An efficient multi-objective ant colony optimization for task allocation of heterogeneous unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have become powerful tools in modern military combat. How to properly allocate the tasks of heterogeneous UAVs in a combat is a fundamental and challenging problem. In this paper, we formulate the cooperative task allocation of heterogeneous UAVs as a constrained multi-objective optimization problem. To efficiently resolve the formulated problem, we further propose a multi-objective ant colony optimization (MOACO) algorithm with a new pheromone updating mechanism and four newly defined heuristic information. Simulation results on test cases with different scales and characteristics have shown that the proposed methods can perform better than several recently published algorithms, in terms of convergence speed, solution quality and solution diversity.