战场环境下遗传黏菌算法的多机协同任务分配

Multi-UAVs collaborative task allocation based on genetic slime mould algorithm in battlefield environment

针对已知战场环境下的多无人机协同任务分配问题,提出基于融合遗传黏菌算法的任务分配方法.综合单机约束、机群总体收益和损耗以及任务需求等条件,构建多机协同任务分配目标函数.针对遗传算法易陷入局部最优、黏菌算法收敛慢的问题,改进遗传迭代和黏菌探索行为.将离散黏菌算法引入遗传算法,增强融合算法的搜索能力.在种群迭代中加入干扰操作,提高求解精度.在已知环境下进行分配试验和路径演示,并与其他算法进行对比.结果表明,利用所提出的融合算法,能够获得目标函数值更高的任务分配方案.

A task allocation method based on the fusion genetic slime mould algorithm(FGSMA)was proposed aiming at the problem of collaborative multi-drone task allocation in a known battlefield environment.The objective function for multi-drone collaborative task allocation was constructed by considering the constraints of individual drones,the overall benefit and loss of the drone group,as well as the task requirements.The genetic iteration and slime mould exploration behaviors were improved in order to address the issues of genetic algorithms’tendency to fall into local optima and the slow convergence of slime mould algorithms.The discrete slime mould algorithm was introduced into the genetic algorithm to enhance the search capability of the fused algorithm.A disturbance operation was added to the population iteration in order to improve the solution accuracy.Allocation experiments and path demonstrations were conducted in a known environment,and comparisons with other algorithms were conducted.Results show that the proposed fusion algorithm can obtain a task allocation scheme with a higher objective function value.