信息筛选多任务优化自组织迁移算法  

Self-organized migrating algorithm for multi-task optimization with information filtering

作　　者：程美英 钱乾 倪志伟[4] 朱旭辉[4] CHENG Meiying;QIAN Qian;NI Zhiwei;ZHU Xuhui(School of Economics&Management,Huzhou University,Huzhou Zhejiang 313000,China;Research Center of Education Information Evaluation and Application of Zhejiang Province,Huzhou Zhejiang 313000,China;School of Teacher Education,Huzhou University,Huzhou Zhejiang 313000,China;School of Management,Hefei University of Technology,Hefei Anhui 230009,China)

机构地区：[1]湖州师范学院经济管理学院,浙江湖州313000 [2]浙江省教育信息化评价与应用研究中心,浙江湖州313000 [3]湖州师范学院教师教育学院,浙江湖州313000 [4]合肥工业大学管理学院,合肥230009

出　　处：《计算机应用》2021年第6期1748-1755,共8页journal of Computer Applications

基　　金：浙江省教育科学规划课题(2019SCG036);湖州市科技计划课题(2018YZ11);浙江省人力资源和社会保障科研项目(2019088)。

摘　　要：针对现有自组织迁移算法(SOMA)只能求解单个优化问题及其"隐并行性"未能被充分挖掘的缺陷,提出信息筛选多任务优化自组织迁移算法(SOMAMIF)实现同一时刻处理多个优化问题。首先,构造多任务统一搜索空间,并根据任务个数设置相应的子种群;然后,对各子种群当前最优适应值进行判断,当任务连续若干代停滞进化时则产生信息交互需求;接着,按概率从剩余子种群中筛选对自己有用的信息并过滤无用信息,从而在保证信息正向迁移同时实现种群结构的重新调整;最后对算法的时间复杂度和空间复杂度进行分析。实验结果表明,SOMAMIF在同时求解多个高维函数优化问题时均快速收敛至全局最优解0,而SOMAMIF与分形技术相结合同时提取不同户籍高校学生返乡关键制约因素时,其在两个数据集上得到的平均分类准确率与原始数据集的平均分类准确率相比分别提高了0.348 66个百分点和0.598 57个百分点。The Self-Organized Migrating Algorithm(SOMA)only can solve the single task,and the“implicit parallelism”of SOMA is not fully exploited.Aiming at the shortcomings,a new Self-Organized Migrating Algorithm for Multi-task optimization with Information Filtering(SOMAMIF)was proposed to solve multiple tasks concurrently.Firstly,the multi-task uniform search space was constructed,and the subpopulations were set according to the number of tasks.Secondly,the current optimal fitness of each subpopulation was judged,and the information transfer need was generated when the evolution of a task stagnated in a successive generations.Thirdly,the useful information was chosen from the remaining subpopulations and the useless information was filtered according to a probability,so as to ensure the positive transfer and readjust the population structure at the same time.Finally,the time complexity and space complexity of SOMAMIF were analyzed.Experimental results show that,SOMAMIF converges rapidly to the global optimal solution 0 when solving multiple high-dimensional function problems simultaneously;compared with those of the original datasets,the average classification accuracies obtained on two datasets by SOMAMIF combing with the fractal technology to extract the key home returning constraints from college students with different census register increase by 0.34866 percentage points and 0.59857 percentage points respectively.

关 键 词：多任务优化 自组织迁移算法 信息筛选 多任务高维函数优化 多任务离散优化问题 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]