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| Content Provider | Springer Nature Link |
|---|---|
| Author | Bi, Xiaojun Xiao, Jing |
| Copyright Year | 2012 |
| Abstract | Multi-lateral multi-issue negotiations are the most complex realistic negotiation problems. Automated approaches have proven particularly promising for complex negotiations and previous research indicates evolutionary computation could be useful for such complex systems. To improve the efficiency of realistic multi-lateral multi-issue negotiations and avoid the requirement of complete information about negotiators, a novel negotiation model based on an improved evolutionary algorithm p-ADE is proposed. The new model includes a new multi-agent negotiation protocol and strategy which utilize p-ADE to improve the negotiation efficiency by generating more acceptable solutions with stronger suitability for all the participants. Where p-ADE is improved based on the well-known differential evolution (DE), in which a new classification-based mutation strategy DE/rand-to-best/pbest as well as a dynamic self-adaptive parameter setting strategy are proposed. Experimental results confirm the superiority of p-ADE over several state-of-the-art evolutionary optimizers. In addition, the p-ADE based multiagent negotiation model shows good performance in solving realistic multi-lateral multi-issue negotiations. |
| Starting Page | 442 |
| Ending Page | 461 |
| Page Count | 20 |
| File Format | |
| ISSN | 20952228 |
| Journal | Frontiers of Computer Science in China |
| Volume Number | 6 |
| Issue Number | 4 |
| e-ISSN | 16737466 |
| Language | English |
| Publisher | SP Higher Education Press |
| Publisher Date | 2012-05-07 |
| Publisher Institution | Chinese Universities |
| Publisher Place | Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | differential evolution global optimum e-commerce agent multi-lateral multi-issue negotiation Computer Science |
| Content Type | Text |
| Resource Type | Article |
| Subject | Theoretical Computer Science Computer Science |
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