[1]柳缔西子,范勤勤,胡志华.基于混沌搜索和权重学习的教与学优化算法及其应用[J].智能系统学报,2018,13(5):818-828.[doi:10.11992/tis.201705017]
　LIU Dixizi,FAN Qinqin,HU Zhihua.Teaching-learning-based optimization algorithm based on chaotic search and weighted learning and its application[J].CAAI Transactions on Intelligent Systems,2018,13(5):818-828.[doi:10.11992/tis.201705017]
点击复制

基于混沌搜索和权重学习的教与学优化算法及其应用

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 13 期数: 2018年第5期 页码: 818-828 栏目: 学术论文—机器学习 出版日期: 2018-09-05
Title:
Teaching-learning-based optimization algorithm based on chaotic search and weighted learning and its application
作者:
柳缔西子1, 范勤勤1,2, 胡志华1
1. 上海海事大学 物流研究中心, 上海 201306;
2. 华东理工大学 化工过程先进控制和优化技术教育部重点实验室, 上海 200237
Author(s):
LIU Dixizi1, FAN Qinqin1,2, HU Zhihua1
1. Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China;
2. MOE Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai 200237, China
关键词:
教与学优化; 权重学习; 启发式算法; 混沌搜索; 全局优化; 进化计算; 非合作博弈; 纳什均衡
Keywords:
teaching-learning-based optimization; weight learning; heuristic algorithm; chaotic search; global optimization; evolutionary computation; non-cooperative game; Nash equilibrium
分类号:
TP301.6
DOI:
10.11992/tis.201705017
摘要:
针对教与学优化算法容易陷入早熟收敛的问题，本研究提出了一种基于混沌搜索和权重学习的教与学优化（teaching-learning-based optimization algorithm based on chaotic search and weighted learning，TLBO-CSWL）算法。在TLBO-CSWL算法的教学阶段，不仅利用权重学习得到的个体来指引种群的进化，而且还使用正态分布随机数来替代原有的均匀随机数。另外，TLBO-CSWL还使用Logistics混沌搜索策略来提高其全局搜索能力。仿真结果表明，TLBO-CSWL的整体优化性能要好于其他所比较的算法。最后，将TLBO-CSWL用于求解非合作博弈纳什均衡问题，获得满意的结果。
Abstract:
To avoid premature convergence, a teaching-learning-based optimization algorithm based on chaotic search and weighted learning (TLBO-CSWL) is introduced in this study. In the teaching phase, TLBO-CSWL does not only use the individuals obtained by weight learning to guide the population evolution, it also utilizes a normal random number to replace the original uniform random number. In addition, TLBO-CSWL uses a logistics chaotic search strategy to improve its global search ability. Simulation results showed that TLBO-CSWL outperformed other compared algorithms in terms of overall performance. Finally, the proposed algorithm was employed to solve two Nash equilibrium problems of non-cooperative game, and satisfactory results were obtained.
参考文献/References:
[1] NASH J. Non-cooperative games[J]. Annals of mathematics, 1951, 54(2):286-295.
[2] LEMKE C E, HOWSON J T JR. Equilibrium points of bimatrix games[J]. Journal of the society for industrial and applied mathematics, 1964, 12(2):413-423.
[3] GOVINDAN S, WILSON R. A global Newton method to compute Nash equilibria[J]. Journal of economic theory, 2003, 110(1):65-86.
[4] HERINGS P J J, PEETERS R. Homotopy methods to compute equilibria in game theory[J]. Economic theory, 2010, 42(1):119-156.
[5] 陈士俊, 孙永广, 吴宗鑫. 一种求解NASH均衡解的遗传算法[J]. 系统工程, 2001, 19(5):67-70 CHEN Sijun, SUN Yongguang, WU Zongxin. A genetic algorithm to acquire the nash equilibrium[J]. Systems engineering, 2001, 19(5):67-70
[6] 邱中华, 高洁, 朱跃星. 应用免疫算法求解博弈问题[J]. 系统工程学报, 2006, 21(4):398-404 QIU Zhonghua, GAO Jie, ZHU Yuexing. Applying immune algorithm to solving game problem[J]. Journal of systems engineering, 2006, 21(4):398-404
[7] 贾文生, 向淑文, 杨剑锋, 等. 基于免疫粒子群算法的非合作博弈Nash均衡问题求解[J]. 计算机应用研究, 2012, 29(1):28-31 JIA Wensheng, YUAN Shuwen, YANG Jianfeng, et al. Solving nash equilibrium for N-persons’ non-cooperative game based on immune particle swarm algorithm[J]. Application research of computers, 2012, 29(1):28-31
[8] 王志勇, 韩旭, 许维胜, 等. 基于改进蚁群算法的纳什均衡求解[J]. 计算机工程, 2010, 36(14):166-168, 171 WANG Zhiyong, HAN Xu, XU Weisheng, et al. Nash equilibrium solution based on improved ant colony algorithm[J]. Computer engineering, 2010, 36(14):166-168, 171
[9] 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.
[10] 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.
[11] 拓守恒, 邓方安, 雍龙泉. 改进教与学优化算法的LQR控制器优化设计[J]. 智能系统学报, 2014, 9(5):602-607 TUO Shouheng, DENG Fang’an, YONG Longquan. Optimal design of a linear quadratic regulator (LQR) controller based on the modified teaching-learning-based optimization algorithm[J]. CAAI transactions on intelligent systems, 2014, 9(5):602-607
[12] RAO R V, PATEL V. Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm[J]. Applied mathematical modelling, 2013, 37(3):1147-1162.
[13] WANG Lei, ZOU Feng, HEI Xinhong, et al. An improved teaching-learning-based optimization with neighborhood search for applications of ANN[J]. Neurocomputing, 2014, 143:231-247.
[14] 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.
[15] YU Kunjie, WANG Xin, WANG Zhenlei. An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems[J]. Journal of intelligent manufacturing, 2016, 27(4):831-843.
[16] ZOU Feng, WANG Lei, HEI Xinhong, et al. Teaching- learning-based optimization with dynamic group strategy for global optimization[J]. Information sciences, 2014, 273:112-131.
[17] CHEN Debao, LU Renquan, ZOU Feng, et al. Teaching-learning-based optimization with variable-population scheme and its application for ANN and global optimization[J]. Neurocomputing, 2016, 173:1096-1111.
[18] WU Zongsheng, FU Weiping, XUE Ru. Nonlinear inertia weighted teaching-learning-based optimization for solving global optimization problem[J]. Computational Intelligence and Neuroscience, 2015, 2015:1-15.
[19] SHAHBEIG S, HELFROUSH M S, RAHIDEH A. A fuzzy multi-objective hybrid TLBO-PSO approach to select the associated genes with breast cancer[J]. Signal processing, 2017, 131:58-65.
[20] 刘军梅. 新型混沌粒子群混合优化算法[J]. 软件导刊, 2017, 16(2):59-62 LIU Junmei. New chaotic particle swarm hybrid optimization algorithm[J]. Software guide, 2017, 16(2):59-62
[21] 唐娜, 张公让, 李磊. 自适应混沌搜索的双粒子群优化算法[J]. 计算机工程与设计, 2016, 37(9):2421-2428 TANG Na, ZHANG Gongrang, LI Lei. Double particle swarm optimization algorithm of self-adaption and chaos search[J]. Computer engineering and design, 2016, 37(9):2421-2428
[22] BREST J, GREINER S, BOSKOVIC B, et al. Self-adapting control parameters in differential evolution:a comparative study on numerical benchmark problems[J]. IEEE transactions on evolutionary computation, 2006, 10(6):646-657.
[23] QIN A K, HUANG V L, SUGANTHAN P N. Differential evolution algorithm with strategy adaptation for global numerical optimization[J]. IEEE transactions on evolutionary computation, 2009, 13(2):398-417.
[24] MENDES R, KENNEDY J, NEVES J. The fully informed particle swarm:simpler, maybe better[J]. IEEE transactions on evolutionary computation, 2004, 8(3):204-210.
[25] LIANG J J, QIN A K, SUGANTHAN P N, et al. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions[J]. IEEE transactions on evolutionary computation, 2006, 10(3):281-295.
[26] FRIEDMAN M. The use of ranks to avoid the assumption of normality implicit in the analysis of variance[J]. Journal of the American statistical association, 1937, 34(200):675-701.
[27] DUNN O J. Multiple comparisons among means[J]. Journal of the American statistical association, 1961, 56(293):52-64.
[28] GARCíA S, MOLINA D, LOZANO M, et al. A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour:a case study on the CEC’2005 Special Session on Real Parameter Optimization[J]. Journal of heuristics, 2009, 15(6):617-644.
[29] WOLPERT D H, MACREADY W G. No free lunch theorems for optimization[J]. IEEE transactions on evolutionary computation, 1997, 1(1):67-82.
[30] WEIBULL J W. Evolutionary game theory[M]. Cambridge, MA, USA:MIT Press, 1995.
[31] GIBBONS R. A primer in game theory[M]. New York, USA:Harvester Wheatsheaf, 1992.
[32] WILCOXON F. Individual comparisons by ranking methods[J]. Biometrics bulletin, 1945, 1(6):80-83.
相似文献/References:
[1]黎延海,雍龙泉,拓守恒.随机交叉-自学策略改进的教与学优化算法[J].智能系统学报,2021,16(2):313.[doi:10.11992/tis.201910045]
　LI Yanhai,YONG Longquan,TUO Shouheng.Teaching-learning-based optimization algorithm based on random crossover-self-learning strategy[J].CAAI Transactions on Intelligent Systems,2021,16():313.[doi:10.11992/tis.201910045]

备注/Memo

收稿日期:2017-05-15。
基金项目:国家自然科学基金项目（611603244）；中央高校基本科研业务费重点科研基地创新基金项目（222201717006）；上海海事大学研究生创新基金资助项目（2017YCX020）.
作者简介:柳缔西子,女,1995年生,硕士研究生,主要研究方向为教与学优化算法、物流与供应链管理;范勤勤,男,1986年生,讲师,主要研究方向为多目标优化、机器学习、进化计算。发表学术论文20余篇;胡志华,男,1977年生,教授,博士生导师,主要研究方向为物流与港航运作优化、大数据系统与管理、计算智能与计算实验。发表学术论文百余篇,被SCI、EI检索30余篇。
通讯作者:范勤勤.E-mail:forever123fan@163.com.

更新日期/Last Update: 2018-10-25
Copyright © 《 智能系统学报》 编辑部
地址：（150001）黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话：0451- 82534001、82518134　邮箱：tis@vip.sina.com