[1]刘昌芬,韩红桂,乔俊飞.广义逆向学习方法的自适应差分算法[J].智能系统学报,2015,10(01):131-137.[doi:10.3969/j.issn.1673-4785.201310068]
　LIU Changfen,HAN Honggui,QIAO Junfei.Self-adaptive DE algorithm via generalized opposition-based learning[J].CAAI Transactions on Intelligent Systems,2015,10(01):131-137.[doi:10.3969/j.issn.1673-4785.201310068]
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广义逆向学习方法的自适应差分算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年01期
页码:
131-137
栏目:
出版日期:
2015-03-25

文章信息/Info

Title:
Self-adaptive DE algorithm via generalized opposition-based learning
作者:
刘昌芬1; 2;  韩红桂1; 2;  乔俊飞1; 2
1. 北京工业大学 电子信息与控制工程学院, 北京 100124;
2. 计算智能与智能系统北京市重点实验室, 北京 100124
Author(s):
LIU Changfen1; 2;  HAN Honggui1; 2;  QIAO Junfei1; 2
1. College of Electronic and Control Engineering, Beijing University of Technology, Beijing 100124, China;
2. Beijing Key Laboratory of Computational Intelligence and Intelligence System, Beijing 100124, China
关键词:
差分算法; 优化; 自适应; 逆向学习; 收敛速度; 精度; 高维; 初始化
Keywords:
differential evolution; optimization; generalized opposition-based learning; convergencespeed; accuracy; highdimension; initialization
分类号:
TP18;O224
DOI:
10.3969/j.issn.1673-4785.201310068
文献标志码:
A
摘要:
针对差分算法(differential evolution, DE)在解决高维优化问题时参数设置复杂、选择变异策略困难的现象,提出了广义逆向学习方法的自适应差分进化算法(self-adaptive DE algorithm via generalized opposition-based learning, SDE-GOBL)。利用广义的逆向学习方法(generalized opposition-based learning, GOBL)来进行多策略自适应差分算法(Self-adaptive DE, SaDE)的初始化策略调整,求出各个候选解的相应逆向点,并在候选解和其逆向点中选择所需要的最优初始种群,然后再进行自适应变异、杂交、选择操作,最后通过CEC2005国际竞赛所提供的9个标准测试函数对SDE-GOBL算法进行验证,结果证明该算法具有较快的收敛速度和较高的求解精度。
Abstract:
The problem related to defects of complex parameter setting and difficult selection of mutation strategies existing in the differential evolution (DE) algorithm when solving high-dimensional optimization problem is studied. This paper proposed a new self-adaptive DE algorithm based on generalized opposition-based learning (SDE-GOBL). The generalized opposition-based learning (GOBL) is utilized for the adjustment of initiation strategy on multi-strategy self-adaptive DE (SaDE) algorithm. The corresponding reverse points of each candidate solution are figured out. In addition, the necessary optimal initial population is selected among the candidate solutions and its reverse points. Next, the self-adaptive mutation, hybridization and selection operations are conducted. Finally, nine standard test functions provided in CEC2005 International Competition are applied for demonstrating SDE-GOBL algorithm. The result showed that the algorithm has fast convergence speed and high solution precision.

参考文献/References:

[1] STORN R, PRICE K. Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11(4): 341-359.
[2] ALATAS B, AKIN E. MODENAR: multi-objective differential evolution algorithm for mining numeric association rules[J]. Applied Soft Computing, 2008, 8(1):646-656.
[3] DAS S, ABRAHAM A. Automatic clustering using an improved differential evolution algorithm[J].IEEE Trans on Systems Man and Cybernetics, 2008, 38(1): 218-237.
[4] FEOKTISTOV V. Differential evolution: in search of solutions[M]. New York: Springer Verlag, 2006: 205-276.
[5] CHAKRABORTY U. Advances in differential evolution[M]. Berline: Springer Verlag, 2008: 800-805.
[6] QING A. Differential evolution: fundamentals and applications in electrical engineering[M]. IEEE & John Wiley, 2009: 580-590.
[7] QING A, LEE C K. Differential evolution in electromagnetics[M]. Berlin: Springer Verlag, 2010:508-530.
[8] ONWUBOLU G C, DAVENDRA D. Differential evolution: a handbook for global permutation-based combinatorial optimization[M]. Berlin: Springer Verlag, 2009: 301-320.
[9] QIN A K, SUGANTHAN P N. Self-adaptive differential evolution algorithm for numerical optimization[J]. Proc IEEE Congr Evolut Comput, 2005, 2 (2): 1785-1791.
[10] ABBASS H A. The self-adaptive Pareto differential evolution algorithm[J]. Proc Congr Evolut Comput, 2002, 1: 831-836.
[11] OMRAN M G H, SALMAN A, ENGELBRECHT A P. Self-adaptive differential evolution[J]. Lecture Notes in Artificial Intelligence, 2005, 3801: 192-199.
[12] TEO J. Exploring dynamic self-adaptive populations in differential evolution[J]. Soft Comput, 2006, 10 (8): 637-686.
[13] QIN A K, HUANG V L, SUGANTHAN P N. Differential evolution algorithm with strategy adaptation for global numerical optimization[J]. IEEE Trans Evolut Comput, 2009, 13(2): 398-417.
[14] RAHNAMAYAN S, TIZHOOSH H R, SALAMA M M A. Opposition based differential evolution[J].IEEE Trans Evolut Comput, 2008, 12(1): 64-79.
[15] 吴亮红,王耀南,袁小芳.自适应二次变异差分进化算法[J].控制与决策, 2006, 21(8): 898-902.WU Lianghong, WANG Yaonan, YUAN Xiaofang. Differential evolution algorithm with adaptive second mutation[J].Control and Decision, 2006, 21(8): 898- 902.
[16] 陈亮.改进自适应差分算法及其应用研究[D]. 上海:东华大学,2012:39-40.CHEN Liang. Improved adaptive differential evolution algorithm and its applications[D]. Shanghai: Donghua University, 2010: 39-40.
[17] 刘荣辉. 多阶段自适应差分进化算法及应用研究[D].上海:东华大学, 2012: 23-29.LIU Ronghui. Multi-stage self-adapting differential evolution algorithm and its application[D].Shanghai: Donghua University, 2010: 39-40.
[18] WANG Hui, WU Zhijian, RAHNAMAYAN S. Enha nced opposition-based differential evolution for solving high-dimensional continuous optimization problems[J]. Soft Comput, 2010, 15(11): 2127-2140.
[19] RAHNAMAYAN S, TIZHOOSH H R, SALAMA M M A. Opposition versus randomness in soft computing techniques[J]. Soft Comput, 2008, 8(2): 906-918.
[20] SUGANTHAN P N, HANSEN N, LIANG J J, et al. Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization[J]. KanGAL Report, 2005, 2005005.
[21] 贾丽媛,张弛.自适应差分演化进化算法[J].中南大学学报:自然科学版, 2013, 44(9): 3759-3765.JIA Liyuan, ZHANG Chi. Self-adaptive differential evolution[J]. Journal of Central South University: Science and Technology, 2013, 44(9): 3759-3765.
[22] GäMPERLE R, MüLLER S D, KOUMOUTSAKOS P. A parameter study for differential evolution[J]. Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, 2002, 10: 293-298.

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备注/Memo

备注/Memo:
收稿日期:2013-10-25;改回日期:。
基金项目:国家自然科学基金资助项目(61034008,61203099,61225016);北京市自然科学基金资助项目(4122006);教育部博士点新教师基金资助项目(20121103120020);北京市科技新星计划资助项目(Z131104000413007).
作者简介:刘昌芬,女,1990年生,硕士研究生,主要研究方向为智能控制理论及应用;韩红桂,男,1983年生,教授,主要研究方向为污水处理过程建模、优化与控制。入选北京市科技新星计划、北京市组织部优秀人才等。申请国家发明专利13项,获专利授权7项。近5年来,发表学术论文30余篇。参与编写专著3部;乔俊飞,男,1968年生,教授,博士生导师,主要研究方向为智能信息处理、智能优化控制。国家杰出青年基金获得者,教育部新世纪优秀人才,北京市精品课程负责人。教育部科技进步奖一等奖和北京市科学技术奖三等奖各1项,获国家发明专利授权12项。近5年发表学术论文近70篇,被SCI收录15篇。
通讯作者:乔俊飞.E-mail:liuchangfen2009@163.com.
更新日期/Last Update: 2015-06-16