Path Planning for Autonomous Navigation in Grid Environments Using Salp Swarm Algorithm and Genetic Algorithm

Global path planning for autonomous navigation aims to generate safe, efficient, and collision-free routes, particularly in complex and dynamic environments where uncertainty and obstacles pose significant challenges. Among various environment modeling techniques, grid-based map representations are widely adopted due to their simplicity, scalability, and effectiveness in discretizing space for computational processing. This work presents a hybrid path planning approach that integrates the Salp Swarm Algorithm (SSA) with a Genetic Algorithm (GA) to enhance navigation performance in grid-based environments. The proposed method leverages the strong global exploration capability of SSA to initially identify promising and feasible paths across the search space. These candidate solutions are then utilized to initialize the Genetic Algorithm, which further refines the paths through evolutionary operations such as selection, crossover, and mutation. This cooperative integration enables a balanced trade-off between exploration and exploitation, improving convergence behavior and avoiding premature stagnation. As a result, the hybrid SSA-GA approach produces higher-quality paths in terms of reduced path length, improved smoothness, better convergence stability, and enhanced computational efficiency. The performance of the proposed method is evaluated on six planar grid maps with progressively increasing size and complexity to test robustness and scalability. Comparative analysis is conducted against well-known algorithms including A-star (A*), Ant Colony Optimization (ACO), Genetic Algorithm (GA), Salp Swarm Algorithm (SSA), Improved Sparrow Search Algorithm (ISpSA), Proposed Grey Wolf Optimization and Genetic Algorithm (GWO-GA) and proposed Salp Swarm Algoritjm and Genetic Algorithm (SSA-GA) . Experimental results demonstrate that the proposed SSA-GA method achieves a 100% success rate across all tested scenarios, consistently generating feasible solutions where several standalone algorithms fail, while maintaining competitive runtime and superior path optimality.

Autonomous Navigation, Grid-Based Path Planning

1. Badrloo, S., Varshosaz, M., Pirasteh, S., & Li, J. (2022). Image-Based Obstacle Detection Methods for the Safe Navigation of Unmanned Vehicles: A Review. Remote Sensing, 14(15), 3824. https://doi.org/10.3390/rs14153824 [Google Scholar] [Crossref]

2. C. H. Chen and S. Y. Yang, “Neural fuzzy inference systems with knowledge-based cultural differential evolution for nonlinear system control,” Information Sciences, vol. 270, pp. 154–171, 2014. https://doi.org/10.1016/j.ins.2014.02.071 [Google Scholar] [Crossref]

3. Cardoso, J. S., Domingues, I., & Oliveira, H. P. (2015). Closed Shortest Path in the Original Coordinates with an Application to Breast Cancer. International Journal of Pattern Recognition and Artificial Intelligence, 29(01), 1555002. https://doi.org/10.1142/S0218001415550022 [Google Scholar] [Crossref]

4. D. Floreano and J. Urzelai, “Evolutionary robots with on-line self-organization and behavioral fitness,” https://doi.org/10.1016/S0893-6080(00)00032-0 [Google Scholar] [Crossref]

5. Dasgupta, K., Mandal, B., Dutta, P., Mandal, J. K., & Dam, S. (2013). A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing. Procedia Technology, 10, 340–347. https://doi.org/10.1016/j.protcy.2013.12.369 [Google Scholar] [Crossref]

6. Deep, K., & Thakur, M. (2007). A new crossover operator for real coded genetic algorithms. Applied Mathematics and Computation, 188(1), 895–911. https://doi.org/10.1016/j.amc.2006.10.047 [Google Scholar] [Crossref]

7. Gong, W., & Cai, Z. (2013). Differential Evolution With Ranking-Based Mutation Operators. IEEE Transactions on Cybernetics, 43(6), 2066–2081. https://doi.org/10.1109/TCYB.2013.2239988 [Google Scholar] [Crossref]

8. Grujic, Z., & Grujic, B. (2025). Optimal Routing in Urban Road Networks: A Graph-Based Approach Using Dijkstra’s Algorithm. Applied Sciences, 15(8), 4162. https://doi.org/10.3390/app15084162 [Google Scholar] [Crossref]

9. H. Heng and W. Rahiman, “ACO-GA-based optimization to enhance global path planning for autonomous navigation in grid environments,” IEEE Transactions on Evolutionary Computation, 2025. doi: 10.1109/TEVC.2025.3543401 [Google Scholar] [Crossref]

10. Hooshyar, M., & Huang, Y.-M. (2023). Meta-heuristic Algorithms in UAV Path Planning Optimization: A Systematic Review (2018–2022). Drones, 7(12), 687. https://doi.org/10.3390/drones7120687 [Google Scholar] [Crossref]

11. Kashyap, A. K., & Parhi, D. R. (2022). Implementation of intelligent navigational techniques for inter-collision avoidance of multiple humanoid robots in complex environment. Applied Soft Computing, 124, 109001. https://doi.org/10.1016/j.asoc.2022.109001 [Google Scholar] [Crossref]

12. L. Aualigah, M. Shehab, M. Alshinwan, and H. Alabool, “Salp swarm algorithm: A comprehensive survey,” Neural Computing and Applications, 2020. https://doi.org/10.1007/s00521-019-04629-4 [Google Scholar] [Crossref]

13. Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002 [Google Scholar] [Crossref]

14. Mittal, N., Singh, U., & Sohi, B. S. (2016). Modified Grey Wolf Optimizer for Global Engineering Optimization. Applied Computational Intelligence and Soft Computing, 2016, 1–16. https://doi.org/10.1155/2016/7950348 [Google Scholar] [Crossref]

15. Pan, Y., Yang, Y., & Li, W. (2021). A Deep Learning Trained by Genetic Algorithm to Improve the Efficiency of Path Planning for Data Collection With Multi-UAV. IEEE Access, 9, 7994–8005. https://doi.org/10.1109/ACCESS.2021.3049892 [Google Scholar] [Crossref]

16. Parekh, D., Poddar, N., Rajpurkar, A., Chahal, M., Kumar, N., Joshi, G. P., & Cho, W. (2022). A Review on Autonomous Vehicles: Progress, Methods and Challenges. Electronics, 11(14), 2162. https://doi.org/10.3390/electronics11142162 [Google Scholar] [Crossref]

17. R. Saravanan, P. Asokan, and M. Sachidanandam, “A multi-objective genetic algorithm (GA) approach for optimization of surface grinding operations,” International Journal of Machine Tools and Manufacture, vol. 42, 2002. https://doi.org/10.1016/S0890-6955(02)00074-3 [Google Scholar] [Crossref]

18. Sengupta, S., Basak, S., & Peters, R. (2018). Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives. Machine Learning and Knowledge Extraction, 1(1), 157–191. https://doi.org/10.3390/make1010010 [Google Scholar] [Crossref]

19. Sengupta, S., Basak, S., & Peters, R. (2018). Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives. Machine Learning and Knowledge Extraction, 1(1), 157–191. https://doi.org/10.3390/make1010010 [Google Scholar] [Crossref]

20. Walchand College of Engineering, India, A.J., U., P.D., S., & Government College of Engineering, Karad, India. (2015). CROSSOVER OPERATORS IN GENETIC ALGORITHMS: A REVIEW. ICTACT Journal on Soft Computing, 6(1), 1083–1092. https://doi.org/10.21917/ijsc.2015.0150 [Google Scholar] [Crossref]

21. Z. H. Ni, F. S. Li, and H. Wang, “Simplification of the combustion mechanism of Jatropha biodiesel surrogate fuel and reaction path analysis,” Energy, vol. 282, 2023. https://doi.org/10.1016/j.energy.2023.128859 [Google Scholar] [Crossref]

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