改进的动态自适应学习教与学优化算法

doi:10.11772/j.issn.1001-9081.2016.03.708

计算机应用 ›› 2016, Vol. 36 ›› Issue (3): 708-712.DOI: 10.11772/j.issn.1001-9081.2016.03.708

改进的动态自适应学习教与学优化算法

王培崇^1,2

1. 石家庄经济学院信息工程学院, 石家庄 050031;
2. 中国矿业大学机电与信息工程学院, 北京 100083

收稿日期:2015-08-10 修回日期:2015-09-23 出版日期:2016-03-10 发布日期:2016-03-17
通讯作者: 王培崇
作者简介:王培崇(1972-),男,河北辛集人,副教授,博士,CCF会员,主要研究方向:机器学习、模式识别。
基金资助:
河北省科技支撑项目(13214711,15210710),石家庄经济学院预研项目(syy201310)。

Improved dynamic self-adaptive teaching-learning-based optimization algorithm

WANG Peichong^1,2

1. School of Information Engineering, Shijiazhuang University of Economics, Shijiazhuang Hebei 050031, China;
2. School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing 100083, China

Received:2015-08-10 Revised:2015-09-23 Online:2016-03-10 Published:2016-03-17
Supported by:
This work is partially supported by the Science and Technique Support Fund of Hebei Province (13214711, 15210710), the Pre-research Fund of Shijiazhuang University of Economics (syy201310).

摘要/Abstract

摘要： 为了克服教与学优化(TLBO)算法在求解函数优化问题时容易陷入局部最优、后期收敛速度慢、解精度较低等的弱点,提出了一种动态自适应学习和动态随机搜索机制的改进教与学优化算法。首先,在教师的教学过程中,引入一个线性变化的动态学习因子,来调整在迭代寻优过程中学生自身知识对本次学习的贡献价值。其次,为了提高算法的解精度,教师个体将执行动态随机搜索算法以加强对种群内的最优个体所在解空间的勘探。在14个标准测试函数上进行仿真实验,将所提算法与其他相关算法进行对比,结果表明所提算法不仅在求解精度,而且其收敛速度均优于标准TLBO算法,适合求解较高维的函数优化问题。

关键词: 教与学优化, 函数优化, 动态自适应学习, 种群多样性, 动态随机搜索

Abstract: The Teaching-Learning-Based Optimization (TLBO) algorithm in function optimization problems has some weakness, such as falling into the local optimum value,converging slowly in the later period and acquiring solution inaccurately. To overcome these shortcomings, an improved TLBO algorithm with dynamic self-adaptive learning and dynamic random searching was proposed. Firstly, a linear increment dynamic variation coefficient was introduced into the teaching process to adjust the value of knowledge to individual learning in the iterative optimization process. Secondly, in order to improve the precision of solution, teacher individual executed dynamic random searching to exploit the solution space around the best individual. The experiments were conducted on 14 classic testing functions, and the experimental results show that the proposed algorithm is much better than standard TLBO at not only the accuracy of solutions but also for the convergence speed. It is suitable to solve the high-dimensional function optimization problem.

Key words: Teaching-Learning-Based Optimization (TLBO), function optimization, dynamic self-adaptive learning, diversity of population, dynamic random search

中图分类号:

TP301.6

王培崇. 改进的动态自适应学习教与学优化算法[J]. 计算机应用, 2016, 36(3): 708-712.

WANG Peichong. Improved dynamic self-adaptive teaching-learning-based optimization algorithm[J]. Journal of Computer Applications, 2016, 36(3): 708-712.

参考文献

[1] 王培崇.群体智能算法及其应用 [M].北京:电子工业出版社,2015:3-5.(WANG P C. Swarm intelligence algorithms and their applications [M]. Beijing: Publishing House of Electronics Industry, 2015:3-5.
[2] 郁书好,苏守宝.混沌萤火虫优化算法的研究及应用[J].计算机科学与探索,2014,8(3):352-358.(YU S H, SU S B. Research and application of chaotic glowworm swarm optimization algorithm [J]. Journal of Frontiers of Computer Science and Technology, 2014,8(3):352-358.)
[3] HE S, WU Q H, SAUNDERS J R. Group search optimizer: an optimization algorithm inspired by animal searching behavior [J]. IEEE Transactions on Evolutionary Computation, 2009,13(5):973-990.
[4] KARABOGA D, AKAY B. A comparative study of artificial bee colony algorithm [J]. Applied Mathematics and Computation, 2009,214(1):108-132.
[5] RAO R V, SAVSANI V J, VAKHARIA D P. Teaching-learning based optimization: a novel method for constrained mechanical design optimization problems [J]. Computer Aided Design, 2011,43(3):303-315.
[6] RAO R V, SAVSANI V J, VAKHARIA D P. Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems [J]. Information Sciences, 2012,183(1):1-15.
[7] NIKNAM T, AZIZIPANAH-ABARGHOOEE R, NARIMANI M R. A new multi objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems[J]. Engineering Applications of Artificial Intelligence, 2012,25(8):1577-1588.
[8] SATAPATHY S C, NAIK A. Data clustering based on teaching-learning-based optimization [M]//PANIGRAHI B K, SUGANTHAN P N, DAS S, et al. Swarm, Evolutionary, and Memetic Computing, LNCS 7077. Berlin: Springer, 2011:148-156.
[9] 拓守恒,雍龙泉.一种用于PID控制的教与学优化算法[J].智能系统学报,2014,9(6):740-746.(TUO S H, YONG L Q. A modified teaching-learning-based optimization algorithm for parameter tuning of a PID controller [J]. CCAI Transactions on Intelligent Systems, 2014,9(6):740-746.)
[10] RAO R V, PATEL V. An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems [J]. International Journal of Industrial Engineering Computations, 2012,3(4):535-560.
[11] RAJASEKHAR A, RANI R, RAMYA K, et al. Elitist teaching-learning opposition based algorithm for global optimization [C]//Proceedings of the 2012 IEEE International Conference on Systems, Man, and Cybernetics. Piscataway, NJ: IEEE, 2012:1124-1129.
[12] RAO R V, PATEL V. An improved teaching- learning- based optimization algorithm for solving unconstrained optimization problems [J]. Scientia Iranica, 2013,20(3):710-720.
[13] 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.(YU K J, WANG X, WANG Z L. Elitist teaching-learning-based optimization algorithm based on feedback [J]. Acta Automatica Sinica, 2014,40(9):1976-1983.)
[14] 高立群,欧阳海滨,孔祥勇,等.带有交叉操作的教-学优化算法[J].东北大学学报 (自然科学版),2014,35(3):323-327.(GAO L Q, OUYANG H B, KONG X Y, et al. Teaching-learning based optimization algorithm with crossover operation [J]. Journal of Northeastern University (Natural Science), 2014,35(3):323-327.)
[15] 梁昔明,陈富,龙文.基于动态随机搜索和佳点集构造的改进粒子群优化算法[J].计算机应用,2011,31(10):2796-2799.(LIANG X M, CHEN F, LONG W. Improved PSO based on dynamic random search technique and good-point set [J]. Journal of Computer Applications, 2011,31(10):2796-2799.)
[16] 朱旭辉,倪志伟,程美英.变步长自适应的改进人工鱼群算法[J].计算机科学,2015,42(2):210-216.(ZHU X H, NI Z W, CHENG M Y. Self-adaptive improved artificial fish swarm algorithm with changing step [J]. Computer Science, 2015,42(2):210-216.)

改进的动态自适应学习教与学优化算法

Improved dynamic self-adaptive teaching-learning-based optimization algorithm

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	程美英, 钱乾, 倪志伟, 朱旭辉. 信息筛选多任务优化自组织迁移算法[J]. 计算机应用, 2021, 41(6): 1748-1755.
[2]	李二超, 杨蓉蓉. 基于自适应反向学习的多目标分布估计算法[J]. 计算机应用, 2021, 41(1): 15-21.
[3]	李丽荣, 杨坤, 王培崇. 融合头脑风暴思想的教与学优化算法[J]. 计算机应用, 2020, 40(9): 2677-2682.
[4]	沈莹, 黄樟灿, 谈庆, 刘宁. 基于动态压力控制算子的磷虾群算法[J]. 计算机应用, 2019, 39(3): 663-667.
[5]	袁小平, 蒋硕. 基于分层自主学习的改进粒子群优化算法[J]. 计算机应用, 2019, 39(1): 148-153.
[6]	王永贵, 胡彩云, 李鑫. 基于局部远亲差分增强的扰动粒子群优化算法[J]. 计算机应用, 2018, 38(5): 1239-1244.
[7]	周海鹏, 高芹, 蒋丰千, 余大为, 乔焰, 李旸. 自适应混沌量子粒子群算法及其在WSN覆盖优化中的应用[J]. 计算机应用, 2018, 38(4): 1064-1071.
[8]	童楠, 符强, 钟才明. 基于自主学习行为的教与学优化算法[J]. 计算机应用, 2018, 38(2): 443-447.
[9]	张强, 邹德旋, 耿娜, 沈鑫. 基于多变异策略的自适应差分进化算法[J]. 计算机应用, 2018, 38(10): 2812-2821.
[10]	梁冰, 徐华. 基于改进人工蜂群的核模糊聚类算法[J]. 计算机应用, 2017, 37(9): 2600-2604.
[11]	马艺元, 宋卫平, 宁爱平, 牛海帆. 混沌云模型多目标布谷鸟搜索算法[J]. 计算机应用, 2017, 37(4): 1088-1092.
[12]	张斌, 李延晖, 郭昊. 基于反向学习的跨种群差分进化算法[J]. 计算机应用, 2017, 37(4): 1093-1099.
[13]	陈闯, Ryad Chellali, 邢尹. 采用动态权重和概率扰动策略改进的灰狼优化算法[J]. 计算机应用, 2017, 37(12): 3493-3497.
[14]	牛海帆, 宋卫平, 宁爱平, 马艺元. 混沌布谷鸟搜索算法在谐波估计中的应用[J]. 计算机应用, 2017, 37(1): 239-243.
[15]	谢红侠, 马晓伟, 陈晓晓, 邢强. 基于多种群的改进粒子群算法多模态优化[J]. 计算机应用, 2016, 36(9): 2516-2520.