UCAV path planning based on Ant Colony Optimization and satisficing decision algorithm

Abstract

Path planning of uninhabited combat air vehicle (UCAV) is a complicated global optimum problem. Ant colony optimization (ACO) algorithm was originally presented under the inspiration during collective behavior study results on real ant system, and it has strong robustness and easy to combine with other methods in optimization. In this paper, we propose a hybrid ACO with satisficing decision algorithm for solving the UCAV path planning in complicated combat field environments. When ant chooses the next node from the current candidate path nodes, the acceptance function and rejection function in satisficing decision are calculated. In this way, the efficiency of global optimization can be greatly improved. The detailed realization procedure for this hybrid approach is also presented. Series experimental comparison results show the proposed hybrid method is more effective and feasible in the UCAV path planning than the basic ACO model.