融合改进遗传算法的动态资源调控算法设计  

Design of dynamic resource regulation algorithm by integrating improved Genetic Algorithm

作　　者：张伟 杨华飞 杨文清 段淼臻 钱恒顺 ZHANG Wei;YANG Huafei;YANG Wenqing;DUAN Miaozhen;QIAN Hengshun(NARI Group Corporation,Nanjing 211100,China)

机构地区：[1]南瑞集团有限公司,江苏南京211100

出　　处：《电子设计工程》2025年第2期38-41,46,共5页Electronic Design Engineering

基　　金：南瑞集团有限公司科技项目(SGNR0000KJJS2206005)。

摘　　要：为了降低智慧电网硬件设备的购买和维护成本、提高各类资源的利用效率,文中提出一种基于改进遗传算法(GA)和蚁群算法(ACO)相融合的动态资源调控算法,并构建了相应的模型。通过在传统GA中引入时间-负载双适应度函数,提高了GA全局最优解的准确度。在传统ACO中利用时间-成本双函数来确定信息素,提高了ACO初期的寻优速度。采用动态融合策略将改进后的GA和ACO相结合,构建出ACO-GA动态资源调控算法。算例仿真结果表明,所提ACO-GA动态资源调控算法在任务数为400时,执行时间、不均衡值分别为120 ms和0.52。相比其他算法,提出算法的执行时间最低且不均衡值最为稳定,证明了ACO-GA动态资源调控算法用于资源调控的可行性。In order to reduce the purchase and maintenance costs of Smart grid hardware equipment and improve the utilization efficiency of various resources,this paper proposes a dynamic resource regulation algorithm based on the combination of improved Genetic Algorithm(GA)and Ant Colony Optimization(ACO),and constructs a corresponding model.By introducing a time load dual fitness function into the traditional GA,the accuracy of the global optimal solution of the GA has been improved.In the traditional ACO,the time cost double function is used to determine the Pheromone,which improves the initial optimization speed of the ACO.The dynamic fusion strategy is used to combine the improved GA and the improved ACO to build the ACO-GA dynamic resource regulation algorithm.The simulation results of the example show that the proposed ACO-GA dynamic resource regulation algorithm has an execution time of 120 ms and an imbalance value of 0.52 when the number of tasks is 400.Compared to other algorithms,the ACO-GA dynamic resource regulation algorithm has the lowest execution time and the most stable imbalance value,proving its feasibility for resource regulation.

关 键 词：资源调控 遗传算法 蚁群算法 动态融合 全局最优解 

分 类 号：TP393[自动化与计算机技术—计算机应用技术] TN925[自动化与计算机技术—计算机科学与技术]