融合认知心理学理论的新型教与学优化算法及应用  

New teaching-learning-based optimization algorithm with cognitive psychology theory and its application

作　　者：何佩苑 刘勇[1] He Peiyuan;Liu Yong(Business School,University of Shanghai for Science&Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学管理学院,上海200093

出　　处：《计算机应用研究》2022年第3期785-789,796,共6页Application Research of Computers

基　　金：教育部人文社会科学研究青年基金资助项目(21YJC620087);上海市“科技创新行动计划”软科学研究重点项目(18692110500);上海市哲学社会科学规划课题(2019BGL014);上海市高原科学建设项目(第2期);上海理工大学科技发展资助项目(2020KJFZ040)。

摘　　要：针对教与学优化算法(teaching-learning-based optimization, TLBO)寻优精度低、易陷入局部最优的问题,提出了一种融合认知心理学理论的新型教与学优化算法(cognitive psychology teaching-learning-based optimization, CPTLBO)。在教阶段融入登门槛效应理论,对于学习有困难的学生设置阶段性学习目标,从而提高学生的整体水平;在学阶段加入老师引导机制,提高算法收敛速度;随后,加入自我调整阶段,学生根据心理控制源理论可被分为内控型和外控型,不同类型的学生对自身成绩采取不同的归因方式并采取相应措施。利用经典的基准测试函数对CPTLBO进行测验,结果表明改进算法在寻优精度和收敛速度方面具有优势。构建CPTLBO-ELM自来水供水量预测模型,采用CPTLBO算法优化极端学习机的输入权值和隐含层阈值参数,以提高模型的预测精度和泛化能力。仿真结果表明:用CPTLBO算法优化后的模型预测结果更准确。Aiming at the problem of low accuracy of TLBO and easy falling into local optima, the paper proposed a new TLBO algorithm integrating cognitive psychology theory. Incorporating the theory of foot in the door effect in the teaching stage, it could set the periodic learning goals for students with learning difficulties, so as to improve the overall level of students. Adding teacher guidance mechanism in the learning stage was good for increasing the speed of algorithm convergence. There was a self-adjustment stage after learning stage. According to psychological locus of control theory, it divided students into internal control type and external control type. Different types of students adopt different attribution methods for their own performance and took corresponding measures. Using the classic benchmark function to test CPTLBO, the results show that the improved algorithm has advantages in optimization accuracy and convergence speed. Constructing the CPTLBO-ELM tap water supply prediction model and using CPTLBO algorithm to optimize the input weights and hidden layer threshold parameters of the extreme learning machine, so as to improve the prediction accuracy and generalization ability of the model. The simulation results show that it is more accurate for the model which is optimized by CPTLBO algorithm.

关 键 词：教与学优化算法 认知心理学 登门槛效应 心理控制源理论 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]