混合模式搜索的分布式memetic差分进化算法

doi:10.11772/j.issn.1001-9081.2014.05.1267

计算机应用 ›› 2014, Vol. 34 ›› Issue (5): 1267-1270.DOI: 10.11772/j.issn.1001-9081.2014.05.1267

混合模式搜索的分布式memetic差分进化算法

张春美,郭红戈

太原科技大学电子信息工程学院，太原 030024

收稿日期:2013-11-07 修回日期:2013-12-20 出版日期:2014-05-01 发布日期:2014-05-30
通讯作者: 张春美
作者简介:张春美(1978-)，女，山西五台人，讲师，博士，主要研究方向：智能优化；郭红戈(1977-)，女，山西潞城人，讲师，硕士，主要研究方向：智能信息处理、优化算法、预测控制。
基金资助:
国家自然科学基金资助项目;山西省基础研究计划项目（青年）;太原科技大学校青年基金

Distributed memetic differential evolution algorithm combined with pattern search

ZHANG Chunmei,GUO Hongge

School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan Shanxi 030024, china

Received:2013-11-07 Revised:2013-12-20 Online:2014-05-01 Published:2014-05-30
Contact: ZHANG Chunmei
Supported by:
National Natural Science Foundation

摘要/Abstract

摘要：

针对差分进化（DE）算法存在的早熟收敛与搜索停滞的问题，提出memetic分布式差分进化（DDE）算法。将memetic算法的思想融入到差分进化算法中，采用分布式的种群结构以及memetic算法中的混合策略，前者将初始种群分为多个子种群，子种群间根据冯·诺依曼拓扑结构周期性地实现信息交流，后者将差分进化算法作为进化的主要框架，模式搜索作为辅助手段，从而平衡算法的探索与开发能力。所提算法充分利用了模式搜索和差分进化算法的优势，建立了有效的搜索机制，增强了算法摆脱局部最优的能力，能够满足搜索过程对种群多样性及收敛速度的需求。将所提算法与几种先进的差分进化算法相比较，对标准测试函数进行优化的实验结果显示：所提算法在解的质量和收敛性能方面，均优于其他几种相比较的先进的差分进化算法。

Abstract:

In view of the problem of premature convergence and stagnation in the Differential Evolution (DE), the distributed memetic differential evolution was put forward. The idea of memetic algorithm was introduced into the DE algorithm. The distributed population structure and the combination strategy in memetic algorithm were applied. In the former strategy, the initial population was divided into multiple subpopulations according to the von Neumann topology and the periodical information exchange was realized among the subpopulations. And in the latter idea, the differential evolution was taken as an evolutionary frame that was assisted by pattern search to balance the exploration and exploitation abilities. The proposed algorithm made full use of advantages of the pattern search and differential evolution, set up an effective search mechanism and enhanced the algorithm to break away from local optima so as to satisfy the demand on population diversity and convergence speed of the search process. The proposed algorithm was run on a set of classic benchmark functions and compared with several state-of-the-art DE algorithms. Numerical results show that the proposed algorithm has excellent performance in terms of solution quality and convergence speed for all test problems given in this study.

中图分类号:

TP18

张春美郭红戈. 混合模式搜索的分布式memetic差分进化算法[J]. 计算机应用, 2014, 34(5): 1267-1270.

ZHANG Chunmei GUO Hongge. Distributed memetic differential evolution algorithm combined with pattern search[J]. Journal of Computer Applications, 2014, 34(5): 1267-1270.

参考文献

［1］CHEN J, XIN B, DOU L. Centralization and decentralization of intelligent optimization ［J］. CAAI Transactions on Intelligent Systems, 2007,2(2):48-56.(陈杰,辛斌, 窦丽华.关于智能优化方法的集聚性与弥散性问题［J］.智能系统学报,2007,2(2):48-56.)
［2］MACREADY W G, WOLPERT D H. Bandit problems and the exploration/exploitation tradeoff ［J］. IEEE Transactions on Evolutionary Computation, 1998,2(1):2-22.
［3］XIN B, CHEN J, ZHANG J, et al. Hybridizing differential evolution and particle swarm optimization to design powerful optimizers: a review and taxonomy［J］. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications & Reviews, 2012,42(5):744-767.
［4］CHEN J, XIN B, PENG Z, et al. Optimal contraction theorem for exploration-exploitation tradeoff in search and optimization ［J］. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 2009,39(3):680-691.
［5］KRASNOGOR N, SMITH J. A tutorial for competent memetic algorithms: model, taxonomy, and design issues ［J］. IEEE Transactions on Evolutionary Computation, 2005,9(5):474-488.
［6］ZHANG C, CHEN J, XIN B. Distributed memetic differential evolution with the synergy of Lamarckian and Baldwinian learning ［J］. Applied Soft Computing, 2013,13(5):2947-2959.
［7］TASOULIS D K, PAVLIDIS N G, PLAGIANAKOS V P, et al. Parallel differential evolution ［C］// Proceedings of the 2004 Congress on Evolutionary Computation. Piscataway: IEEE Press, 2004,2:2023-2029.
［8］DORRONSORO B, BOUVRY P. Improving classical and decentralized differential evolution with new mutation operator and population topologies ［J］. IEEE Transactions on Evolutionary Computation, 2011,15(1):67-98.
［9］SINGH L, KUMAR S. Parallel evolutionary asymmetric subsethood product fuzzy-neural inference system: an island model approach ［C］// Proceedings of the 2007 International Conference on Computing: Theory and Applications. Piscataway: IEEE Press, 2007:282-286.
［10］APOLLONI J, LEGUIZAMON G, GARCIA-NIETO J, et al. Island based distributed differential evolution: an experimental study on hybrid testbeds ［C］// Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems. Piscataway: IEEE Press,2008: 696-701.
［11］de FALCO I, MAISTO D, SCAFURI U, et al. Distributed differential evolution for the registration of remotely sensed images ［C］// Proceedings of the 2007 15th EUROMICRO International Conference on Parallel, Distributed and Network-Based Processing. Piscataway: IEEE Press, 2007:358-362.
［12］WEBER M, NERI F, TIRRONEN V. A study on scale factor in distributed differential evolution ［J］. Information Science, 2011,181(12):2488-2511.
［13］WEBER M, TIRRONEN V, NERI F. Scale factor inheritance mechanism in distributed differential evolution ［J］. Soft Computing, 2010,14(11):1187-1207.
［14］ZHANG C. Research on adaptive differential evolution and its application in the weapon target assignment ［D］. Beijing: Beijing Institute of Technology, 2013.(张春美.适应性差分进化算法及其在武器目标分配中的应用研究［D］.北京：北京理工大学，2013.)
［15］HOOKE R, JEEVES T A. "Direct Search" solution of numerical and statistical problems ［J］. Journal of the ACM, 1961,8(2):212-229.
［16］HAN M F, LIN C T, CHANG J Y, et al. Group-based differential evolution for numerical optimization problems ［J］. International Journal of Innovative Computing, Information and Control, 2013,9(3):1357-1372.
［17］ZHANG C, CHEN J, XIN B, et al. Differential evolution with adaptive population size combining lifetime and extinction mechanisms ［C］// Proceedings of the 8th Asian Control Conference. Piscataway: IEEE Press, 2011:1221-1226.

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混合模式搜索的分布式memetic差分进化算法

Distributed memetic differential evolution algorithm combined with pattern search

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