• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Parallel Distributed Processing Symposium >Using Physical Stigmergy in Decentralized Optimization under Multiple Non-separable Constraints: Formal Methods and an Intelligent Lighting Example
【24h】

Using Physical Stigmergy in Decentralized Optimization under Multiple Non-separable Constraints: Formal Methods and an Intelligent Lighting Example

机译：在多个不可分约束下的分散优化中使用物理电能像差：形式化方法和智能照明示例

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, a distributed asynchronous algorithm for intelligent lighting is presented that minimizes collective power use while meeting multiple user lighting constraints simultaneously and requires very little communication among agents participating in the distributed computation. Consequently, the approach is arbitrarily scalable, adapts to exogenous disturbances, and is robust to failures of individual agents. This algorithm is an example of a decentralized primal-space algorithm for constrained non-linear optimization that achieves coordination between agents using stigmergic memory cues present in the physical system as opposed to explicit communication and synchronization. Not only does this work make of stigmergy, a property first used to describe decentralized decision making in eusocial insects, but details of the algorithm are inspired by classic social foraging theory and more recent results in eusocial-insect macronutrient regulation. This theoretical analysis in this paper guarantees that the decentralized stigmergically coupled system converges to within a finite neighborhood of the optimal resource allocation. These results are validated using a hardware implementation of the algorithm in a small-scale intelligent lighting scenario. There are other real-time distributed resource allocation applications that are amenable to these methods, like distributed power generation, in general, this paper means to provide proof of concept that physical variables in cyberphysical systems can be leveraged to reduce the communication burden of algorithms.
机译：在本文中，提出了一种用于智能照明的分布式异步算法，该算法可在满足多个用户照明约束的同时最大程度地减少集体用电，并且参与分布式计算的座席之间几乎不需要通信。因此，该方法可任意扩展，适应外部干扰，并且对单个代理的故障具有鲁棒性。此算法是用于约束非线性优化的分散原始空间算法的示例，该算法使用存在于物理系统中的斯蒂格曼记忆线索（与显式通信和同步相反）来实现代理之间的协调。这项工作不仅利用了首先用于描述正常社会昆虫的分散决策的特性-斯蒂格曼能，而且算法的细节受到了经典的社会觅食理论和最近对正常社会昆虫宏观营养素调控的研究结果的启发。本文中的这一理论分析保证了分散的斯蒂格默耦合系统收敛到最优资源分配的有限邻域内。这些结果在小规模智能照明场景中使用算法的硬件实现进行了验证。通常，还有其他适用于这些方法的实时分布式资源分配应用程序，例如分布式发电，本文旨在提供概念证明，可以利用电子物理系统中的物理变量来减轻算法的通信负担。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号