期刊文献+

优化和约束推理的动态分布式双向导遗传算法 被引量:2

A Dynamic Distributed Double Guided Genetic Algorithm for Optimization and Constraint Reasoning
下载PDF
导出
摘要 为了解决优化和约束推理,基于向导遗传算法(GGA)和分布式向导遗传算法(DGGA),通过引入向导概率Pguid、本地优化监测LOD和权ε共3个新参数,提出了一种D3G2A算法的改进算法。该算法采用多代理方法,不仅使搜索过程多样化,避免出现局部最优,而且代理能计算各自的遗传参数。将改进的D3G2A和GGA用于随机生成的二元CSPs,实验表明,D3G2A能有效改善适应度值和节省CPU时间开销,使算法的性能得到提高。 A Dynamic Distributed Double Guided Genetic Algorithm (D^3 G^2A) is a new multi-agent approach which leads to additive constraint satisfaction problem. This approach is inspired by the guided genetic algorithm (GGA) and by the dynamic distributed double guided genetic algorithm for Max_CSPs. It consists of agents dynamically created and cooperating in order to solve problem with each agent performing its own GA. Firstly, our approach is enhanced by three parameters, guidance probability (Pguid), local optima detector( LOD), weight (6), which allow not only diversification but also escaping from local optima. Secondly, the GGAs performed agents will no longer be the same. This is stirred by the natural laws. In fact, our approach will let the agents able to count their own GA parameters. In order to show D^3G^2A advantages, the approach and the GGA are applied to the randomly generated binary constraints satisfaction problems. Compared with the centralized guided genetic algorithm and applied to a set of literature known problems, our new approaches have been experimentally shown to be always better in terms of fitness values and CPU time.
作者 钟静 应宏
出处 《重庆师范大学学报(自然科学版)》 CAS 2009年第2期94-98,共5页 Journal of Chongqing Normal University:Natural Science
基金 重庆市教委科技计划(No.KJ081109) 重庆三峡学院青年资助项目(No.2006-sxxyqingnian-01)
关键词 优化 约束推理 动态分布式 双向导 遗传算法 optimization constraint reasoning dynamic distribution double guided genetic algorithm
  • 相关文献

参考文献8

  • 1杨轻云,孙吉贵,张居阳.最大度二元约束满足问题粒子群算法[J].计算机研究与发展,2006,43(3):436-441. 被引量:19
  • 2Tsang E P K, Wang C J, Davenport A, et al. A family of stochastic methods for constraint satisfaction and optimization[ R]. Colchester:Technical Report University of Essex, 1999.
  • 3Ghedira K, Jlifi B. A distributed guided genetic algorithm for max_CSPs[J]. Journal of Sciences and Technologies of Information (RSTI), Journal of Artificial Intelligence Series (RIA) ,2002,16(3 ) : 192-207.
  • 4Bouamama S, Ghedira K. ED3 G2A:an enhanced version of the dynamic distribute double guided genetic algorithms for max_ CSPs [ C ]. In proceedings of the 8th world multiconference on systemics, Cybernetics and Informatics (SCI' 04 ) , Orlando, Florida, USA, 2004.
  • 5Bouamama S, Ghedira K. D2 G2 A and D3 G2 A : a new generation of distributed guided genetic algorithms for max_CSPs [ C ]. In proceedings of the 7th world muhieonference on systemics, Cybernetics and informaties ( SC103 ), Orlando, Florida, USA, 2003.
  • 6Lau T L, Tsang E P K. Solving the radio link frequency assignment problem with the guided genetic algorithm[ C]. In proceedings of NATO symposium on radio length frequency assignment staring and conservation systems, Albrg, Denmark, UK, 1998.
  • 7杨晓琪.约束最优化问题的非线性无约束方法[J].重庆师范大学学报(自然科学版),2004,21(2):1-3. 被引量:2
  • 8刘双印,徐龙琴,沈玉利.改进小生境遗传算法在元搜索引擎调度优化中的研究[J].重庆师范大学学报(自然科学版),2008,25(3):46-50. 被引量:4

二级参考文献34

共引文献22

同被引文献26

引证文献2

二级引证文献3

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部