MSCSO: A Hybrid Nature-Inspired Algorithm for High-Dimensional Traffic Optimization in Urban Environments: A Hybrid Nature-Inspired Algorithm for High-Dimensional Traffic Optimization in Urban Environments

Kuldeep Vayadande; Viomesh Kumar Singh; Amol Bhosle; Ranjana Gore; Yogesh Uttamrao Bodhe; Aditi Bhat; Zulfikar Charoliya; Aayush Chavan; Pranav Bachhav; Aditya Bhoyar

doi:10.4108/airo.9344

Authors

Kuldeep Vayadande Vishwakarma Institute of Technology, Pune
Viomesh Kumar Singh Vishwakarma Institute of Technology
Amol Bhosle MIT Art, Design and Technology University
Ranjana Gore MIT Art, Design and Technology University
Yogesh Uttamrao Bodhe Government Polytechnic Pune
Aditi Bhat Vishwakarma Institute of Technology
Zulfikar Charoliya Vishwakarma Institute of Technology
Aayush Chavan Vishwakarma Institute of Technology
Pranav Bachhav Vishwakarma Institute of Technology
Aditya Bhoyar Vishwakarma Institute of Technology

DOI:

https://doi.org/10.4108/airo.9344

Keywords:

Hybrid Optimization, animal foraging, sand cat swarm optimization, metaheuristics, traffic optimization

Abstract

Metropolitan regions have experienced higher economical and environmental pressure due to the fasted urbanization leading to increased traffic jams that necessitate the use of higher optimization techniques. Traditional traffic models do not usually take large-dimensional and dynamicity of urban mobility into consideration and require extraordinary computational approaches. Modified Sand Cat Swarm Optimization (MSCSO) improves the Sand Cat Swarm Optimization (SCSO) algorithm that adds Levy flights to global exploration and roulette wheel selection to adaptive exploitation to solve problems that are complex and high-dimensional. When used in urban traffic management, MSCSO works with enormous volumes of traffic, speed, weather, and incident, all of which may decrease Travel Time Index by 15 percent during rush hours. Benchmark tests are used to prove that MSCSO is better, scoring 0.0 in Sphere, Ackley and Rastrigin functions, and 28.0753 in Rosenbrock, whereas higher scores belong to Particle Swarm Optimization, Genetic Algorithms, Ant Colony Optimization and SCSO (e.g., 46). It supports urban planning, since a Flask-based web interface has the possibility to input and visualize real time traffic data in a simple way. The success of MSCSO is reliant on high-quality data and hardware-friendly algorithms but can scale to use real-time data sources, such as from GPS, machine learning traffic projections, and cloud hosting, and is of potential use in logistics, energy delivery, and resource assignment.

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References

[1] Seyyedabbasi A, Tareq WZ, Bacanin N. An effective hybrid metaheuristic algorithm for solving global optimization algorithms. Multimedia Tools Appl. 2024;83:85103–38.

[2] Cai Y, Guo C, Chen X. An improved sand cat swarm optimization with lens opposition-based learning and sparrow search algorithm. Sci Rep. 2024;14:20690.

[3] Wu D, Rao H, Wen C, Jia H, Liu Q, Abualigah L. Modified sand cat swarm optimization algorithm for solving constrained engineering optimization problems. Mathematics. 2022;10(22):4350.

[4] Yao L, Yang J, Yuan P, Li G, Lu Y, Zhang T. Multi-strategy improved sand cat swarm optimization: global optimization and feature selection. Biomimetics. 2023;8(6):492.

[5] Kaveh A. Advances in metaheuristic algorithms for optimal design of structures. 3rd ed. Cham: Springer; 2021.

[6] Li Y, Yu Q, Du Z. Sand cat swarm optimization algorithm and its application integrating elite decentralization and crossbar strategy. Sci Rep. 2024;14:8927.

[7] Mohammed A, Onieva E,Woźniak M. Selective ensemble of classifiers trained on selective samples. Neurocomputing. 2022;482:197–211.

[8] Zhang K, He Y, Wang Y, Sun C. Improved multistrategy sand cat swarm optimization for solving global optimization. Biomimetics. 2024;9(5):280.

[9] Hakemi S, Houshmand M, KheirKhah E, Hosseini SA. A review of recent advances in quantum-inspired metaheuristics. Evol Intell. 2024;17:627–42.

[10] Liu R, et al. A novel adaptive sand cat swarm optimization algorithm for feature selection and global optimization. Biomimetics. 2024;9(11):701.

[11] Shaban A, Ibrahim I. Swarm intelligence algorithms: a survey of modifications and applications. Int J SciWorld. 2025;11:59–65. doi:10.14419/vhckcq86.

[12] Greenshields BD. A study of traffic capacity. Highway Res Board Proc. 1935;14:448–77.

[13] Papadimitriou CH, Steiglitz K. Combinatorial Optimization: Algorithms and Complexity. Mineola, NY: Dover Publications; 2007.

[14] Bhattacharyya T, Chatterjee B, Singh P, Yoon J, Geem ZW, Sarkar R. Mayfly in harmony: A new hybrid meta-heuristic feature selection algorithm. IEEE Access. 2020;8:195929–45. doi:10.1109/ACCESS.2020.3031718.

[15] Dorigo M, Maniezzo V, Colorni A. Ant system: Optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B Cybern. 1996 Feb;26(1):29–41.

[16] Rida N, Mohammed Q, Hasbi A. Ant colony optimization for real-time traffic lights control on a single intersection. Int J Interact Mob Technol. 2020;14(2):196–203. doi:10.3991/ijim.v14i02.10332.

[17] Seyyedabbasi A, Kiani F. Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems. Eng Comput. 2022;39. doi:10.1007/s00366-022-01604-x.

[18] Wu D, Rao H,Wen C, Jia H, Liu Q, Abualigah L. Modified Sand Cat Swarm Optimization Algorithm for solving constrained engineering optimization problems. Mathematics. 2022;10:4350. doi:10.3390/math10224350.

[19] Sreenatha M, Ramalinga Y. Analysis of traffic data at uncontrolled junction and recommending suitable remedies for the junction—A case study at Veera Savarkar Flyover Junction—Yelahanka New Town, Bengaluru. In: Proc Int Conf Sustainable Expert Systems. 2024. doi:10.1007/978-981-97-0072-1_44.

[20] Wu Y. Enhancing urban traffic flow through fuzzy logic based signal light control optimization. Int J e-Collab. 2024;20:1–13. doi:10.4018/IJeC.358746.

[21] Jamil M, Yang XS. A literature survey of benchmark functions for global optimization problems. Int J Math Model Numer Optim. 2013;4. doi:10.1504/IJMMNO.2013.055204.

[22] Yang XS. Nature-Inspired Metaheuristic Algorithms. 2nd ed. Luniver Press; 2010.

[23] Kennedy J, Eberhart R. Particle swarm optimization. In: Proc IEEE Int Conf Neural Networks. 1995;4:1942–8.

[24] Holland JH. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Cambridge, MA: MIT Press; 1992.

[25] Dorigo M, Birattari M, Stützle T. Ant colony optimization: Artificial ants as a computational intelligence technique. IEEE Comput Intell Mag. 2006;1:28–39. doi:10.1109/CI-M.2006.248054.

[26] Kiani F, Nematzadeh S, Anka FA, Findikli MA. Chaotic sand cat swarm optimization. Mathematics. 2023;11(10):2340. doi:10.3390/math11102340.

[27] Ishtaiwi A, Al-Shamayleh AS, Fakhouri HN. A hybrid JADE-sine cosine approach for advanced metaheuristic optimization. Appl Sci. 2024;14(22):10248. doi:10.3390/app142210248.

[28] Trojovský P, Dehghani M. A new bio-inspired metaheuristic algorithm for solving optimization problems based on walruses behavior. Sci Rep. 2023;13:8775. doi:10.1038/s41598-023-35863-5.

[29] Kulkarni A, Gandomi A. Handbook of Formal Optimization Reference Work. Singapore: Springer; 2024. doi:10.1007/978-981-97-3820-5.

[30] Rajwar K, Deep K, Das S. An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges. Artif Intell Rev. 2023;56:13187–257. doi:10.1007/s10462-023-10470-y.

[31] Rahnamayan S, Tizhoosh H, Salama MMA. Opposition based differential evolution algorithms. In: Proc IEEE Congress on Evolutionary Computation (CEC). Vancouver, Canada; 2006. p. 2011–8. doi:10.1109/CEC.2006.1688554.

[32] Chong H, Yap HJ, Tan S, Yap K, Wong S. Advances of metaheuristic algorithms in training neural networks for industrial applications. Soft Comput. 2021;25:11203–24. doi:10.1007/s00500-021-05886-z.

[33] Dehghani M, Trojovská E, Trojovský P. A new humanbased metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process. Sci Rep. 2022;12:9924. doi:10.1038/s41598-022-14225-7.

[34] K. Pichaimani and S. T. Kannan, “Revitalizing Image Retrieval: AI Enhancement and Metaheuristic Algorithm Adaptation”, EAI Endorsed Trans IoT, vol. 11, Jan. 2025.

[35] Narang P, Singh AV, Monga H. Integrating Metaheuristics and Two-Tiered Classification for Enhanced Fake News Detection with Feature Optimization . EAI Endorsed Scal Inf Syst [Internet]. 2024 Apr. 3 [cited 2025 Jun. 22];11(6).

[36] Sharma S, Singh G. Diagnosis of cardiac arrhythmia using Swarm-intelligence based Metaheuristic Techniques: A comparative analysis. EAI Endorsed Trans Perv Health Tech [Internet]. 2020 Sep. 22 [cited 2025 Jun. 22];6(23):e7.

[37] A. H. Khan, S. Li and X. Luo, "Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach," in IEEE Transactions on Industrial Informatics, vol. 16, no. 7, pp. 4670-4680, July 2020, doi: 10.1109/TII.2019.2941916.

[38] A. H. Khan, X. Cao, S. Li, V. N. Katsikis and L. Liao, "BAS-ADAM: an ADAM based approach to improve the performance of beetle antennae search optimizer," in IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 2, pp. 461-471, March 2020, doi: 10.1109/JAS.2020.1003048.

[39] Ameer Tamoor Khan, Shuai Li, Xinwei Cao, Control framework for cooperative robots in smart home using bio-inspired neural network, Measurement, Volume 167, 2021, 108253, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2020.108253.

[40] A. Dwivedi, A. T. Khan, and S. Li, “Comparative Analysis of BAS and PSO in Image Transformation Optimization”, EAI Endorsed Trans AI Robotics, vol. 4, May 2025.

[41] N. Rathod and S. Wankhade, “Quality Analysis of Extreme Learning Machine based on Cuckoo Search and Invasive Weed Optimization”, EAI Endorsed Trans AI Robotics, vol. 1, p. e9, May 2022.