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

未来网络物理系统的挑战之一是探索大型设计空间。 进化算法（EA），体现了一个简化的计算模型的自然进化的变异和选择机制，是已知的有效的设计优化。 然而，传统的公式仅限于在给定的固定架构内选择预定参数集的值。 这个项目探讨技术，隐藏基因的想法，使EA选择一个变量数量的组件的基础上，从而扩大了探索的设计空间，包括选择一个系统的架构。 隐遗传优化算法在信息物理系统中具有广泛的潜在应用，包括自动化建筑系统、交通系统、微电网系统和空间系统。该项目将教育与研究相结合，让从高中到研究生院的学生参与与其技能相称的活动，并通过开放源码分发算法实现代码和参加全球轨迹优化竞赛来促进研究成果的传播。这个项目研究增加第二层编码，以便在搜索最佳系统架构的过程中根据需要隐藏一些变量。这种遗传隐藏的概念是在自然界中发现的，并提供了一种自然的方式来处理系统架构，覆盖了设计空间中的一系列不同大小。此外，EA中的标准突变和选择操作将被新操作所取代，这些新操作旨在提取隐藏基因模型的全部潜力。 具体应用包括空间使命设计、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.