摘要
针对现有卫星网络多资源、多任务约束下的资源分配,没有同时考虑任务完成时间和任务优先级导致的任务总体完成时间过长、任务优先级匹配度不高等问题,定义了任务序列优先级逆序数,建立了以任务总体完成时间最短和任务序列优先级逆序数最小为目标的约束模型,提出了一种自适应遗传算法并对模型进行求解.该算法利用精英保留的思想改进了采用轮盘赌策略的选择算子并且给出了一种能够自适应更新自身概率的变异、交叉算子,解决了标准遗传算法容易陷入局部最优的缺陷,避免了最优解的丢失.仿真实验验证表明,本文算法在任务总体完成时间方面降低了15.84%,在优先级逆序数方面降低了24.32%,有效解决了卫星网络多资源、多任务约束下的多目标分配问题.
For the resource allocation under the multi-resource and multi-task constraints of the existing satellite network,without considering the problem that task completion time and task priority lead to long task completion time and lowtask priority matching degree,the priority inverse number of task sequence is defined,the approximation is established aiming at the shortest total task completion time and the smallest priority inverse number of task sequence,and an adaptive genetic algorithm is proposed to solve the model.The algorithm improves the selection mechanism based on elite reservation and roulette strategy,and designs adaptive crossover and mutation operators.It solves the shortcoming that standard genetic algorithm is easy to fall into local optimum and avoids the loss of optimal solution.The simulation results showthat the proposed method reduces the overall task completion time by 15.84%and the priority inverse number by 24.32%.It effectively solves the Multi-objective Assignment Problem with multi-resource and multi-task constraints in satellite network.
作者
杨力
杨恒
魏德宾
潘成胜
YANG Li;YANG Heng;WEI De-bin;PAN Cheng-sheng(Communication and Networks Laboratory,Dalian University,Dalian 116622,China;Information Engineering Department,Dalian University,Dalian 116622,China;Automated Institute,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《小型微型计算机系统》
CSCD
北大核心
2020年第1期122-127,共6页
Journal of Chinese Computer Systems
基金
国家自然基金项目(61722105)资助
装备发展部预研领域基金项目(6140449050116JW61001)资助
辽宁省自然科学基金项目(20170540034)资助
关键词
卫星网络
资源分配
自适应遗传算法
任务序列优先级逆序数
任务完成时间
resource allocation
satellite network
adaptive genetic algorithm
priority inverse number of task sequence
task completion time
