计算机科学与探索2016，Vol.10Issue(4)：565-572,8.DOI:10.3778/j.issn.1673-9418.1507080
基于粒子群算法的粗糙博弈模型与算法设计
Rough Game Model and Algorithm Design Based on Particle Swarm Optimization
摘要
Abstract
There is at least one mixed strategy Nash equilibrium in continuous game, but there are little related research results in existing literature about infinite mixed strategy Nash equilibrium and uncertainty game problem. The uncer-tainty game refers to the game equilibrium problem that the players’strategy set or benefit function are uncertainty. This paper creates an approximation algorithm of infinitely mixed strategy Nash equilibrium, using the advantages of particle swarm optimization, fewer parameters, simple coding and not strictly required to objective function. This paper also proposes the concept of rough game theory, and gives a method converting rough game to a classic game theory based on rough set and vague set theory. This paper provides a theoretical basis for game problem when the strategy sets and benefit function problem are fuzzy. The examples show that the approximation algorithm of infinite mixed strategy Nash equilibrium based on improved particle swarm algorithm and rough game theory solution are feasible and effective.关键词
粗糙集/粒子群算法/混合策略纳什均衡/Vague集Key words
rough set/particle swarm optimization/mixed strategy Nash equilibrium/Vague set分类
数理科学引用本文复制引用
曹黎侠,黄光球..基于粒子群算法的粗糙博弈模型与算法设计[J].计算机科学与探索,2016,10(4):565-572,8.基金项目
The Natural Science Basic Research Program of Shaanxi Province under Grant No.2015JZ010(陕西省自然科学基础研究计划) （陕西省自然科学基础研究计划）
the Social Science Foundation of Shaanxi Province under Grant No.2014P07(陕西省社会科学基金) （陕西省社会科学基金）
the Science&Technology Asso-ciation Decision-Making Advisory Issue of Xi’an under Grant No.201517(西安市科协决策咨询课题) （西安市科协决策咨询课题）
the Fund Project of Xi’an Technological University under Grant No. XAGDXJJ1324(西安工业大学校长基金项目) （西安工业大学校长基金项目）
