一种基于反思机制的教与学优化算法
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摘要
针对教与学优化(teaching-learning-based optimization,TLBO)算法中存在的易陷入局部最优以及收敛速度较慢等问题,提出了基于反思机制的TLBO算法。为提高算法的全局搜索和局部收敛能力,在教学过程中利用教师反思行为来增强教师教学水平,进一步提高学生知识技能。同时学生实现自我反思,达到同步提高的目的。利用基准测试函数对算法进行性能测试,实验结果表明,改进后的TLBO算法具有更好的寻优性能。
This paper proposed an improved TLBO algorithm based on reflection mechanism for the problems such as easy to fall into local optimization and slow convergence in teaching-learning-based optimization( TLBO) algorithm. In order to increase the global search speed and local convergence accuracy of the algorithm,teacher carried out reflection behavior to enhance the teaching level in the teaching process,and improved the students' skills. At the same time,students realized the self-reflection and achieved simultaneous improvement. The experimental results show that the improved TLBO algorithm has better performance.
This paper proposed an improved TLBO algorithm based on reflection mechanism for the problems such as easy to fall into local optimization and slow convergence in teaching-learning-based optimization( TLBO) algorithm. In order to increase the global search speed and local convergence accuracy of the algorithm,teacher carried out reflection behavior to enhance the teaching level in the teaching process,and improved the students' skills. At the same time,students realized the self-reflection and achieved simultaneous improvement. The experimental results show that the improved TLBO algorithm has better performance.
引文
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[9]拓守恒,雍龙泉,黎延海,等.面向高维复杂多模态问题的教与学优化求解算法[J].计算机应用研究,2017,34(7):1939-1945.
[10]Qu Xinghua,Zhang Ran,Liu Bo,et al. An improved TLBO based memetic algorithm for aerodynamic shape optimization[J]. Engineering Applications of Artificial Intelligence,2017,57(1):1-15.
[11]Zhang Hongjun,Li Baozhu,Zhang Jun,et al. Parameter estimation of nonlinear chaotic system by improved TLBO strategy[J]. Soft Computing,2016,20(12):4965-4980.
[2] Li Ruiguo,Zhang Hongli,Fan Wenhui,et al. Hermite orthogonal basis neural network based on improved teaching-learning-based optimization algorithm for chaotic time series prediction[J]. Acta Physica Sinica,2015,64(20):200506.
[3] Yan Juan,Li Kang,Bai Erwei,et al. Time series wind power forecasting based on variant Gaussian process and TLBO[J]. Neurocomputing,2016,189(C):135-144.
[4] Selvam K,Kumar D M V,Siripuram R. Distributed generation planning using peer enhanced multi-objective teaching-learning based optimization in distribution networks[J]. Journal of the Institution of Engineers,2017,98(2):203-211.
[5] Sahu B K,Pati T K,Nayak J R,et al. A novel hybrid LUS-TLBO optimized fuzzy-PID controller for load frequency control of multisource power system[J]. International Journal of Electrical Power&Energy Systems,2016,74(1):58-69.
[6] Rao R,Patel V. Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems[J]. International Journal of Industrial Engineering Computations,2013,4(1):29-50.
[7]于坤杰,王昕,王振雷.基于反馈的精英教学优化算法[J].自动化学报,2014,40(9):1976-1983.
[8]毕晓君,王佳荟.基于混合学习策略的教与学优化算法[J].浙江大学学报:工学版,2017,51(5):1024-1031.
[9]拓守恒,雍龙泉,黎延海,等.面向高维复杂多模态问题的教与学优化求解算法[J].计算机应用研究,2017,34(7):1939-1945.
[10]Qu Xinghua,Zhang Ran,Liu Bo,et al. An improved TLBO based memetic algorithm for aerodynamic shape optimization[J]. Engineering Applications of Artificial Intelligence,2017,57(1):1-15.
[11]Zhang Hongjun,Li Baozhu,Zhang Jun,et al. Parameter estimation of nonlinear chaotic system by improved TLBO strategy[J]. Soft Computing,2016,20(12):4965-4980.