基于反馈的精英教学优化算法

于坤杰; 王昕; 王振雷

doi:10.3724/SP.J.1004.2014.01976

基于反馈的精英教学优化算法

doi: 10.3724/SP.J.1004.2014.01976

1.
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;
2.
上海交通大学电工与电子技术中心上海 200240

基金项目:

国家重点基础研究发展计划（973计划）（2012CB720500），国家自然科学基金（61333010，21276078，21206037），中央高校基本科研业务费专项资金（863计划）（2013AA0400701），上海市科技攻关（12dz1125100），十二五国家科技支撑计划（2012BAF05B00），上海市重点学科建设项目（B504），上海市自然科学基金（14ZR1421800），流程工业综合自动化国家重点实验室开放课题基金资助项目（PAL-N201404）资助

详细信息

作者简介:
于坤杰华东理工大学信息科学与工程学院博士研究生.主要研究方向为智能优化算法.E-mail：yukunjie1990@gmail.com

通讯作者:
王振雷华东理工大学教授.主要研究方向为智能控制，复杂系统的建模及特征分析，故障诊断和智能优化算法.本文通信作者.E-mail：wangzhenl@ecust.edu.cn

计量
- 文章访问数: 3038
- HTML全文浏览量: 209
- PDF下载量: 1441
- 被引次数: 0
出版历程
- 收稿日期: 2013-07-08
- 修回日期: 2014-02-26
- 刊出日期: 2014-09-20

Elitist Teaching-learning-based Optimization Algorithm Based on Feedback

1.
Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai 200237;
2.
Center of Electrical and Electronic Technology, Shanghai Jiao Tong University, Shanghai 200240

Funds:

Supported by National Basic Research Program of China (973 Program)(2012CB720500), National Natural Science Foundation of China (61333010, 21276078, 21206037), The Central University Basic Scientific Research Business Expenses Special Funds (863 Program)(2013AA0400701), Shanghai Science and Technology Research Projects (12dz1125100), National Science and Technology Support Project during the 12th Five-Year Plan Period (2012BAF05B00), Shanghai Leading Academic Discipline Project (B504), Shanghai Natural Science Foundation (14ZR1421800), the State Key Laboratory of Synthetical Automation for Process Industries (PAL-N201404)

摘要

摘要: 精英教学优化算法（Elitist teaching-learning-based optimization，ETLBO）是一种基于实际班级教学过程的新型优化算法. 本文针对ETLBO算法寻优精度低、稳定性差的问题，提出了反馈精英教学优化算法（Feedback ETLBO）. 在ETLBO算法的基础上，通过在学生阶段之后加入反馈阶段，增加了学生的学习方式，保持学生的多样性特性，提高算法的全局搜索能力. 同时，反馈阶段是选举成绩较差的学生与教师交流，使成绩较差的学生快速向教师靠拢，使算法进行局部精细搜索，提高算法的寻优精度. 对6个无约束及5个约束标准函数的测试结果表明，FETLBO算法与其他算法相比在寻优精度和稳定性上更具优势. 最后将FETLBO算法应用于拉压弹簧优化设计问题及0-1背包问题，取得了满意结果.
- 进化算法 /
- 精英教学优化算法 /
- 反馈 /
- 函数优化
Abstract: Elitist teaching-learning-based optimization (ETLBO) is a novel optimization algorithm based on the practical teaching-learning process of the class. In this paper, we propose a feedback elitist teaching-learning-based optimization (FETLBO) to solve the problem of low precision and poor stability of the ETLBO. Based on the ETLBO, a feedback phase is introduced at the end of the learner phase to increase the learning style and ensure the diversity of students so as to improve the algorithm's global search ability. Meanwhile, the feedback phase is for the slow students to communicate with the teacher and enables them to be close to the teacher quickly, so that the algorithm uses the fine local search and improves the precision. Six unconstrained and five constrained classic tests show that the FETLBO algorithm outperforms the other algorithms in precision and stability. Finally, the FETLBO algorithm is applied to the tension/compression spring design problem and the 0-1 knapsack problem, and obtains satisfactory results.
- Evolutionary algorithms /
- elitist teaching-learning-based optimization algorithm /
- feedback /
- function optimization

HTML全文

参考文献(24)

[1]	Holland J H. Adaptation in Natural and Artificial Systems. Ann Arbo: University of Michigan Press, 1975. 1-53
[2]	Kennedy J, Eberhart R C. Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks. Perth, Auslralia: IEEE, 1995. 1942-1948
[3]	Pan Feng, Chen Jie, Xin Bin, Zhang Juan. Several characteristics analysis of particle swarm optimizer. Acta Automatica Sinica, 2009, 35(7): 1010-1016(潘峰, 陈杰, 辛斌, 张娟. 粒子群优化方法若干特性分析. 自动化学报, 2009, 35(7): 1010-1016)
[4]	Pan Feng, Chen Jie, Gan Ming-Gang, Cai Tao, Tu Xu-Yan. Model analysis of particle swarm optimizer. Acta Automatica Sinica, 2006, 32(3): 368-377(潘峰, 陈杰, 甘明刚, 蔡涛, 涂序彦. 粒子群优化算法模型分析. 自动化学报, 2006, 32(3): 368-377)
[5]	Jin Xin-Lei, Ma Long-Hua, Wu Tie-Jun, Qian Ji-Xin. Convergence analysis of the particle swarm optimization based on stochastic processes. Acta Automatica Sinica, 2007, 33(12): 1263-1268(金欣磊, 马龙华, 吴铁军, 钱积新. 基于随机过程的PSO收敛性分析. 自动化学报, 2007, 33(12): 1263-1268)
[6]	Qian W Y, Li A J. Adaptive differential evolution algorithm for multi-objective optimization problems. Applied Mathematic and Computation, 2008, 201(1-2): 431-440
[7]	Storn R, Price K. Differential evolution——a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 1997, 11(4): 341-359
[8]	He S, Wu Q H, Saunders J R. Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Transactions on Evolutionary Computation, 2009, 13(5): 973-990
[9]	Karaboga D, Basturk B. On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 2008, 8(1): 687-697
[10]	Karaboga D, Basturk B. A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 2009, 214(1): 108-132
[11]	Karaboga D, Basturk B. A Powerful and efficient algorithm for numerical function optimization: artificial bee colony algorithm. Journal of Global Optimization, 2007, 39(3): 459-471
[12]	Rao R V, Savsani V J, Vakharia D P. Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer Aided Design, 2011, 43(3): 303-315
[13]	Rao R V, Savsani V J, Vakharia D P. Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems. Information Sciences, 2012, 183(1): 1-15
[14]	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. Engineering Applications of Artificial Intelligence, 2012, 25(8): 1577-1588
[15]	Rao R V, Patel V. An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations, 2012, 3(4): 535-560
[16]	Rajasekhar A, Rani R, Ramya K, Abraham A. Elitist teaching-learning opposition based algorithm for global optimization. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. Seoul, Korea: IEEE, 2012. 1124-1129
[17]	Nian Xiao-Yu, Wang Zhen-Lei, Qian Feng. A hybrid algorithm based on differential evolution and group search optimization and its application on ethylene cracking furnace. Chinese Journal of Chemical Engineering, 2013, 21(5): 537-543
[18]	He Q, Wang L. An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Engineering Applications of Artificial Intelligence, 2007, 20(1): 89-99
[19]	Ray T, Liew K M. Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Transactions on Evolutionary Computation, 2003, 7(4): 386-396
[20]	Wang Y, Cai Z X, Zhou Y R. Accelerating adaptive trade-off model using shrinking space technique for constrained evolutionary optimization. International Journal for Numerical Methods in Engineering, 2009, 77(11): 1501-1534
[21]	Huang F Z, Wang L, He Q. An effective co-evolutionary differential evolution for constrained optimization. Applied Mathematics and computation, 2007, 186(1): 340-356
[22]	Zou D X, Gao L Q, Li S, Wu J H. Solving 0-1 knapsack problem by a novel global harmony search algorithm. Applied Soft Computing, 2011, 11(2): 1556-1554
[23]	Mahdavi M, Fesanghary M, Damangir E. An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation, 2007, 188(2): 1567-1579
[24]	Gao Fang, Cui Gang, Wu Zhi-Bo, Liu Hong-Wei, Yang Xiao-Zong. Virus-evolutionary particle swarm optimization algorithm for knapsackproblem. Journal of Harbin Institute of Technology, 2009, 41(6): 103-107(高芳, 崔刚, 吴智博, 刘宏伟, 杨孝宗. 求解背包问题的病毒协同进化粒子群算法. 哈尔滨工业大学学报, 2009, 41(6): 103-107)