Abstract
Artificial immune algorithm and ant colony algorithm are combined to deal with problem of 2D route planning of aircraft. Initial routes are generated randomly within the flying area and clonal selection algorithm is used to search good routes. A group of routes with minimum cost of threat and oil are gained. Some initial pheromone is put nearby these routes. Based on this, ant colony algorithm are used to search optimal route while threaten avoid and minimum cost are taken into consideration.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Du, P., Yang, C.: Introduction of Air Vehicle Path Planning Algorithms. Flight Dynamics 23(2), 10–14 (2005)
Al-Hasan, S., Vachtsevanos, G.: Intelligent Route Planning for Fast Autonomous Vehicles Operating in a Large Natural Terrain. Robotics and Autonomous Systems 40(1), 1–24
Nikolos, I.K., Valavanis, K.P., Tsourveloudis, N.C., et al.: Evolutionary Algorithm Based Offline/Online Path Planner for UAV Navigation. IEEE Transactions on Systems, Man and Cybernetics, Part B 33(6), 898–912 (2003)
Glmore., J.F., Czuchry, A.J.: A Neural Network Model for Route Planning Constraint Integration. In: Proceedings of the IEEE 1992 Neural Networks International Joint Conference, pp. 842–856 (1992)
Duan, H.-B., Zhang, X.-Y., Wu, J., et al.: Max-Min Adaptive Ant Colony Optimization Approach to Multi-UAVs Coordinated Trajectory Replanning in Dynamic and Uncertain Environments. Journal of Bionic Engineering 6(2), 161–173 (2009)
Tian, L.: Research on Algorithm of Multi Route Planning Based on GA for Air Vehicles. Master thesis Shi Jia Zhuang, HeBei Normal University (2006)
Chen, D., Zhou, D., Feng, Q.: Route Planning for Unmanned Aerial Vehicles Based on Particle Swarm Optimization. Journal of Projectiles, Rockets, Missiles and Guidance 27(4), 340–342
Ding, Y., Ren, L.: Artificial Immune Systems: Theory and Applications. Pattern Recognition and Artificial Intelligence 13(1), 52–59 (2000)
Haibin, D.: Ant Colony Algorithms:Theory and Applications, pp. 306–309. Science Press, Beijing (2005)
David, F., Yap, W., Habibullah, A., Koh, S.P., Tiong, S.K.: An Improved Artificial Immune System based on Antibody Remainder method for Mathematical Function ptimization. In: Proceedings of 2010 IEEE Student Conference on Research and Development (SCOReD 2010), Putrajaya, Malaysia, December 13-14 (2010)
Mo, H.: Artificial Immune System. Science Press, Beijing (2009)
de Castro, L.N.: Learning and Optimization Using the Clonal Selection Principle. IEEE Transactions on Evolutionary Computation 6(3) (2002)
Bian, Z., Zhang, X.: Pattern Recognition. Tsinghua University Press, Beijing (2000)
Wen, S., Li, M., Zheng, J.: Comparison and Research of Mutation Operators in Multi-Objective Evolutionary Algorithms. Computer Engineering and Applications 45(2), 74–78 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, Q., Wang, Y. (2011). Route Planning Based on Combination of Artificial Immune Algorithm and Ant Colony Algorithm. In: Wang, Y., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent and Soft Computing, vol 122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25664-6_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-25664-6_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25663-9
Online ISBN: 978-3-642-25664-6
eBook Packages: EngineeringEngineering (R0)