[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(05):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(05):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(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年05期
页码:
818-828
栏目:
出版日期:
2018-09-05

文章信息/Info

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12 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 Qinqin12 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 optimizationweight learningheuristic algorithmchaotic searchglobal optimizationevolutionary computationnon-cooperative gameNash 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.

备注/Memo

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