合作博弈的连续蚁群算法求解  

Ant Colony Optimization for Continuous Domains Applied to Cooperative Game

作　　者：李壮阔[1] 常凯旋 LI Zhuangkuo;CHANG Kaixuan(School of Business,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China)

机构地区：[1]桂林电子科技大学商学院,广西桂林541004

出　　处：《计算机工程与应用》2021年第24期198-204,共7页Computer Engineering and Applications

基　　金：桂林电子科技大学研究生教育创新计划(2019YCXS061,2020YCXS064)。

摘　　要：在合作博弈的理论研究中,经典的合作博弈解概念在求解过程中没有体现出局中人的有限理性和互动博弈行为。而在现实博弈环境中,联盟的分配方案更多是通过局中人间理性互动与策略博弈形成的。引入理性因子和控制因子来描述局中人在博弈过程中的决策行为,建立了考虑互动行为的合作博弈模型,并利用连续蚁群算法对合作博弈进行求解。算例表明该解法可以保证分配方案满足有效性和个体理性,并能快速得到联盟的唯一分配方案。这为合作博弈的求解提供了新的思路与工具。In the theoretical research of cooperative games,the classic solution concept of cooperative game does not embody the limited rationality and interactional game-playing behavior of the player in the solution process.In the real world,the allocation schemes are often formed through the player’s rational interaction and strategic changes.This paper introduces rational factors and control factors to describe the players’decision-making behaviors during the game,establishes a cooperative game model considering interaction behavior,and uses ant colony optimization for continuous domains to solve the cooperative game.An example shows that this method can ensure the allocation scheme satisfies the effective-ness and the individual rationality,and can quickly obtain the only allocation scheme of the alliance.It provides a new idea and tool for solving cooperative games.

关 键 词：合作博弈 互动行为 谈判 连续蚁群算法 

分 类 号：F224.32[经济管理—国民经济]