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山东大学学报(理学版) ›› 2014, Vol. 49 ›› Issue (08): 118-124.doi: 10.6040/j.issn.1671-9352.7.2014.002

• 论文 • 上一篇    

基于粒子群优化的适应Memetic算法分析

曲滨鹏1, 王智昊2   

  1. 1. 山东医学高等专科学校卫生管理系, 山东 济南 250002;
    2. 山东师范大学信息科学与工程学院, 山东 济南 250014
  • 收稿日期:2014-07-01 修回日期:2014-07-09 发布日期:2014-09-24
  • 作者简介:曲滨鹏(1975-),男,硕士,讲师,研究方向为计算机技术工程.E-mail:Teacherqu@163.com

A study of adaptive Memetic algorithm based on particle swarm optimization

QU Bin-peng1, WANG Zhi-hao2   

  1. 1. Department of Health Management, Shandong Medical College, Jinan 250002, Shandong, China;
    2. School of Information Science and Engineering, Shandong Normal University, Jinan 250014, Shandong, China
  • Received:2014-07-01 Revised:2014-07-09 Published:2014-09-24

摘要: 通过对已提出的适应Memetic算法的研究与分析,采用改进粒子群优化作为Memetic算法的全局优化策略按照不同类型的适应Memetic算法构成六类基于粒子群优化的适应Memetic算法,并用于求解典型的测试函数。根据对实验结果分析这几类算法的优缺点。实验结果表明适应PMemetic算法提高了全局搜索能力、收敛速度和解的精度。

关键词: Memetic算法, 局部搜索策略, 粒子群优化

Abstract: According to the analyzing of the proposed adaptive Memetic algorithm,the Particle Swarm Optimization is used as Memetic algorithm's GS. An Adaptive Memetic Algorithm Based on Particle Swarm Optimization is constituted according to different types of Adaptive Memetic. The experimental results show that the PMemetic algorithm improves the global search capability, the convergence speed and the solution accuracy. Experimental results demonstrate that the algorithm can get the better optimal path.

Key words: local search, Memetic algorithm, particle swarm optimization

中图分类号: 

  • TP301
[1] KRASNOGOR N, SMITH J E. A tutorial for competent memetic algorithms: model, taxonomy and design issues[J]. IEEE Transactions on Evolutionary Computation, 2005, 9(5):474-488.
[2] MCKAY M, BECKMAN R, CONOVER W. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code[J]. Technometrics, 1979, 21(2):239-245.
[3] CRAWFORD B, CASTRO C, MONFROY E. A hyperheuristic approach for guiding enumeration in constraint solving[J]. Advances in Intelligent Systems and Computing, 2013, 175:171-188.
[4] MAASHI M, ZCAN E, KENDALL G. A multi-objective hyper-heuristic based on choice function[J]. Expert Systems with Applications, 2014, 41(9):4475-4493.
[5] KOULINAS G, ANAGNOSTOPOULOS K. A new tabu search-based hyper-heuristic algorithm for solving construction leveling problems with limited resource availabilities[J]. Automation in Construction, 2013, 31:169-175.
[6] DETTERER D, KWAN P. A co-evolving memetic wrapper for Herb-Herb interaction analysis in TCM informatics[J]. Lecture Notes in Computer Science, 2012, 7104:361-371.
[7] HUANG Wei-Hsiu, CHANG Pei-Chann, LIMB Meng-Hiot. Memes co-evolution strategies for fast convergence in solving single machine scheduling problems[J]. 2012, 50(24):7357-7377.
[8] NERI F, WEBER M, CARAFFINI F, et al. Meta-Lamarckian learning in three stage optimal memetic exploration[EB/OL]. Computational Intelligence (UKCI), 2012. http://www.researchgate.net/researcher/2004327828_M_Weber
[9] KANG S, YANG H, SCHOR L, et al. Multi-objective mapping optimization via problem decomposition for many-core systems[EB/OL]. 2012 IEEE 10th Symposium onEmbedded Systems for Real-time Multimedia (ESTIMedia), 2012. http://www.computer.org/csdl/proceedings/estimedia/2012/4968/00/06507026.pdf
[10] LIU Bo, WANG Ling, JIN Yi-Hui. An effective PSO-based memetic algorithm for flow shop scheduling[C]//IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 2007, 37(1): 18-27. DOI: 10.1109/TSMCB.2006.883272
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