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Classification-based self-adaptive differential evolution and its application in multi-lateral multi-issue negotiation

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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.

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Authors and Affiliations

  1. School of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China

    Xiaojun Bi & Jing Xiao

  2. School of Information Engineering, Liaoning Provincial College of Communications, Shenyang, 110122, China

    Jing Xiao

Authors
  1. Xiaojun Bi
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  2. Jing Xiao
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Correspondence to Jing Xiao.

Additional information

Xiaojun BI graduated from the Harbin Engineering University in 1987 and received her MEng in Radio from the Harbin Institute of Technology in 1990. She then returned to Harbin Engineering University to study for her Doctorate in Signal and Information Processing. She is currently a professor and doctoral supervisor in the College of Information and Communication Engineering, Harbin Engineering University. Her current research interests include intelligent information processing and medical image processing.

Jing XIAO graduated from the Harbin Engineering University in 2007 and received her PhD in Signal and Information Processing from the same University in 2011. She is currently a postdoctoral researcher in the same university and a university lecturer in Liaoning Provincial College of Communications. Her main research interests include intelligent information processing, e-commerce and medical image processing.

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Bi, X., Xiao, J. Classification-based self-adaptive differential evolution and its application in multi-lateral multi-issue negotiation. Front. Comput. Sci. 6, 442–461 (2012). https://doi.org/10.1007/s11704-012-0101-y

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