A dynamic parameters genetic algorithm for collaborative strike task allocation of unmanned aerial vehicle clusters towards heterogeneous targets

Abstract

Collaborative strikes by unmanned aerial vehicle clusters (UAVCs) is becoming a key focus in the future air warfare, which can significantly enhance warfare effectiveness and reduce costs. To exactly describe the real battlefield scenarios, various heterogeneous strike-targets should be embedded. However, it will significantly increase the complexity of multi-constraint combinatorial optimization problem, thus the traditional genetic algorithm (GA) is difficult to solve efficiently due to its unchanged gene operator. In this paper, a dynamic parameters genetic algorithm has been proposed for UAVCs collaborative task allocation towards heterogeneous targets. Firstly, according to the differences of type, value, combat and defense, the heterogeneous strike-targets have been abstracted into strike target points and the UAVCs have been formulated into a set. Secondly, an innovative multiple unmanned aerial vehicles duplicate tasks orienteering problem (MUDTOP) model has been built to achieve multiple strikes on certain targets. Finally, the new triple-chromosome encoding and duplicate gene segments have been designed, and a novel genetic algorithm called DPGA-TEDG has been presented through dynamic gene operator. Experimental comparison results across various battlefield scales demonstrate that the outcomes of the proposed DPGA-TEDG algorithm not only meet practical requirements, but also outperform that of the other three algorithms in both optimality and robustness. Especially, in the battlefield scale environment of 180 km* 180 km, the average objective value of DPGA-TEDG is better than that of traditional genetic algorithm (GA-TEDG), simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO) about 2.71 %, 6.58 % and 20.49 %, respectively.