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Genetic Algorithm Optimization Of Experiment Design For Targeted Uncertainty Reduction, Alexander Amedeo Depillis
Genetic Algorithm Optimization Of Experiment Design For Targeted Uncertainty Reduction, Alexander Amedeo Depillis
Masters Theses
Nuclear cross sections are a set of parameters that capture probability information about various nuclear reactions. Nuclear cross section data must be experimentally measured, and this results in simulations with nuclear data-induced uncertainties on simulation outputs. This nuclear data-induced uncertainty on most parameters of interest can be reduced by adjusting the nuclear data based on the results from an experiment. Integral nuclear experiments are experiments where the results are related to many different cross sections. Nuclear data may be adjusted to have less uncertainty by adjusting them to match the results obtained from integral experiments. Different integral experiments will adjust …
Research On Unmanned Swarm Combat System Adaptive Evolution Model Simulation, Zhiqiang Li, Yuanlong Li, Laixiang Yin, Xiangping Ma
Research On Unmanned Swarm Combat System Adaptive Evolution Model Simulation, Zhiqiang Li, Yuanlong Li, Laixiang Yin, Xiangping Ma
Journal of System Simulation
Abstract: Aiming at the fact that the intelligent unmanned swarm combat system is mainly composed of large-scale combat individuals with limited behavioral capabilities and has limited ability to adapt to the changes of battlefield environment and combat opponents, a learning evolution method combining genetic algorithm and reinforcement learning is proposed to construct an individual-based unmanned bee colony combat system evolution model. To improve the adaptive evolution efficiency of bee colony combat system, an improved genetic algorithm is proposed to improve the learning and evolution speed of bee colony individuals by using individual-specific mutation optimization strategy. Simulation experiment on …
Research And Simulation On Control Algorithm For Multi-Objective Optimization Of Urban Rail Train, Jianjun Meng, Minggao Pei, Wu Fu, Tengzhou Wei, Hao Shuai
Research And Simulation On Control Algorithm For Multi-Objective Optimization Of Urban Rail Train, Jianjun Meng, Minggao Pei, Wu Fu, Tengzhou Wei, Hao Shuai
Journal of System Simulation
Abstract: According to the characteristics of urban rail train running multiple objective, the multi-objective operation model for urban rail train was established with the energy consumption, punctuality, accurate parking and comfort level as the optimization indexes. Genetic algorithms was used to optimize running multi-objective model of urban rail train, and according to train traction calculation and computer simulation, the train running target curve was obtained. The fuzzy control and PID control algorithm were applied to urban rail train system to establish adaptive fuzzy PID controller and PID control in order to track the target curve. Simulation results show that adaptive …
Implementation Of Multivariate Artificial Neural Networks Coupled With Genetic Algorithms For The Multi-Objective Property Prediction And Optimization Of Emulsion Polymers, David Chisholm
Master's Theses
Machine learning has been gaining popularity over the past few decades as computers have become more advanced. On a fundamental level, machine learning consists of the use of computerized statistical methods to analyze data and discover trends that may not have been obvious or otherwise observable previously. These trends can then be used to make predictions on new data and explore entirely new design spaces. Methods vary from simple linear regression to highly complex neural networks, but the end goal is similar. The application of these methods to material property prediction and new material discovery has been of high interest …
Rvm Soft Sensing Model Based On Optimized Combined Kernel Function, Yanan Zhang, Huizhong Yang
Rvm Soft Sensing Model Based On Optimized Combined Kernel Function, Yanan Zhang, Huizhong Yang
Journal of System Simulation
Abstract: An RVM spft sensingmodeling method based onthe optimizedcombined kernel functionis proposed.In order to simultaneously get better prediction and sparsity, a fitness function synthesizing regression accuracy and sparsity is created while constructing a combined kernel functionfor RVM.The genetic algorithm is used to optimize the weights and kernel parametersof the RVMcombined kernel.The proposed method is used totomodela cleavage-recovery unit in the production process of Bisphenol-A.The results show that it can guarantee better sparsity andregression accuracy than the general SVM combinedkernel model andGA-RVM single kernel model.
A Genetic Algorithm For Cellular Manufacturing Design And Layout, Xiaodan Wu, Chao-Hsien Chu, Yunfeng Wang, Weili Yan
A Genetic Algorithm For Cellular Manufacturing Design And Layout, Xiaodan Wu, Chao-Hsien Chu, Yunfeng Wang, Weili Yan
Research Collection School Of Computing and Information Systems
Cellular manufacturing (CM) is an approach that can be used to enhance both flexibility and efficiency in today’s small-to-medium lot production environment. The design of a CM system (CMS) often involves three major decisions: cell formation, group layout, and group schedule. Ideally, these decisions should be addressed simultaneously in order to obtain the best results. However, due to the complexity and NP-complete nature of each decision and the limitations of traditional approaches, most researchers have only addressed these decisions sequentially or independently. In this study, a hierarchical genetic algorithm is developed to simultaneously form manufacturing cells and determine the group …
Explicit Building Block Multiobjective Evolutionary Computation: Methods And Applications, Richard O. Day
Explicit Building Block Multiobjective Evolutionary Computation: Methods And Applications, Richard O. Day
Theses and Dissertations
This dissertation presents principles, techniques, and performance of evolutionary computation optimization methods. Concentration is on concepts, design formulation, and prescription for multiobjective problem solving and explicit building block (BB) multiobjective evolutionary algorithms (MOEAs). Current state-of-the-art explicit BB MOEAs are addressed in the innovative design, execution, and testing of a new multiobjective explicit BB MOEA. Evolutionary computation concepts examined are algorithm convergence, population diversity and sizing, genotype and phenotype partitioning, archiving, BB concepts, parallel evolutionary algorithm (EA) models, robustness, visualization of evolutionary process, and performance in terms of effectiveness and efficiency. The main result of this research is the development of …