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Showing 1 to 14 of 14 for “"Metaheuristic Optimization"”.
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Control Hierarchies for Critical Infrastructures in Smart Grid Using Reinforcement Learning and Metaheuristic Optimization
<p>The objective of this work is to develop robust control framework for interdependent smart grid infrastructures comprising two critical infrastructures: 1) power distribution networks that are characterized by high penetration of distributed energy resources (DERs), and 2) DC-rail transportation …
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Metaheuristic Optimization for Automatic Arrangement of Power Electronics Components in a Shipboard Electrical Distribution System
This thesis proposes a novel methodology for the automatic placement of Power Electronics Building Blocks (PEBBs) in modular, integrated power corridor designs. These building blocks, which are created and tested offsite for a variety of applications, are currently placed manually during the design …
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Toward enhancing metaheuristic optimization algorithms using center-based sampling strategies for solving single- and multi- objective large-scale problems
Over the last decade, metaheuristic algorithms have become well-established approaches utilized for solving complex real-world optimization problems. Most metaheuristic algorithms have used stochastic strategies in their initialization as well as during the new candidate solution generation process …
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Mission-driven Sensor Network Design for Space Domain Awareness
… space object population. By employing advanced metaheuristic optimization techniques and high-fidelity modeling and simulation, this research investigates the intricate interplay between sensor characteristics, network topology, and state estimation performance. The research aims to develop …
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Remote sensing of dynamics and aboveground biomass of seagrass in Tauranga Harbor (Bay of Plenty), New Zealand
… of machine learning (ML) and meta-heuristic optimization algorithms, to develop novel and advanced techniques for remote sensing of seagrass. The work used field validation data from Tauranga Harbor, New Zealand, and specifically targeted mapping, change detection, and estimation of seagrass …
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Multiprotocol label switching network optimization by metaheuristic algorithms
… subject to multiple constraints (MCOP) optimization problem in the field of optimization, which is considered as a computationally complex optimization problem.;Metaheuristic optimization algorithms have raised as a mainstream approach for solving MCOP based complex optimization …
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Stochastic Cellular Manufacturing System Design and Control
… (CMS) performance are addressed and stochastic optimization approaches are developed and applied to ten case problems from industrial companies and cellular manufacturing literature. This dissertation consists of mainly three phases, namely: stochastic CMS design, stochastic CMS control and the …
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Designing Power Converter-Based Energy Management Systems with a Hierarchical Optimization Method
This dissertation introduces a hierarchical optimization framework for power converter-based energy management systems, with a primary focus on weight minimization. Emphasizing modularity and scalability, the research systematically tackles the challenges in optimizing these systems, addressing …
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Model-agnostic Methodology for High-Altitude Balloon Mission Planning
… the model-agnostic state equations, a metaheuristic optimization mission planning tool was applied to evaluate the resiliency of a degraded LEO Walker-Delta satellite constellation. One study explores the trends in Walker-Delta design parameters that impact constellation coverage …
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A Coordinated Voltage Management Method Utilizing Battery Energy Storage Systems and Smart PV Inverters in Distribution Networks with High PV and Wind Penetrations
… management method is based on a bi-level optimization algorithm consisting of upper and lower optimization levels. The proposed method determines the optimal location, capacity, numbers and BESS charging and discharging rates to support the distribution system voltage and to ensure optimal …
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Deep Reinforcement Learning-Based Approaches for the MPPT Control of Standalone Solar PV Systems
… GMPP when partial shading is present. Recently, metaheuristic optimization algorithms, including particle swarm optimization (PSO), genetic algorithm (GA), etc., have been used to deal with the limitations of classical methods. The PSO and GA can track the solar PV MPP under uniform irradiation …
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Data-Driven Pipeline for Learning Discrete behavioural Models of Cyber-Physical Systems
… design the pipeline as a nested particle swarm optimization framework guided by a quality measure from the automata learning algorithm of choice. This enables automatic tuning of discretization and learning hyperparameters through non-convex optimization, eliminating the need for manual …
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Advancements in Model Predictive Control for Real-Time Applications With Economic and Conflicting Control Objectives
L'abstract è presente nell'allegato / the abstract is in the attachment