Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 30 for “"Non-Dominated Sorting Genetic Algorithm"”.
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Multi-Objective Optimization Framework for Generation Expansion Planning Under Transmission Constraints
… for Power System Analysis modeling tool with the Non-Dominated Sorting Genetic Algorithm II for multi-objective optimization. The developed framework is applied to the Western Cape electricity network as a case study to evaluate its performance and practicality in a real-world regional context. …
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Optimization of residential buildings and renewable energy integration in small island developing states: the Bahamas as a case study
… the platform for this study, implementing the non-dominated sorting genetic algorithm II (NSGA-II) for optimization. Optimal solutions are compared to a building model developed from audited data. The results indicate that design alternatives presented here can be feasibly implemented that …
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Evaluation of optimal control-based deformable registration model
… quality measures as performance indices, the non-dominated sorting genetic algorithm (NSGA-II) is applied to approximate the Pareto fronts for each model to facilitate proper evaluation. The Pareto fronts are also visualized using level diagrams analysis.
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Sustainable Closed-Loop Supply Chaing Network Design
… via Mixed Integer Linear Programming (MILP), and nondeterministically via Fuzzy Multi-objective Mixed Integer Linear Programming (FMOMILP) model, by considering sustainability and uncertainty. Fuzzy programming approaches were utilized to solve the problem. Two multi-objective evolutionary …
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A Hybrid Multi-Objective Evolutionary Algorithm for Wind-Turbine Blade Optimization
A concurrent-hybrid non-dominated sorting genetic algorithm II (hybrid NSGA-II) has been developed and applied to the simultaneous optimization of the annual energy production, flapwise root-bending moment and mass of the National Renewable Energy Laboratory's (NREL) 5MW wind-turbine blade. To …
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Optimizing COVID-19 control measures using multi-objective deep reinforcement learning
… of global research is the hunt for efficient non-pharmaceutical methods to stop the spread of diseases. Recent research has shown that reinforcement learning can be a helpful tool in the medical industry to ad- dress challenging and delicate issues. The goal of this study is to improve …
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Optimal Volt/VAR Control and Power Dispatch in Active Distribution Systems
… statements such as Multi-Objective Fireworks Algorithm (MOFWA) and Pareto Concavity Elimination Transformation (PaCcET). Furthermore, we propose constraint value (CV) metric and sensitivity children (SC) to handle the distribution system constraints and use the sensitivity respond for …
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Modelling, simulation and multi-objective optimization of industrial hydrocrackers
… and robust evolutionary techniques like Genetic Algorithm (GA), have led to a revolution in the field of multi-objective optimization (MOO). This work describes modeling of industrial hydrocrackers through first principles, data based and hybrid modeling techniques followed by its …
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Projeto de redes ópticas de alta capacidade utilizando técnicas de otimização bioinspiradas
… Particle Swarm Optimization (PSO) e Non-dominated Sorting Genetic Algorithm II (NSGA-II)
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A Genetic Algorithm Framework using Variable Length Chromosomes for Vehicle Maneuver Planning
… an operational campaign. This thesis presents a genetic algorithm framework with Variable Length Chromosomes (VLC) to find this optimal set of maneuvers. Said framework generates Pareto optimal sets of maneuvers using non-dominated sorting genetic algorithm II (NSGA-II). The use of VLC removes …
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Ant Colony Optimisation for Dynamic and Dynamic Multi-objective Railway Rescheduling Problems
… and an investigation into the application of ACO algorithms to solve those problems. A further contribution is the development of a unique two-colony ACO framework to solve the separate problems of platform reallocation and train resequencing at a UK railway station in dynamic delay scenarios. …
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Parallelization of hybrid multi-objective evolutionary algorithm on multi-core architectures
… optimal front. Multi-objective evolutionary algorithms are heuristics that evolve a population of candidate solutions to find the Pareto optimal front in a single run. The selection criterion used to select individuals in the population play an important role in determining the quality of the …
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Design problem optimization with multi-objective evolutionary algorithms
… two advanced multi-objective evolutionary algorithms—Multi-Objective Covariance Matrix Adaptation Evolution Strategy (MOCMA-ES) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II)—to optimize sunshades across five key objectives: thermal comfort, energy consumption, Useful Daylight …
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Enhanced Pump Schedule Optimization For Large Water Distribution Networks To Maximize Environmental And Economic Benefits
… metaheuristic and evolutionary techniques (e.g. Genetic Algorithm) make some commercial and research tools able to optimize the electricity cost of small water distribution systems (WDS). Still reducing the environmental footprint of these systems and dealing with large and complicated water …
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Network disaster recovery via dynamic service-aware risk management
… conditions as a multi-objective mixed-integer nonlinear program (MINLP). The model jointly optimizes five objectives: maximizing revenue and survivability, while minimizing service-aware risk, overutilization cost, and SLA penalties, subject to capacity and service-level constraints. A dynamic …
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A fast non-dominated sorting guided genetic algorithm for multi-objective power distribution system reconfiguration problem
… operations (outages). DSR is a multi-objective, non-linear problem. A new, fast, non-dominated sorting genetic algorithm (FNSGA) is introduced for solving the DSR problem in normal operation by satisfying all objectives simultaneously with a relatively small numbers of population size and …
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Application of evolutionary computation to open channel flow modelling
… with evaluating the ability of an evolutionary algorithm to provide insight and guidance into the correct magnitude and trend of the three parameters required in order to successfully apply a quasi 2D depth averaged Reynolds Averaged Navier Stokes (RANS) model to the flow in prismatic open …
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Multi-objective Optimization in Traffic Signal Control
… time-consuming. Multi-objective Evolutionary Algorithms (MOEAs) are in many ways superior to traditional search methods. They have been widely utilized in traffic signal optimization problems. However, running MOEAs on traffic optimization problems using microscopic traffic simulators to …
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DESIGNING WATERSHED-SCALE STRUCTURAL BEST MANAGEMENT PRACTICES USING EVOLUTIONARY ALGORITHMS TO ACHIEVE WATER QUALITY GOALS
… sources of water pollution: point sources and non-point sources, which are differentiated based on their mode of generation. Pollution generated from point sources has been effectively controlled by the implementation of the National Pollution Discharge Elimination System (NPDES) program, under …
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Improved genetic algorithm for distribution system performance analysis by taking advantage of essential spanning trees
… automation tools. One such instrument, the Fast Non-Dominated Sorting Genetic Algorithm (FNSGA), has been shown to be very effective at finding Pareto optimal radial distribution systems that are optimized with respect to voltages, currents, and power losses. However, the original FNSGA is …
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