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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 705238, 9 pages
Research Article

UCAV Path Planning by Fitness-Scaling Adaptive Chaotic Particle Swarm Optimization

1School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China
2School of Information Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
3School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210046, China

Received 10 April 2013; Revised 15 June 2013; Accepted 28 June 2013

Academic Editor: Saeed Balochian

Copyright © 2013 Yudong Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Path planning plays an extremely important role in the design of UCAVs to accomplish the air combat task fleetly and reliably. The planned path should ensure that UCAVs reach the destination along the optimal path with minimum probability of being found and minimal consumed fuel. Traditional methods tend to find local best solutions due to the large search space. In this paper, a Fitness-scaling Adaptive Chaotic Particle Swarm Optimization (FAC-PSO) approach was proposed as a fast and robust approach for the task of path planning of UCAVs. The FAC-PSO employed the fitness-scaling method, the adaptive parameter mechanism, and the chaotic theory. Experiments show that the FAC-PSO is more robust and costs less time than elite genetic algorithm with migration, simulated annealing, and chaotic artificial bee colony. Moreover, the FAC-PSO performs well on the application of dynamic path planning when the threats cruise randomly and on the application of 3D path planning.