific Reports, vol. 14, p. 5032, 2024.
[9] M. Nemati, Y. Zandi, and A. S. Agdas, “Application of a novel metaheuristic algorithm inspired by stadium spectators in global optimization problems,” Scientific Reports, vol. 14, p. 3078, 2024.
[10] O. Altay and E. V. Altay, “A novel chaotic transient search optimization algorithm for global optimization, real-world engineering problems and feature selection,” PeerJ Computer Science, vol. 9, p. e1526, 2023.
[11] G. K. Sahoo, S. Choudhury, R. S. Rathore, and M. Ba-jaj, “A novel prairie dog-based meta-heuristic optimiza-tion algorithm for improved control, better transient response, and power quality enhancement of hybrid microgrids,” Sensors, vol. 23, no. 13, p. 5973, 2023.
[12] Y. Gao, H. Zhang, Y. Duan, and H. Zhang, “A novel hybrid PSO based on levy flight and wavelet mutation for global optimization,” PLOS ONE, vol. 18, no. 1, p. e0279572, 2023.
[13] A. M. Khalid, K. M. Hosny, and S. Mirjalili, “COVI-DOA: A novel evolutionary optimization algorithm based on coronavirus disease replication lifecycle,” Neu-ral Computing and Applications, vol. 34, no. 24, pp. 22 465–22 492, 2022.
[14] W. Zhang and Y. Lan, “A novel memetic algorithm based on multiparent evolution and adaptive local search for large-scale global optimization,” Computational In-telligence and Neuroscience, vol. 2022, p. 3558385, 2022.
[15] P. Trojovsky and M. Dehghani, “Pelican optimization algorithm: A novel nature-inspired algorithm for engi-neering applications,” Sensors, vol. 22, no. 3, p. 855, 2022.
[16] M. M. Draz, O. Emam, and S. M. Azzam, “Software cost estimation prediction using a convolutional neural network and particle swarm optimization algorithm,” Scientific Reports, vol. 14, p. 13129, 2024.
[17] V. H. S. Pham, N. T. Nguyen Dang, and N. V. Nam, “Optimizing time and cost in construction projects with a hybridized multi-verse optimizer and opposition-based learning,” Engineering, Construction and Architectural Management, vol. 32, no. 7, pp. 4852–4886, 2024.
[18] R. Mohamed, M. Abdel-Basset, K. M. Sallam, I. M. Hezam, A. M. Alshamrani, and I. A. Hameed, “Novel hybrid kepler optimization algorithm for parameter es-timation of photovoltaic modules,” Scientific Reports, vol. 14, p. 3453, 2024.
[19] H. Zhang, X. Wang et al., “Mppt control of photovoltaic array based on improved marine predator algorithm under complex solar irradiance conditions,” Scientific Reports, vol. 14, p. 19745, 2024.
[20] H. Rezk, A. Ghani Olabi, T. Wilberforce, and E. Taha Sayed, “Metaheuristic optimization algorithms for real-world electrical and civil engineering applica-tion: A review,” Results in Engineering, vol. 23, p. 102437, 2024.
[21] A. Mathur, R. Kumari, V. P. Meena, V. P. Singh, A. T. Azar, and I. A. Hameed, “Data-driven optimization for microgrid control under distributed energy resource vari-ability,” Scientific Reports, vol. 14, p. 10806, 2024.
[22] N. Karthik, A. Rajagopalan, M. Bajaj et al., “Chaotic self-adaptive sine cosine multi-objective optimization algorithm to solve microgrid optimal energy scheduling problems,” Scientific Reports, vol. 14, p. 18997, 2024.
[23] L. Li, “Lightgbm integration with modified data bal-ancing and whale optimization algorithm for rock mass classification,” Scientific Reports, vol. 14, p. 23028, 2024.
[24] N. Aditya and S. S. Mahapatra, “An adaptive gravita-tional search algorithm for optimizing mechanical engi-neering design and machining problems,” Engineering Applications of Artificial Intelligence, vol. 138, no. Part A, p. 109298, 2024.
[25] Y. Chun, X. Hua, C. Qi, and Y. X. Yao, “Improved marine predators algorithm for engineering design op-timization problems,” Scientific Reports, vol. 14, p. 13000, 2024.
[26] E. H. Houssein, M. K. Saeed, M. M. Al-Sayed, and G. Hu, “Metaheuristics for solving global and engi-neering optimization problems: Review, applications, open issues and challenges,” Archives of Computational Methods in Engineering, vol. 31, pp. 4485–4519, 2024.
[27] A. Bilal, A. Alzahrani, H. Long et al., “Advanced ckd detection through optimized metaheuristic modeling in healthcare informatics,” Scientific Reports, vol. 14, p. 12601, 2024.
[28] J. L. Potharlanka et al., “Feature importance feedback with deep q process in ensemble-based metaheuristic feature selection algorithms,” Scientific Reports, vol. 14, p. 2923, 2024.
[29] B. Gulsun and M. R. Aydin, “Optimizing a machine learning algorithm by a novel metaheuristic approach: A case study in forecasting,” Mathematics, vol. 12, no. 24, p. 3921, 2024.
[30] S. M. Azzam, O. E. Emam, and A. S. Abolaban, “An im-proved differential evolution with sailfish optimizer for handling feature selection problem,” Scientific Reports, vol. 14, p. 13517, 2024.
[31] F. Berchez-Moreno et al., “A memetic dynamic coral reef optimisation algorithm for simultaneous training, design, and optimisation of artificial neural networks,” Scientific Reports, vol. 14, p. 6961, 2024.
[32] F. H. Rizk, M. E. Mohamed, B. Sameh, A. M. Zaki, M. M. Eid, and E.-S. M. El-Kenawy, “Enhancing stu-dent performance prediction with greylag goose opti-mization algorithm,” Proceedings of the 2024 Interna-tional Telecommunications Conference (ITC-Egypt), pp. 32–37, 2024.
[33] M. S. Rani and S. Subramanian, “Meta-heuristic approaches for sentiment analysis,” Expert Systems, vol. 41, no. 8, p. e13592, 2024.
[34] X. Bian, R. Zhang, P. Liu, Y. Xiang, S. Wang, and X. Tan, “Near infrared spectroscopic variable selection by a novel swarm intelligence algorithm for rapid quan-tification of high order edible blend oil,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 284, p. 121788, 2023.
[35] E. H. Cui, Z. Zhang, C. J. Chen et al., “Applications of nature-inspired metaheuristic algorithms for tackling optimization problems across disciplines,” Scientific Reports, vol. 14, p. 9403, 2024.
[36] S. Gopi and P. Mohapatra, “Learning cooking algorithm for solving global optimization problems,” Scientific Reports, vol. 14, p. 13359, 2024.
[37] M. H. Hassan, S. Kamel, and A. W. Mohamed, “En-hanced gorilla troops optimizer powered by marine predator algorithm for global optimization and engi-neering design,” Scientific Reports, vol. 14, p. 7650, 2024.
[38] T. Cai, S. Zhang, Z. Ye, W. Zhou, M. Wang, Q. He, Z. Chen, and W. Bai, “Cooperative metaheuristic algo-rithm for global optimization and engineering problems inspired by heterosis theory,” Scientific Reports, vol. 14, p. 28876, 2024.
[39] X. Wang, Y. Feng, J. Tang, Z. Dai et al., “A uav path planning method based on the framework of multi-objective jellyfish search algorithm,” Scientific Reports, vol. 14, p. 28058, 2024.
[40] D.-N. Truong and J.-S. Chou, “Metaheuristic algo-rithm inspired by enterprise development for global op-timization and structural engineering problems with fre-quency constraints,” Engineering Structures, vol. 318, p. 118679, 2024.
[41] C. Xie, S. Li, X. Qin, S. Fu et al., “Multiple elite strat-egy enhanced rime algorithm for 3d uav path planning,” Scientific Reports, vol. 14, p. 21734, 2024.
[42] C. Jia, L. He, D. Liu, and S. Fu, “Path planning andengineering problems of 3d uav based on adaptive coati optimization algorithm,” Scientific Reports, vol. 14, p. 30717, 2024.
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