根据贪的算法和估计的分配算法最大化纳什社会福利
Weizhi Liao1, Youzhen Jin1, Zijia Wang2
1School of Information Science and Engineering, Jiaxing University, Jiaxing 314001, China.
纳什社会福利 (NSW) 的最大化,一个APX难题,是通过两种新方法来解决的. 与邻里搜索的分布算法估计 (EDA) 与一般贪算法 (GA) 相比显示出优异的结果.
科学领域:
- 计算机科学 计算机科学
- 经济学 经济学 经济学
- 运营研究 运营研究
背景情况:
- 纳什社会福利 (NSW) 问题在经济学和计算机科学中至关重要.
- 最大化NSW在计算上具有挑战性,被归类为APX-hard问题.
研究的目的:
- 提出和评估新的算法,以提高纳什社会福利最大化.
- 解决新南威尔士州对具有相同和不同估值的代理人的问题.
主要方法:
- 开发一种适用于各种代理人估值类型的一般贪算法 (GA).
- 介绍了一种创新的进化方法,将估计分布算法 (EDA) 与邻里搜索集成.
- 在不同实例上实证实施和对拟议的GA和EDA方法进行比较.
主要成果:
- 该GA与现有方法对同一代理商的估值保持一致.
- 基于EDA的方法在接近最大纳什社会福利方面表现出了卓越的表现.
- 实验结果证实了EDA对GA在NSW最大化方面的有效性.
结论:
- 拟议的EDA与邻里搜索提供了一个更有效的策略,以最大限度地提高纳什的社会福利.
- 这项研究推进了多代理系统中福利最大化问题的计算方法.
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