基于支配强度的NSGA Ⅱ改进算法在研究生招生面试分组中的应用  

Application of Improved NSGA Ⅱ Algorithm Based on Dominance Strength in Graduate Enrollment Interview Grouping

作　　者：彭光彬 何静媛[2] PENG Guang-bin;HE Jing-yuan(CollegeofInformation Engineering,Chongqing Vocational and Technical University of Mechatronics,Chongqing 402760,China;College of Computer Science,Chongqing University,Chongqing 400044,China)

机构地区：[1]重庆机电职业技术大学信息工程学院,重庆402760 [2]重庆大学计算机学院,重庆400044

出　　处：《运筹与管理》2022年第10期127-132,共6页Operations Research and Management Science

基　　金：教育部新工科研究与实践项目(E-JSJRJ20201335);重庆市高等教育教学改革研究项目(191003);重庆市教育委员会科学技术研究资助项目(KJQN201903701)。

摘　　要：针对研究生招生面试分组这一NP难问题,提出了一种以分组遗传算法(GGA)和基于支配强度的改进NSGAⅡ算法为基础的混合多目标分组遗传算法。通过基于矩阵编码的多交叉/多变异算子、次精英化的初始化种群策略以及改进的帕累托支配关系,解决了经典NSGAⅡ算法在该问题中的收敛速度慢、易陷入局部最优的问题。仿真实验结果表明,该方法只需进行较少代数(不超过100代)的进化,即可获得最优解集,满足了快速分组的用户偏好。In order to solve the NP hard problem of graduate enrollment interview grouping,a hybrid multi-objective grouping genetic algorithm is proposed.It is integrated with grouping genetic algorithm(GGA) and improved NSGA Ⅱ algorithm based on dominance strength.By using multi-crossover/multi-mutation operator based on matrix coding,sub elitist initialization population strategy and improved Pareto dominance relation,the problems of slow convergence speed and easily falling into local optimum of classical NSGA Ⅱ algorithm in this problem are solved.The simulation results show that the optimal solution sets can be obtained in only a few times of evolution(no more than 100 generations),which meets the user’s preferences of fast grouping.

关 键 词：面试分组 多目标优化 NSGAⅡ 分组遗传算法 

分 类 号：O221.6[理学—运筹学与控制论] G643[理学—数学]