[1]潘长城,徐晨,李国.解全局优化问题的差分进化策略[J].深圳大学学报理工版,2008,25(2):211-215.
 PAN Chang-cheng,XU Chen,and LI Guo.Differential evolutionary strategies for global optimization[J].Journal of Shenzhen University Science and Engineering,2008,25(2):211-215.
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解全局优化问题的差分进化策略()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第25卷
期数:
2008年2期
页码:
211-215
栏目:
数学与应用数学
出版日期:
2008-04-30

文章信息/Info

Title:
Differential evolutionary strategies for global optimization
文章编号:
1000-2618(2008)02-0211-05
作者:
潘长城徐晨李国
深圳大学数学与计算科学学院,智能计算科学研究所,深圳 518060
Author(s):
PAN Chang-chengXU Chenand LI Guo
Institute of Intelligent Computing Science,College of Mathematics and Computational Science,Shenzhen University,Shenzhen 518060,P.R.China
关键词:
差分演化进化策略变异算子全局优化信号处理机器学习人工智能
Keywords:
differential evolutionevolutionary strategiesmutation operatorglobal optimizationdigital signal processingmachine learningartificial intelligence
分类号:
TP 391
文献标志码:
A
摘要:
以进化策略算法为框架,提出一种求解连续函数,特别是高维连续函数问题的优化算法——差分进化策略.该算法利用进化策略快速收敛的优点,融入了差分演化算法中具有较强全局搜索能力的变异算子.经数值实验分析表明,差分进化策略在函数优化过程中具有较强稳健性,可提高全局搜索能力,保持快速收敛优势,能用于研究生物进化、机器学习、人工智能、模糊系统及人工神经网络训练等领域.
Abstract:
A new algorithm,differential evolutionary strategies (DES),for the high-dimensional continuous function optimization,was proposed.The proposed algorithm was designed by making use of both the strong global search capability of differential evolutionary strategies and the rapidly converging capability of evolution strategies.Computer simulations were tested on several high-dimensional continuous function optimization problems,and the results indicate that the proposed algorithm improves the efficiency and is much more robust than conventional evolutionary strategies.The proposed algorithm can be used in biological evolution researching,machine learning,artificial intelligence,fuzzy system,artificial neural network training etc.,especially in digital signal processing,data mining and multi-programming.

参考文献/References:

[1]云庆夏.进化算法[M].北京:冶金工业出版社,2000:148-151.
[2]Storn R,Price K.差分演化算法-在连续空间上简单有效的全局优化策略[R].技术报告,TR-95-012,Berkeley,USA:ICSI,1995 (英文版).
[3]Price K,Storn R,Lampinen J.差分演化算法-一种实用的全局优化算法[M].柏林:Springer-Verlag出版社,2005:20-27 (英文版).
[4]Francois O.解全局最小化问题的进化策略及其马尔可夫链分析[J].IEEE进化计算学报,1998,2(3):77-90 (英文版).
[5]Ebenau C,Rottschäfer J,Thierauf G.加入惩罚函数的改进进化策略对混合离散结构的优化[J].工程软件进展,2005,36(1):29-38 (英文版).
[6]Yang S M,Shao D G,Luo Y J.一种新型的多目标函数优化进化策略[J].应用数学与计算学报,2005,170(2):850-873 (英文版).
[7]Montes E M,Coello C A C.求解约束优化问题的简单多成员进化策略[J].IEEE进化计算学报,2005,9(1):1-17 (英文版).
[8]Berlich R,Kunze M.并行进化算法[J].核仪器与物理研究方法,2003,502(2):467-470 (英文版).
[9]王正志,薄涛.进化计算[M].长沙:国防科技大学出版社,2000:258-262.
[10]徐宗本.计算智能——模拟进化计算[M].北京:高等教育出版社,2004:116-118.

[1]YUN Qing-xia.Evolutionary Algorithms[M].Beijing:Metallurgical Industry Press,2000:148-151(in Chinese).
[2]Storn R,Price K.Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces[R].Technical Report,TR-95-012,Berkeley,USA:ICSI,1995.
[3]Price K,Storn R,Lampinen J.Differential Evolution——A Practical Approach to Global Optimization[M].Springer-Verlag Press,2005:20-27.
[4]Francois O.An evolutionary strategy for global minimization and its Markov chain analysis[J].IEEE Trans on Evolutionary Computation,1998,2(3):77-90.
[5]Ebenau C,Rottschäfer J,Thierauf G.An advanced evolutionary strategy with an adaptive penalty functions for mixed-discrete structural optimization[J].Advances in Engineering Software,2005,36(1):29-38.
[6]YANG S M,Shao D G,Luo Y J.A novel evolution strategy for multi-objective optimization problem[J].Applied Mathematics and Computation,2005,170(2):850-873.
[7]Montes E M,Coello C A C.A simple multi-membered evolution strategy to solve constrained optimization problems[J].IEEE Trans on Evolutionary Computation,2005,9(1):1-17.
[8]Berlich R,Kunze M.Parallel evolutionary algorithms[J].Nuclear Instruments & Methods in Physics Research,2003,502(2):467-470.
[9]WANG Zheng-zhi,Bo Tao.Evolutionary Computation[M].Changsha:National University of Defense Technology Press,2000:258-262(in Chinese).
[10]XU Zong-ben.Computational Intelligence Simulated Evolutionary Algorithm[M].Beijing:Higher Education Press,2004:116-118(in Chinese).

备注/Memo

备注/Memo:
收稿日期:2007-10-22;修回日期:2008-02-18
基金项目:国家高技术研究发展计划(863)基金资助项目(2006AA01A116);深圳大学科研基金资助项目(4LGB)
作者简介:潘长城 (1983-) , 男(汉族) , 江苏省徐州市人,深圳大学在读硕士研究生.E-mail:ccpann@gmail.com
通讯作者:徐晨 (1965-),男(汉族),深圳大学教授.E-mail:xuchen@szu.edu.cn
更新日期/Last Update: 2008-05-11