自适应遗传算法在多边多议题协商中的应用

doi:10.13190/jbupt.200806.67.199

北京邮电大学学报 ›› 2008, Vol. 31 ›› Issue (6): 67-70.doi: 10.13190/jbupt.200806.67.199

自适应遗传算法在多边多议题协商中的应用

李剑1,2, 景博2, 杨义先1

(1. 北京邮电大学灾备技术国家工程实验室, 北京 100876; 2. 北京理工大学软件学院, 北京 100081)

收稿日期:2008-06-16 修回日期:2008-09-28 出版日期:2008-12-31 发布日期:2008-12-31
通讯作者: 李剑

Adaptive Genetic Algorithm and Its Application in Multi-lateral Multi-issue Negotiation

LI Jian1,2, JING Bo2, YANG Yi-xian1

(1. National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing 100876, China; 2. School of Software, Beijing Institute of Technology, Beijing 100876, China)

Received:2008-06-16 Revised:2008-09-28 Online:2008-12-31 Published:2008-12-31
Contact: LI Jian

摘要/Abstract

摘要：

为了使基于智能体电子商务中协商智能体最大限度地达到协商的最优解，并且提高协商的效率，提出将自适应遗传算法AGA应用于基于智能体电子商务的多边多议题同时出价的协商中. 经过1000次实验，标准遗传算法SGA平均需要210次才能达到协商的最优解，而AGA平均仅需要187次. 这表明，在基于智能体的电子商务中，自适应遗传算法可以使多边多议题协商中的智能体高效达到协商的最优解.

关键词: 电子商务, 多边多议题协商, 自适应遗传算法

Abstract:

To make the negotiation agents gain optimal result and more efficiently to negotiate in multi-agent based e-commerce, an adaptive genetic algorithm is presented and applied in multi-lateral multi-issue simultaneous bidding negotiations. 1000 times of experiments show that, for satisfying results, the adaptive genetic algorithm averagely needs 210 times run with contrast to 187 times run of the standard genetic algorithm. So it also shows that the adaptive genetic algorithm can gain the optimal negotiation result more efficiently in multi-literal multi-issue negotiation.

Key words: e-commerce, multi-literal multi-issue negotiation, adaptive genetic algorithm.

中图分类号:

TN929.53

李剑1,2, 景博2, 杨义先1. 自适应遗传算法在多边多议题协商中的应用[J]. 北京邮电大学学报, 2008, 31(6): 67-70.

LI Jian1,2, JING Bo2, YANG Yi-xian1

. Adaptive Genetic Algorithm and Its Application in Multi-lateral Multi-issue Negotiation[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2008, 31(6): 67-70.

参考文献

[1] 苏安洋. 电子商务中的网上协商探索[J]. 北京邮电大学学报, 2003, 26 (Sup): 95-99. Su Anyang. Bargaining exploration in electronic business[J]. Journal of Beijing University of Posts and Telecommunications, 2003, 26(Sup): 95-99.
[2] 孙文辉, 刘峰, 张俊温, 等. 面向电子商务的服务等级协议研究[J]. 北京邮电大学学报, 2004, 27(Sup): 69-73. Sun Wenhui, Liu Feng, Zhang Junwen, et al. Research on E2Commerce oriented service level agreements[J]. Journal of Beijing University of Posts and Telecommunications, 2004, 27 (Sup): 69-73.
[3] 梁茹冰. 基于资源的多agent多议题协商模型研究与设计[J]. 系统技术, 2006, 2(2): 56-58. Liang Rubing. Research and design of multi-agent multi-issue negotiation model based on resource[J]. Financial Computer of HuaNan, 2006, 2(2): 56-58.
[4] 王立春, 陈世福. 多Agent多问题协商模型[J]. 软件学报, 2004, 13(8): 1637-1643. Wang Lichun, Chen Shifu. A multi-agent multi-issue negotiation model[J]. Journal of Software, 2004, 13(8): 1637-1643.
[5] Wang Yan, Lin Kwei Jay. Reputation-oriented trustworthy computing in e-commerce environments[J]. IEEE Internet Computing, 2008, 12(4): 55-59.
[6] Pittayachawan S, Singh M, Corbitt B. A multitheoretical approach for solving trust problems in B2C e-commerce[J]. International Journal of Networking and Virtual Organizations, 2008, 5(3): 369-395.
[7] 翁鸣, 梁俊斌, 苏德富. 基于遗传算法的多边多议题自动协商模型[J]. 计算机工程, 2005, 31(16): 154-156. Weng Ming, Liang Junbin, Su Defu. Many-to-many multi-issue automated negotiation model based on genetic algorithm[J]. Computer Engineering, 2005, 31(16): 154-156.
[8] 陈国良, 王煦法, 庄镇泉, 等. 遗传算法及应用[M]. 北京: 人民邮电出版社, 1996. Chen Guoliang, Wang Jongfa, Zhuang Zhenquan, et al. Genetic algorithm and its application[M]. Beijing: Pepole's Posts and Telecommunications Publishing House, 1996.
[9] 陈利兵, 张阳安, 黄永清, 等. 基于遗传算法的静态逻辑拓扑设计[J]. 北京邮电大学学报, 2006, 29(1): 26-29. Chen Libing, Zhang Yangan, Huang Yongqing, et al. Static virtual topology design using genetic algorithm[J]. Journal of Beijing University of Posts and Telecommunications, 2006, 29(1): 26-29.
[10] 郑岩, 黄荣怀, 战晓苏, 等. 基于遗传算法的动态模糊聚类[J]. 北京邮电大学学报, 2005, 28(1): 75-78. Zheng Yan, Huang Ronghuai, Zhan Xiaosu, et al. Dynamic fuzzy clustering method based on genetic algorithm[J]. Journal of Beijing University of Posts and Telecommunications, 2005, 28(1): 75-78.

[1]	蔚承英, 吴兰, 刘焕淋, 陈勇. 编码链路最少的多源光组播路由[J]. 北京邮电大学学报, 2017, 40(5): 87-91.
[2]	修佳鹏, 陈晨, 杨正球, 刘辰. 电动汽车加电站电池配送路径优化[J]. 北京邮电大学学报, 2014, 37(3): 120-124.
[3]	彭艳斌艾解清李吉明. 对手偏好主动学习驱动的协商框架[J]. 北京邮电大学学报, 2012, 35(5): 22-25.
[4]	庄家俊刘琼. 自适应遗传优化BP网络的研究与应用[J]. 北京邮电大学学报, 2012, 35(5): 41-45.
[5]	李剑, 牛少彰. 学习机制在电子商务中的应用[J]. 北京邮电大学学报, 2009, 32(3): 1-4.
[6]	李剑牛少彰. 一种基于混合遗传算法的双边多议题协商[J]. 北京邮电大学学报, 2009, 32(2): 1-4.
[7]	朱振荣, 李晖, 杨义先. 一种改进的基于账户的安全电子支付协议[J]. 北京邮电大学学报, 2006, 29(s2): 155-159.
[8]	苏安洋. 电子商务中的网上协商探索[J]. 北京邮电大学学报, 2003, 26(S1): 95-98.
[9]	张泽莉, 马华东. 简单网络支付协议的形式模型[J]. 北京邮电大学学报, 2002, 25(4): 42-46.
[10]	李明柱, 李志江, 杨义先. 微电子彩票支付研究[J]. 北京邮电大学学报, 2002, 25(3): 27-30.
[11]	刘杰,王春萌,范春晓. 移动电子商务及WPKI技术移动电子商务及WPKI技术[J]. 北京邮电大学学报, 2002, 25(2): 1-7.

自适应遗传算法在多边多议题协商中的应用

Adaptive Genetic Algorithm and Its Application in Multi-lateral Multi-issue Negotiation

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 11

编辑推荐

Metrics

本文评价