融合局部搜索与Pareto支配的多目标任务调度模型  

Multi-objective task scheduling model incorporating local search and Pareto domination

作　　者：韩迪雅 张凤荔[1] 尹嘉奇 王瑞锦[1] 韩英军 Han Diya;Zhang Fengli;Yin Jiaqi;Wang Ruijin;Han Yingjun(School of Information&Software Engineering,University of Electronic Science&Technology of China,Chengdu 610054,China;Sichuan Environmental Information Center,Chengdu 610041,China)

机构地区：[1]电子科技大学信息与软件工程学院,成都610054 [2]四川省环境信息中心,成都610041

出　　处：《计算机应用研究》2023年第8期2298-2303,共6页Application Research of Computers

基　　金：国家自然科学基金资助项目(61133016);四川省科技计划资助项目(2020YFG0475,2020YFQ0018);四川省重大科技专项资助项目(22ZDZX0046)。

摘　　要：为了解决复杂任务群调度过程中资源利用不均、任务完成时间较长等问题,以最小化资源负载均方差和最小化任务群完成时间为目标构建复杂任务群资源调度模型,提出一种融合局部搜索和Pareto支配的多目标优化算法BRLSN(multi-objective optimization based on boundary range local search and NSGA-Ⅱ,BRLSN)。该算法采用有效的编码方式与交叉变异算子进行迭代寻优,并利用基于边界区域局部搜索的精英保留策略扩大算法搜索范围,保存种群优良个体。实验结果表明,BRLSN相较于其他多目标算法在收敛性和多样性上有显著的提升,同时算法收敛速度更快,种群质量更高,明显优化了最终目标函数的结果值。In order to solve the problems of uneven resource utilization and long task completion time in complex task group scheduling,this paper constructed a complex task group resource scheduling model to minimize the mean square error of resource load and the task group completion time,and proposed a multi-objective optimization algorithm based on boundary range local search and NSGA-Ⅱ,called BRLSN.The algorithm used an effective coding method and cross mutation operator for iterative optimization,and constructed an elite retention strategy based on local search in boundary region to expand the search scope of the algorithm and preserved good individuals in the population.Experimental results show that compared with other multi-objective algorithms,the convergence and diversity of BRLSN are significantly improved.At the same time,the algorithm convergence speed is faster,the population quality is higher,and the result value of the final objective function is obviously optimized.

关 键 词：多目标优化 局部搜索 智能算法 任务调度 PARETO支配 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]