解决动态多中心问题的自学习差异进化算法
Self-learning differential evolution algorithm for dynamic polycentric problems
- 通信学报 2015年36卷第7期 页码：166-175
- 作者机构：
1. 湖南商学院 计算机与信息工程学院，湖南 长沙 410205
2. 国防科学技术大学 计算机学院，湖南 长沙 410073
- 作者简介：
[ "刘星宝（1977-），男，山东临沂人，博士，湖南商学院讲师，主要研究方向为智能计算、投资组合优化。" ]
[ "殷建平（1963-），男，湖南益阳人，博士，国防科学技术大学教授，主要研究方向为人工智能、模式识别和信息安全。" ]
[ "胡春华（1973-），男，湖南新化人，博士，湖南商学院教授，主要研究方向为云计算，电子商务。" ]
[ "陈荣元[通信作者]（1976-），男，江苏泰州人，博士，湖南商学院高级研究员，主要研究方向为图形图像处理。E-mail:chenrongyuan@126.com。" ]
- 基金信息：国家自然科学基金资助项目(61273232);国家自然科学基金资助项目(41101425);教育部新世纪优秀人才支持计划基金资助项目(NECT-2013-0785)
DOI：10.11959/j.issn.1000-436x.2015154
中图分类号： TP18-
网络首发：2015-07，
纸质出版：2015-07-25
- 稿件说明：
移动端阅览
刘星宝, 殷建平, 胡春华, 等. 解决动态多中心问题的自学习差异进化算法[J]. 通信学报, 2015,36(7):166-175.
Xing-bao LIU, Jian-ping YIN, Chun-hua HU, et al. Self-learning differential evolution algorithm for dynamic polycentric problems[J]. Journal on Communications, 2015, 36(7): 166-175.
刘星宝, 殷建平, 胡春华, 等. 解决动态多中心问题的自学习差异进化算法[J]. 通信学报, 2015,36(7):166-175. DOI： 10.11959/j.issn.1000-436x.2015154.
Xing-bao LIU, Jian-ping YIN, Chun-hua HU, et al. Self-learning differential evolution algorithm for dynamic polycentric problems[J]. Journal on Communications, 2015, 36(7): 166-175. DOI： 10.11959/j.issn.1000-436x.2015154.
摘要
为解决动态环境下的多中心优化问题，提出自学习差异进化算法。通过评估特定个体检测到环境变化，自学习算子将群体引至新的环境，并保持群体的拓扑结构不变，以继续当前的进化趋势。采用邻域搜索机制加快算法的收敛速度，引入随机个体迁入机制增加群体多样性。实验以周期动态函数为测试对象，比较自学习差异进化算法与部分智能优化算法的性能，结果表明，新算法有更快的收敛速度和更好的环境适应能力。
Abstract
A novel self-learning differential evolution algorithm is proposed to solve dynamical multi-center optimization problems.The approach of re-evaluating some specific individuals is used to monitor environmental changes.The proposed self-learning operator guides the evolutionary group to a new environment
meanwhile maintains the stable topology structure of group to maintain the current evolutionary trend.A neighborhood search mechanism and a random immigrant mechanism are adapted to make a tradeoff between algorithmic convergence and population diversity.The experiment studies on a periodic dynamic function set suits are done
and the comparisons with peer algorithms show that the self-learning differential algorithm outperforms other algorithms in term of convergence and adaptability under dynamical environment.
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references
NGUYEN T T , YANG S , BRANKE J . Evolutionary dynamic optimization:a survey of the state of the art [J ] . Swarm and Evolutionary Computation , 2012 , 6 : 1 - 24 .
CRUZ C , GONZÁLEZ J R , PELTA D A . Optimization in dynamic environments:a survey on problems,methods and measures [J ] . Soft Computing , 2011 , 15 ( 7 ): 1427 - 1448 .
陈莉 , 丁立新 . 动态优化算法综述 [J ] . 武汉大学学报(理学版) , 2011 , 57 ( 3 ): 255 - 264 .
CHEN L , DING L X . Survey on dynamic optimization algorithms [J ] . Journal of Wuhan University(Natural Science Edition) , 2011 , 57 ( 3 ): 255 - 264 .
FERNANDES C M , MERELO J J , RAMOS V , et al . A self organized criticality mutation operator for dynamic optimization problems [A ] . Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation [C ] . New York:ACM Press , 2008 . 937 - 944 .
GREFENSTETTE J . Genetic Algorithms for Changing Environments [M ] . Navy Center for Applied Research in Artificial Intelligence : Navy Research LaboratoryPress , 1992 . 137 - 144 .
YANG S . Genetic algorithms with memory and elitism-based immigrants in dynamic environments [J ] . Evolutionary Computation , 2008 , 16 ( 3 ): 385 - 416 .
YAO X.YANG S . Population-based incremental learning with associative memory for dynamic environments [J ] . IEEE Transactions on Evolutionary Computation , 2008 , 12 ( 5 ): 542 - 561 .
GOLDBERG D E , SMITH R E . Nonstationary function optimization using genetic algorithms with dominance and diploidy [A ] . Proceedings of the 2nd International Conference on Genetic Algorithms and Their Applications [C ] . Hillsdale:Lawrence Erlbaum Associates , 1987 . 59 - 68 .
MAVROVOUNIOTIS M , YANG S . Memory-based immigrants for ant colony optimization in changing environments [A ] . Proceedings of the 2011 International Conference on Applications of Evolutionary Computation-Volume Part I,EvoApplications’11 [C ] . Springer-Verlag,Berlin,Heidelberg , 2011 . 324 - 333 .
BENDTSEN C N , KRINK T . Dynamic memory model for nonstationary optimization [A ] . Proceeding of the 2002 Congress on Evolutionary Computation [C ] . New York,USA , 2002 . 145 - 150 .
周传华 , 谢安世 . 一种机遇动态小生境的自组织学习算法 [J ] . 软件学报 , 2011 , 57 ( 3 ): 255 - 264 .
ZHOU C H , XIE A S . Dynamic niche-based self-organizing learning algorithm [J ] . Journal of Software , 2011 , 22 ( 8 ): 1738 - 1748 .
YANG S . A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments [J ] . IEEE Transactions on Evolutionary Computation , 2010 , 14 ( 6 ): 959 - 974 .
吴晓军 , 杨战中 , 赵明 . 均匀搜索粒子群算法 [J ] . 电子学报 , 2012 , 40 ( 6 ): 1115 - 1120 .
WU X J , YANG Z Z , ZHAO M . The convergence analysis of the uniform search particle swarm optimization [J ] . Acta Elctronic Sinica , 2012 , 40 ( 6 ): 1115 - 1120 .
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 .
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 .
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