Document Type : Research Paper


1 Control and Systems Department, University of Technology-Iraq, Baghdad, Iraq.

2 Control and Systems Department, University of Technology-Iraq, Baghdad, Iraq


A modified version of the artificial Bee Colony Algorithm (ABC) was suggested namely Adaptive Dimension Limit- Artificial Bee Colony Algorithm (ADL-ABC). To determine the optimum global path for mobile robot that satisfies the chosen criteria for shortest distance and collision–free with circular shaped static obstacles on robot environment. The cubic polynomial connects the start point to the end point through three via points used, so the generated paths are smooth and achievable by the robot. Two case studies (or scenarios) are presented in this task and comparative research (or study) is adopted between two algorithm’s results in order to evaluate the performance of the suggested algorithm. The results of the simulation showed that modified parameter (dynamic control limit) is avoiding static number of limit which excludes unnecessary Iteration, so it can find solution with minimum number of iterations and less computational time. From tables of result if there is an equal distance along the path such as in case A (14.490, 14.459) unit, there will be a reduction in time approximately to halve at percentage 5%.


[1] P. A.M. Ehlert, “The use of Artificial Intelligence Robots,”Report on research project, Delft University of Technology, Netherlands, October 1999.
[2] Nizar Hadi Abbas and Farah Mahdi Ali, “Path Planning of an Autonomous Mobile Robot using Directed Artificial Bee Colony Algorithm”, International Journal of Computer Applications (0975 – 8887) Vol. 96, No. 11, June 2014 [3] K. Heero, “Path Planning and Learning Strategies for Mobile Robots in Dynamic Partially Unknown Environments,” Ph. D. Thesis, University of Tartu, Estonia, ., 2006. [4] H. Chen, Y. Zhu, and K. Hu, “Adaptive Bacterial Foraging Optimization,” Abstract and Applied Analysis., Vol. 2011, pp. 1–27, 2011. [5] D. Wang, D. Tan, and L. Liu, “Particle swarm optimization algorithm: an overview,” Soft Comput.,V. 22, no. 2, pp. 387–408, 2018. [6] A. M. Husain, S. M. Sohail, and V. S. Narwane, “Path planning of material handling robot using Ant Colony Optimization ( ACO ) technique,” International Journal of Engineering Research and Applications (IJERA) ,Vol. 2, no. 5, pp. 1698–1701, 2012. [7] X. Yang, “Optimization techniques and applications with examples”, JohnWiley & Sons, Inc, USA, pp 1-350, 2018. [8] V. Maheshwari and U. Datta, “Enhanced Artificial Bee Colony Algorithm for Travelling Salesman Problem using Crossover and Mutation,” Int. J. Comput. Appl., Vol. 91, no. 13, pp. 37–40, 2014. [9] M. J. Mohamed, “Enhanced Genetic Algorithm Based on Node Codes for Mobile Robot Path Planning” IJCCCE, Vol. 12, no. 2, pp. 69–80, 2012. [10] I. K. Ibraheem and F. H. Ajeil “Multi-Objective Path Planning of an Autonomous Mobile Robot in Static and Dynamic Environments using a Hybrid PSO-MFB Optimization Algorithm,”Electrical Engineering Department, College of Engineering, University of Baghdad,arXiv:1805.00224v2, no. May, 2018.
[11] N. Buniyamin, N. Sariff, W. N. W. A. J, and Z. Mohamad, “Robot global path planning overview and a variation of ant colony system algorithm,” International journal of mathematicls and computers, Vol. 5, no. 1, 2011. [12] M. Agarwal and P. Goel, “Path Planning of Mobile Robots using Bee Colony Algorithm,” MIT International Journal of Computer Science & Information Technology, Vol. 3, No. 2, August, pp. 86–89 ISSN 2230-7621. 2013 [13] E. García-gonzalo and J. L. Fernández-martínez, “Convergence and stochastic stability analysis of particle swarm optimization variants with generic parameter distributions,” Elsevier/Applied Mathematics and Computation Vol. 249, pp. 286–302, 2014. [14] N.Hadi and F.Mahdi, “Path Planning of an Autonomous Mobile Robot using Enhanced Bacterial Foraging Optimization Algorithm” Al-Khwarizmi Engineering Journal,Vol. 12, No. 4, P.P. 26- 35,2016. [15] M. J. Mohamed, “Enhanced GA for Mobile Robot Path Planning Based on Links among Distributed Nodes.” Eng. & Tech. Journal, Vol.31, No., 2013. [16] I. Brajevi and P.Stanimirovi´c 1, “An improved chaotic firefly algorithm for global numerical optimization,” International Journal of Computational Intelligence Systems, Vol. 12 ,131-148, 2018. [17] L. E. Soong, O. Pauline, and C. K. Chun, “Solving the optimal path planning of a mobile robot using improved Q-learning,” Robotics and Autonomous Systems, Malaysia, 2019. [18] D. Karaboga and B. Gorkemli, “Solving Traveling Salesman Problem by Using Combinatorial Artificial Bee Colony Algorithms,” International Journal on Artificial Intelligence Tools 28 (1). World Scientific Publishing Co. Vol. 28, no. 1, 2019. [19] E. A. Hadi and S. By, “Multi-Objective Decision Maker for Single and Multi-Robot Path Planning,” M.Sc. Thesis, Control and System Dept., University of Technology, Baghdad-Iraq, 2018. [20] P. Sudhakara, V. Ganapathy, and K. Sundaran, “Mobile robot trajectory planning using enhanced artificial bee colony optimization algorithm,” IEEE Int. Conf. Power, Control. Signals Instrum. Eng. ICPCSI 2017, pp. 363–367, 2018. [21] M. H. Saffari and M. J. Mahjoob, “Bee Colony Algorithm for Real-Time Optimal Path Planning of Mobile Robots”, International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, Iran, pp. 2–5, 2009. [22] S. Kumar, V. K. Sharma, and R. Kumari, “An Improved Memetic Search in Artificial Bee Colony Algorithm,” International Journal of Computer Science and Information Technologies,Vol. 5, no. 2, pp. 1237–1247, 2014. [23] N. H. Abbas and B. saleh “Design of a Kinematic Neural Controller for Mobile Robots based on Enhanced Hybrid Firefly-Artificial Firefly Bee Colony Algorithm,” Al-Khwarizmi Journal,Vol. 12,pp. 45–60, 2016. [24] Bai Jing and Liu Hong ,“Improved Artificial Bee Colony Algorithm and Application in Path Planning of Crowd Animation,” International Journal of Control and Automation, Vol. 8, no. 3, pp. 53–66, 2015. [25] N. H. Abbas and F. M. Ali, “Path Planning of an Autonomous Mobile Robot using Directed Artificial Bee Path Planning of an Autonomous Mobile Robot using Directed Artificial Bee Colony Algorithm,” International Journal of Computer Applications , University of Baghdad, Iraq,Vol. 96. December, pp. 10–16, 2016. [26] A. Özkiş, “Performance Analysis of ADL-ABC Algorithm on Continuous Optimization Problems,” International Conference on Systems, Control and Informatics, Turkey,August, 2014.