Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization Algorithms


  • Chika Yinka-Banjo University of Lagos
  • U. Agwogie


Swarm intelligence, Fruit fly algorithm, Ant Colony optimization, Particule swarm optimization, Optimal path, Mobile robot


This article presents the implementation and comparison of fruit fly optimization (FOA), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms in solving the mobile robot path planning problem. FOA is one of the newest nature-inspired algorithms while PSO and ACO has been in existence for a long time. PSO has been shown by other studies to have long search time while ACO have fast convergence speed. Therefore there is need to benchmark FOA performance with these older nature-inspired algorithms. The objective is to find an optimal path in an obstacle free static environment from a start point to the goal point using the aforementioned techniques. The performance of these algorithms was measured using three criteria: average path length, average computational time and average convergence speed. The results show that the fruit fly algorithm produced shorter path length (19.5128 m) with faster convergence speed (3149.217 m/secs) than the older swarm intelligence algorithms. The computational time of the algorithms were in close range, with ant colony optimization having the minimum (0.000576 secs).


AbWahab M. N.; S. Nefti-Meziani and A. Atyabi. (2015). A Comprehensive Review of Swarm Optimization Algorithms. PLOS ONE 10(5): 1-36.

Ajeil F.H.; I. K. Ibraheem and M. A. Sahib. (2020). Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm, Applied Soft Computing Journal, 89, 1-27.

Allah, R. M. (2016). Hybridization of Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Nonlinear Programming Problems. International Journal of Swarm Intelligence and Evolutionary Computation, 5(2), 1-10.

Blum, C. (2005). Ant Colony Optimization: Introduction and recent trends. Elsevier, Physics of Life Review 2, 353–373.

Cholodowicz E. and Figureurowski D. (2017). Mobile Robot Path Planning with Obstacle Avoidance using Particle Swarm Optimization. Research Gate, DOI: 10.14313/PAR_225/59, 59–68.

Closet H. (2007). Robotic Motion Planning: Cell Decompositions. Available online at:, Accessed on June 7, 2020.

Connors, J. and Elkaim G. (2007). Manipulating B-Spline Based Paths for Obstacle Avoidance in Autonomous Ground Vehicles, Proceedings of the National Technical Meeting of The Institute of Navigation, San Diego, CA, 1081-1088

Gangadharan M. M. and Salgaonkar A. (2020). Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments: University of Mumbai, India. Engineering Reports published by John Wiley & Sons, Ltd.

Hazim I. and Mesut, G. (2014). Parameter Analysis on Fruit Fly Optimization Algorithm. Journal of Computer and Communications, 2: 137-141.

Kan E.; M. Lim; S. Yeo; J. Ho and Z. Shao. (2011). Contour Based Path Planning with B-Spline Trajectory Generation for Unmanned Aerial Vehicles (UAVs) over Hostile Terrain. Journal of Intelligent Learning Systems and Applications, 3(3): 122-130. doi: 10.4236/jilsa.2011.33014

Li, Y. and Han, M. (2020). Improved fruit fly algorithm on structural optimization. Brain Informatics, 7(1): 1-13.

Mansi, A. and Priyanka, G. (2013). Path planning of Mobile robots using Bee Colony Algorithm. MIT International Journal of Computer Science & Information Technology, 3(2): 86–89.

Narendra, S. P. and Sanjeev, S. (2013). Robot Path planning using Swarm Intelligence: A Survey. International Journal of Computer Applications 83(12): 0975 – 8887.

Pratap, B. S.; V. R. Harsha and M. Amitabha. (2013). Voronoi Diagram Based Roadmap Motion Planning. Available online at: Accessed on June 7, 2020.

Qinghai, B. (2010). Analysis of Particle Swarm Optimization Algorithm. Computer and Information Science, 3(1): 180-184.

Rizk, M. A. (2016). Hybridization of Fruit Fly Optimization Algorithm and Firefly algorithm for Solving Nonlinear Programming Problems. International Journal of Swarm Intelligence and Evolutionary Computation 5(2): 1-10, DOI: 10.4172/2090-4908.1000134.

Sheng-Xiang, L.; Z. Yu‑Rong and W. Lin. (2018). An effective fruit fly optimization algorithm with hybrid information exchange and its applications. International Journal of Machine Learning and Cybernetics. 9 (10): 1623-1648.

Shui-ping, Z.; C. Yang and G. Yang-dan. (2016). Fruit fly algorithm Based on Extremal optimization. 12th International Conference on Computational Intelligence and Security, Chicago, USA. 534-537.

Wen–Tsao, P. (2014). A New Evolutionary Computation – Fruit Fly optimization Algorithm second edition. Taiwan, China: Canghai Press.

Xing, G.; Z. Jian; L. Wei and Z. Yiwen. (2017). A fruit fly optimization algorithm with a traction mechanism and its applications. International journal of distributed sensor network. 13(11): 1-12, DOI: 10.1177/1550147717739831.

Ye, F.; X.Y. Lou and L.F. Sun. (2017). An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications. PLOS ONE, 12(4): 1-36.

Yue, L. and Chen, H. (2019). Unmanned vehicle path planning using a novel ant colony algorithm EURASIP Journal on Wireless Communications and Networking. 136, 1-9.

Zhang, L.; L. Liu; X. Yang and Y. Dai. (2016). A Novel Hybrid Firefly Algorithm for Global Optimization.11(9): 1-17.

Additional Files