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一个协作的神经动力学优化方法分布式纳什平衡寻找多集群游戏与非凸函数的搜索
IEEE transactions on cybernetics
|July 19, 2023
概括
本研究介绍了一种协作神经动力学优化 (CNO) 方法,用于在复杂游戏中找到通用纳什平衡 (GNE). 该方法使用神经网络进行高效的分布式搜索,在模拟的电力市场场景中证明有效.
科学领域:
- 人工智能的人工智能
- 游戏理论 游戏理论
- 优化优化 优化优化
背景情况:
- 分布式优化问题往往涉及复杂的,非凸函数.
- 寻找通用纳什平衡 (GNEs) 对于分析多代理系统至关重要.
- 现有的方法可能在分散的环境中难以实现可扩展性和融合.
研究的目的:
- 提出一种新的协作神经动力学优化 (CNO) 方法.
- 为了使在具有非凸函数的多集群游戏中分布式搜索GNE.
- 用电力市场价格竞标示例来证明该方法的有效性.
主要方法:
- 基于增强的拉格朗基函数用于本地GNE搜索的投影神经网络的开发.
- 对投影神经网络到局部GNE的收证明.
- 制定一个全局优化问题,并采用CNO方法,使用循环神经网络和元启发式重新启动来进行全局搜索.
主要成果:
- 投影神经网络被证明会汇聚到一个本地GNE.
- CNO方法有效地执行分散搜索和状态重新启动.
- 拟议的方法在实际电力市场价格竞标问题中证明了可行性.
结论:
- CNO方法为寻求具有挑战性的游戏设置的分布式GNE提供了强大的方法.
- 神经网络和元启发学的整合提高了搜索效率和质量.
- 该方法通过相关的经济应用得到验证,表明其实际可用性.
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