CAJ | 학술논문

针对以往考虑时间窗约束的多无人机协同任务分配问题模型不能反映在有效时间窗内，任务执行时间对任务收益的影响及求解算法效率较低的问题。建立了将任务收益和任务执行时间直接联系起来的任务分配模型和可行解到粒子整数编码方式的映射，设计了混合离散粒子群－郭涛算法的组合优化问题求解策略。借助粒子群算法利用粒子自身信息和种群有用信息指导种群进化的本质特点，优化郭涛算法的适应性序列倒置操作；设计了可变的学习选择概率来选择个体的学习粒子，改进了序列倒置算子。仿真实验验证了该方法处理复杂任务分配问题的有效性。
침대이왕고필시간창약속적다무인궤협동임무분배문제모형불능반영재유효시간창내，임무집행시간대임무수익적영향급구해산법효솔교저적문제。건립료장임무수익화임무집행시간직접련계기래적임무분배모형화가행해도입자정수편마방식적영사，설계료혼합리산입자군－곽도산법적조합우화문제구해책략。차조입자군산법이용입자자신신식화충군유용신식지도충군진화적본질특점，우화곽도산법적괄응성서렬도치조작；설계료가변적학습선택개솔래선택개체적학습입자，개진료서렬도치산자。방진실험험증료해방법처리복잡임무분배문제적유효성。
A general mathematics model for cooperative task allocation of multi-UAVs with time windows constrains was proposed which incorporating task gains and execution time directly,and simplifing the model formulation and algorithm designing.By defining a suitable particle structure,an algorithm based on the principles of discrete particle swarm optimization and Guo Tao algorithm was designed.The Inver-over Operator was directed by the swarm,the local and global optimal.Variable learning selection probability is introduced into the algorithm to select the learning particles,and the Inver-Over operator was modified.Simulation verifies the proposed task planning methodology for complex missions.