摘要
在传统的合作博弈求解中,通常假设联盟收益确定或者局中人对联盟收益取值意见一致.现实中,联盟收益往往不确定,局中人对联盟收益取值意见不一致,且联盟分配方案的达成通常是局中人基于个体理性与判断进行多轮谈判,互相影响、相互妥协、最终趋同的结果.针对这种情况,本文首先对联盟收益不确定时局中人的收益进行描述,建立合作博弈的扩展模型,再考虑局中人的理性互动与策略博弈,借鉴群智能的建模思想和求解思路,利用多目标粒子群扩展算法对模型进行求解.本文对于联盟收益不确定时合作博弈的求解提供了新的思路与方法.
In traditional solution of cooperative game,it is usually assumed that alliance income is determined or the players agree on the value of alliance income.In reality,the income of alliance is often uncertain and the opinions of the players on the value of alliance income are inconsistent.In addition,alliance allocation schemes are usually reached after rounds of negotiation and consultation based on individual rationality and judgment.In this case,this paper first describes the benefits of the players when alliance income is uncertain,and establishes the extended model of the cooperative game.Then,considering the rational interaction and strategy game of the players,using the modeling ideas and solving ideas of group intelligence,the model is solved by the muti-objective particle swarm optimization algorithm.This paper provides new ideas and methods for solving the cooperative game in uncertainty alliance income.
作者
李壮阔
陈水鹏
LI Zhuang-kuo;CHEN Shui-peng(School of Business,Guilin University of Electronic Technology,Guilin 541004,China)
出处
《数学的实践与认识》
北大核心
2020年第13期25-37,共13页
Mathematics in Practice and Theory
基金
广西科技计划项目(桂科AB17205004)
桂林电子科技大学研究生优秀学位论文培育项目(17YJPYSS19)
桂林电子科技大学研究生教育创新计划项目(2019YCXS061)。
关键词
合作博弈
不确定性
谈判
多目标粒子群扩展算法
cooperative game
uncertainty
negotiation
muti-objective particle swarm optimization
