求解变量重叠型大尺度优化问题的相关性学习协同演化策略

doi:10.3969/j.issn.0372-2112.2018.03.003

电子学报 ›› 2018, Vol. 46 ›› Issue (3): 529-536.DOI: 10.3969/j.issn.0372-2112.2018.03.003

求解变量重叠型大尺度优化问题的相关性学习协同演化策略

王豫峰^1,2, 董文永¹, 董学士¹

1. 武汉大学计算机学院, 湖北武汉 430072;
2. 南阳理工学院软件学院, 河南南阳 473000

收稿日期:2016-09-29 修回日期:2017-01-05 出版日期:2018-03-25
- 作者简介:
- 王豫峰,男,1982年生于山东平度,武汉大学计算机学院博士生,研究方向为智能计算、机器学习、数据降维等.E-mail:shandian9876@163.com;董文永,男,1973年生于河南南阳,计算机软件与理论博士,现为武汉大学计算机学院教授、博士生导师.长期从事系统科学、仿真与控制、演化计算、并行计算、机器学习、数据挖掘等方面的工作.E-mail:dwy@whu.edu.cn;董学士,男,1985年出生于山东日照,武汉大学计算机学院博士生,研究方向为机器学习、智能计算等.E-mail:dxs_cs@163.com.
- 基金资助:
- 国家自然科学基金 (No.61170305，No.61672024）; 河南省高等学校重点科研项目计划 (No.17A520046）

Cooperative Coevolution with Correlation Learning Between Variables for Large Scale Overlapping Problem

WANG Yu-feng^1,2, DONG Wen-yong¹, DONG Xue-shi¹

1. Computer School, Wuhan University, Wuhan, Hubei 430072, China;
2. Software School, Nanyang Institute of Technology, Nanyang, Henan 473000, China

Received:2016-09-29 Revised:2017-01-05 Online:2018-03-25 Published:2018-03-25
- Supported by:
- National Natural Science Foundation of China (No.61170305, No.61672024); Key Scientific Research Programs of colleges and universities of Henan Province (No.17A520046)

摘要/Abstract

摘要： 协同演化是解决大尺度连续优化问题的一种有效策略.但是，对于决策变量重叠型（决策变量不可分且相互依赖）的高维问题，其分组方法可能会误导算法的搜索.针对这一情况，本文提出一种全新的协同演化策略（Differential Evolution Cooperative Coevolution with Correlation Learning Between Variables，DECC-CLV），其思想是首先计算演化种群分布所包含的主特征轴，然后计算各维决策变量在主轴上的投影值并利用它们之间的正负相关性进行分组.该算法在迭代过程中，利用期望最大化算法对种群进行概率主成分分析，并根据决策变量在当前种群主轴上的投影值大小关系对其进行动态分组.通过和目前主流的演化算法在CEC2013的第三类函数上的仿真试验和分析，验证了算法的有效性和适用性.

Abstract: Cooperative co-evolution (CC) is an effective strategy to solve large-scale continuous optimization problem. However, its grouping method may mislead the search direction when solving the large-scale overlapping problem (decision variables are non-separable and interact with each other). In order to overcome this issue, we propose a differential evolution cooperative coevolution with correlation learning between variables (DECC-CLV) to improve the performance of CC. DECC-CLV firstly detects the positive and negative correlations of variables based on the projected value of decision variables on the principal component of the population, and then groups variables into different groups. During the evolutionary process, DECC-CLV employs the expectation maximization algorithm for probabilistic principal component analysis on the population to deduce the complexity. Comparing with the state-of-the-art CCs on the large-scale overlapping benchmark functions on CEC2013, the experimental results verified the effectiveness and applicability of our proposed algorithm.

Key words: large-scale optimization problem, variables correlation, cooperative co-evolution, large-scale optimization problem decomposition

中图分类号:

TP301

王豫峰, 董文永, 董学士. 求解变量重叠型大尺度优化问题的相关性学习协同演化策略[J]. 电子学报, 2018, 46(3): 529-536.

WANG Yu-feng, DONG Wen-yong, DONG Xue-shi . Cooperative Coevolution with Correlation Learning Between Variables for Large Scale Overlapping Problem[J]. Acta Electronica Sinica, 2018, 46(3): 529-536.

参考文献

[1] 巩敦卫,季新芳,孙晓燕.基于集合的高维多目标优化问题的进化算法[J].电子学报,2014,42(1):77-83. GONG Dun-wei,JI Xin-fang,SUN Xiao-yan.Solving many-objective optimization problems using set-based evolutionary algorithms[J].Acta Electronica Sinica,2014,42(1):77-83.(in Chinese)
[2] POTTER M A,JONG K A D.A cooperative coevolutionary approach to function optimization[A].Proceedings of The Parallel Problem Solving From Nature-PPSN Ⅲ[C].Berlin Heidelberg:Springer,1994.249-257.
[3] CHUNYU H U,LIU H,ZHANG P.Cooperative co-evolutionary artificial bee colony algorithm based on hierarchical communication model[J].Chinese Journal of Electronics,2016,25(3):570-576.
[4] YANG Z,TANG K,YAO X.Large scale evolutionary optimization using cooperative coevolution[J].Inform Sciences,2008,178(15):2985-2999.
[5] OMIDVAR M N,LI X,MEI Y,et al.Cooperative co-evolution with differential grouping for large scale optimization[J].Evolutionary Computation IEEE Transactions on,2014,18(3):378-393.
[6] CHEN W,WEISE T,YANG Z,et al.Large-scale global optimization using cooperative coevolution with variable interaction learning[A].Proceedings of the Parallel Problem Solving From Nature-PPSN Xi[C].Kraków,Poland:Springer,2010.300-309.
[7] TIPPING M E,BISHOP C M.Probabilistic principal component analysis[J].Journal of the Royal Statistical Society,1999,61(3):611-622.
[8] BISHOP C M.Pattern Recognition and Machine Learning[M].Berlin:Springer,2006.571-586.
[9] 周鑫.基于EM算法的G0分布参数最大似然估计[J].电子学报,2013,41(1):178-184. ZHOU Xin.An EM algorithm based maximum likelihood parameter estimation method for the G0 distribution[J].Acta Electronica Sinica,2013,41(1):178-184.(in Chinese)
[10] TANG K,LI X,SUGANTHAN P N,et al.Benchmark functions for the CEC'2013 special session and competition on large-scale global optimization[J].Nature Inspired Computation & Applications,2013,7(33):1-8.
[11] KENNEDY J.Particle swarm optimization[A].Proceedings of the Neural Networks,1995[C].Perth,Western Australia:IEEE,1995.1942-1948
[12] KARABOGA D.An Idea Based on Honey Bee Swarm for Numerical Optimization[D].Kayseri:Erciyes University,2005.
[13] OMIDVAR M N,LI X,YAO X.Cooperative co-evolution with delta grouping for large scale non-separable function optimization[A].Proceedings of the Evolutionary Computation,2010 IEEE Congress on[C].Barcelona,Spain:IEEE,2010.1-8.
[14] YANG Z,TANG K,YAO X.Self-adaptive differential evolution with neighborhood search[A].Proceedings of the Evolutionary Computation,2008[C].Hong Kong,China:IEEE,2008.1110-1116.

[1]	刘景森, 李浩然, 李煜, 周欢. 一种面向大规模复杂全局优化的流场吸引动态涡流搜索算法[J]. 电子学报, 2023, (): 1-7.
[2]	黄鹤, 李文龙, 吴琨, 杨澜, 王会峰, 王萍. 动态自适应特征融合的MFOPA跟踪器[J]. 电子学报, 2023, (): 1-10.
[3]	张淑军, 彭中, 李辉. SAU-Net：基于U-Net和自注意力机制的医学图像分割方法[J]. 电子学报, 2022, 50(10): 2433-2442.
[4]	陇盛, 陶蔚, 张泽东, 陶卿. 非光滑强凸情形Adam型算法的最优收敛速率[J]. 电子学报, 2022, 50(9): 2049-2059.
[5]	张长胜, 张健忠, 钱斌, 胡蓉. 多策略融合的改进天鹰优化算法[J]. 电子学报, 2022, (): 1-11.
[6]	邓海刚, 王传旭, 李成伟, 林晓萌. 深度学习框架下群组行为识别算法综述[J]. 电子学报, 2022, 50(8): 2018-2036.
[7]	穆磊, 王鹏. 量子启发式优化算法的尺度动态调速机制[J]. 电子学报, 2022, 50(7): 1664-1673.
[8]	李凯, 张可心. 结构α-熵的加权高斯混合模型的子空间聚类[J]. 电子学报, 2022, 50(3): 718-725.
[9]	于娟, 杨琼, 鲁剑锋, 韩建民, 彭浩. 高级地图匹配算法：研究现状和趋势[J]. 电子学报, 2021, 49(9): 1818-1829.
[10]	周鹏, 董朝轶, 陈晓艳, 齐咏生, 赵肖懿, 王启来. 基于阶梯式Tent混沌和模拟退火的樽海鞘群算法[J]. 电子学报, 2021, 49(9): 1724-1735.
[11]	李彦苍, 巩翔宇. 基于信息熵的改进狮群算法及其在组合优化中的应用[J]. 电子学报, 2021, 49(8): 1577-1585.
[12]	林运国. 一维离散时间量子行走的路径分析法、概率分布与对称性[J]. 电子学报, 2021, 49(7): 1323-1330.
[13]	刘景森, 马义想, 李煜. 改进蝴蝶算法求解多维复杂函数优化问题[J]. 电子学报, 2021, 49(6): 1068-1076.
[14]	廖彬, 黄静莱, 王鑫, 孙瑞娜, 葛晓燕, 国冰磊. SCEA:一种适应高维海量数据的并行聚类集成算法[J]. 电子学报, 2021, 49(6): 1077-1087.
[15]	董海, 齐新娜. 基于改进细菌觅食算法的自动化立体仓库集成优化[J]. 电子学报, 2021, 49(5): 1001-1012.

求解变量重叠型大尺度优化问题的相关性学习协同演化策略

Cooperative Coevolution with Correlation Learning Between Variables for Large Scale Overlapping Problem

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics