Improved discrete mapping differential evolution for multi-unmanned aerial vehicles cooperative multi-targets assignment under unified model

摘要

The cooperative multi-targets assignment for multiple unmanned aerial vehicles (UAV) is a complex combinatorial optimization problem. Multi-UAVs cooperation increases the scale of problems which cause a noticeable increase in task planning time. Moreover, it is difficult to build a unified assignment model because different tasks often require different numbers of UAVs and targets. Besides, the cooperative constraints of multi-UAVs in a three-dimensional environments are more complex than that in a two-dimensional environments, which makes it difficult to obtain an optimal solution. To solve these problems, we present a unified gene coding strategy to handle various models in a consistent framework. Then, a cooperative target assignment algorithm in a three-dimensional environments based on discrete mapping differential evolution is given. First, we use flight path cost to indicate the assignment relationship between the UAV and the target, which turns the optimization problem from discrete space to continuous space, and so the solving process can be simplified. Secondly, in order to obtain reasonable offspring for differential evolution, we map the solution back to the assignment relationship space according to inverse mapping rules. Finally, to avoid falling into a local optimal, a balance between exploration and exploitation is achieved by combining the dynamic crossover rate with the hybrid evolution strategy. The simulation results show that the proposed discrete mapping differential evolution algorithm with the unified gene coding strategy not only effectively solves the cooperative multi-targets assignment problem, but also improves the accuracy of the multi-targets assignment. It is also suitable for solving the large scale problem of assignment.