A Negotiation Based on Improved Genetic Algorithm and Its Application in Competitive E-commerce

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A Negotiation Based on Improved Genetic Algorithm and Its Application in Competitive E-commerce

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- A Negotiation Based on Improved Genetic Algorithm and Its Application in Competitive E-commerce
- Acta Scientiarum Naturalium Universitatis SunYatseni Vol. 50, Issue 1, Pages: 18-22(2011)
- 作者机构：
  
  1. ．北京邮电大学网络与交换国家重点实验室,北京,100876
  2. 2．北京理工大学软件学院,北京,100081
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2011，
  
  Published Online：25 January 2011，
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A Negotiation Based on Improved Genetic Algorithm and Its Application in Competitive E-commerce. [J]. Acta Scientiarum Naturalium Universitatis SunYatseni 50(1):18-22(2011)
DOI：

A Negotiation Based on Improved Genetic Algorithm and Its Application in Competitive E-commerce. [J]. Acta Scientiarum Naturalium Universitatis SunYatseni 50(1):18-22(2011) DOI：

摘要

为了提高竞争环境下基于智能体电子商务双边多议题协商当中agent协商的效率，提出了一种竞争环境下agent的协商模型，并且将改进的遗传算法IGA应用于这种模型当中，来提高模型中agent协商的效率。在相同实验条件下，分别对四种遗传算法各进行了1000次的实验，结果表明同样达到协商满意解的时候，标准遗传算法SGA平均需要360次协商，基于Metropolis准则的遗传算法MGA平均需要230次协商，自适应遗传算法AGA平均需要207次协商，而IGA平均仅需要151次协商。这个结果说明，在求解竞争环境下双边多议题协商问题的时候，改进的遗传算法IGA可以使得协商当中的agent高效达到协商的满意解。

Abstract

To make the agents negotiate more efficiently in bilateral multi-issue negotiation in multi-agent based competitive e-commerce

an agent negotiation model in competitive environment is presented

and the Improved Genetic Algorithm (IGA) is applied in the model to enhance the negotiation efficiency. After 1000 times of experiments for the four kinds of agents in the same condition

the Standard Genetic Algorithm (SGA) averagely needs negotiation of 360 runs

Genetic Algorithm based on Metropolis rule (MGA) averagely needs 230 runs

Adaptive Genetic Algorithm AGA averagely needs 207 runs

while the IGA averagely needs only 151 runs. The experiment results show that the IGA can gain the optimal negotiation result more efficiently than other three kinds of genetic algorithms in competitive bilateral multi-issue negotiation.

关键词

电子商务双边多议题协商改进的遗传算法

Keywords

E-commercebilateral multi-issue negotiationimproved genetic algorithm

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Related Author

LI Qingxia

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Related Institution

Department of Computer and Information Science, City College of Dongguan University of Technology

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