UCAV path planning based on FSCABC

Path planning plays an extremely important role in the design of UCAV to accomplish the air combat task fleetly and reliably. The planned path should ensure UCAV reach the destination along the optimal path with minimum probability of being found and the minimal consumed fuel. Traditional methods tend to find local best solutions due to the large search space. In this paper a Fitness-Scaling Chaotic Artificial Bee Colony (FSCABC) approach was proposed as a fast and robust approach for the task of path planning of UCAV. The FSCABC employed the fitness-scaling method and the chaotic theory. Experiments show that the FSCABC is more robust, uses less iteration, and costs less time than elite genetic algorithm with migration, particle swarm optimization, and chaotic artificial bee colony.