CPS: Breakthrough: Toward Revolutionary Algorithms for Cyber-Physical Systems Architecture Optimization
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One of the challenges for the future cyber-physical systems is the exploration of large design spaces. Genetic algorithms (GAs), 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 GAs to select a variable number of components, thereby expanding the explored design space to include selection of a system’s architecture.
The concept of hidden genes is introduced in this project in genetic algorithms to enable the search for optimal systems architectures. 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 GAs, this project investigated 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 GAs will be replaced by new operations that are intended to extract the full potential of the hidden gene model.
This new concept of Hidden Genes Genetic Algorithms (HGGA) has been implemented in the following application domains: interplanetary space missions design optimization, optimization of space mission for space debris removal, topology optimization for wave energy converters, and mobile microgrid optimization. The convergence of different mechanisms of HGGA were investigated using Markov models, and it was shown that HGGAs generate a sequence of solutions with the limit value of the global optima.
Submitted by Ossama Abdelkhalik
on
One of the challenges for the future cyber-physical systems is the exploration of large design spaces. Genetic algorithms (GAs), 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 GAs to select a variable number of components, thereby expanding the explored design space to include selection of a system’s architecture.
The concept of hidden genes is introduced in this project in genetic algorithms to enable the search for optimal systems architectures. 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 GAs, this project investigated 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 GAs will be replaced by new operations that are intended to extract the full potential of the hidden gene model.
This new concept of Hidden Genes Genetic Algorithms (HGGA) has been implemented in the following application domains: interplanetary space missions design optimization, optimization of space mission for space debris removal, topology optimization for wave energy converters, and mobile microgrid optimization. The convergence of different mechanisms of HGGA were investigated using Markov models, and it was shown that HGGAs generate a sequence of solutions with the limit value of the global optima.