CPS: Breakthrough: Toward Revolutionary Algorithms for Cyber-Physical Systems Architecture Optimization

未来网络物理系统面临的挑战之一是探索大型设计空间。进化算法体现了自然进化中变异和选择机制的简化计算模型，被认为是进行设计优化的有效方法。然而，传统的公式仅限于为给定的固定体系结构内的一组预定参数选择值。该项目基于隐藏基因的思想探索技术，使EA能够选择数量可变的组件，从而扩展所探索的设计空间，包括对系统架构的选择。隐藏遗传优化算法在网络物理系统中有着广泛的潜在应用，包括自动化建筑系统、交通系统、微电网系统和空间系统。该项目将教育和研究结合在一起，让从高中到研究生的学生参与与他们的技能相称的活动，并通过开放源码分发算法实现代码和参与全球轨道优化竞赛来促进研究成果的传播。该项目不是在当前的EA中使用单层编码来表示系统的变量，而是研究增加第二层编码，以便在搜索最佳系统的体系结构时能够根据需要隐藏一些变量。这种遗传隐藏概念存在于自然界中，它提供了一种自然的方式来处理覆盖设计空间中各种不同大小的系统架构。此外，EAs中的标准突变和选择操作将被旨在提取隐藏基因模型的全部潜力的新操作所取代。具体应用包括空间任务设计、微电网优化、交通网络信号协调规划等。

One of the challenges for the future cyber-physical systems is the exploration of large design spaces. Evolutionary algorithms (EAs), which embody a simplified computational model of the mutation and selection mechanisms of natural evolution, are known to be effective for design optimization. However, the traditional formulations are limited to choosing values for a predetermined set of parameters within a given fixed architecture. This project explores techniques, based on the idea of hidden genes, which enable EAs to select a variable number of components, thereby expanding the explored design space to include selection of a system's architecture. Hidden genetic optimization algorithms have a broad range of potential applications in cyber-physical systems, including automated construction systems, transportation systems, micro-grid systems, and space systems. The project integrates education with research by involving students ranging from high school through graduate school in activities commensurate with their skills, and promotes dissemination of the research results through open source distribution of algorithm implementation code and participation in the worldwide Global Trajectory Optimization Competition.Instead of using a single layer of coding to represent the variables of the system in current EAs, this project investigates adding a second layer of coding to enable hiding some of the variables, as needed, during the search for the optimal system's architecture. This genetic hiding concept is found in nature and provides a natural way of handling system architectures covering a range of different sizes in the design space. In addition, the standard mutation and selection operations in EAs will be replaced by new operations that are intended to extract the full potential of the hidden gene model. Specific applications include space mission design, microgrid optimization, and traffic network signal coordinated planning.