Unmanned Combat Aerial Vehicle Path Planning by Brain Storm Optimization Algorithm

The use of the unmanned aerial vehicles is rapidly growing in ever wider range of applications where military use is among the oldest ones. One of the fundamental problems in the unmanned combat aerial vehicles control is the path planning problem that refers to establish the optimal route from the start position to the target, where optimality can be defined in numerous ways. Path planning represents a multi-objective constrained hard optimization problem. In this paper, we adjusted a recent swarm intelligence brain storm optimization algorithm for finding the unmanned combat aerial vehicle optimal path considering fuel consumption and safety degree. The proposed method was tested and compared to eleven different methods from literature. Based on the simulation results, it can be concluded that our proposed approach is robust, exhibits better performance in almost all cases and has potential for further improvements.