14 research outputs found

    Large-Scale Network Plan Optimization Using Improved Particle Swarm Optimization Algorithm

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    No relevant reports have been reported on the optimization of a large-scale network plan with more than 200 works due to the complexity of the problem and the huge amount of computation. In this paper, an improved particle swarm optimization algorithm via optimization of initial particle swarm (OIPSO) is first explained by the stochastic processes theory. Then two optimization examples are solved using this method which are the optimization of resource-leveling with fixed duration and the optimization of resources constraints with shortest project duration in a large network plan with 223 works. Through these two examples, under the same number of iterations, it is proven that the improved algorithm (OIPSO) can accelerate the optimization speed and improve the optimization effect of particle swarm optimization (PSO)

    Improvement of Metaheuristic Algorithms Using Symbolic Regression

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    Jelikož na poli optimalizace stále dochází k vývoji a mnohým výzkumům, je cílem této práce nalezení zlepšení metaheuristických algoritmů SOMA, PSO a DE prostřednictvím analytického programování. Proto se tato práce v prvních částech zabývá rozborem těchto metaheuristických algoritmů a jsou zde také popsány principy analytického programování, jež bylo využito jako metoda symbolické regrese. Všechny algoritmy byly posléze implementovány v jazyce C++ pro účely experimentů, jejichž výsledky jsou poté prezentovány a vyhodnoceny v závěrečných částech. Zlepšení optimalizačních schopností se podařilo nalézt především u algoritmu SOMA. U algoritmů PSO a DE došlo ke zlepšení u vybraných testovacích funkcí.Optimization methods are still under development, and researchers are working on the improvement of current methods. The purpose of this thesis is to find an improvement of three metaheuristic algorithms - SOMA, PSO, and DE. The analytic programming is used as a method of symbolic regression for this purpose. The beginning of this thesis consists of descriptions of SOMA, PSO, and DE, as well as of analytic programming. All algorithms were implemented in the C++ programming language and experiments were performed. The results are evaluated at the end of this thesis. Significant improvement was found for the SOMA algorithm. For PSO and DE, improvements were observed for some of the objective functions.460 - Katedra informatikyvýborn

    A Novel Hybrid Algorithm for Optimized Solutions in Ocean Renewable Energy Industry: Enhancing Power Take-Off Parameters and Site Selection Procedure of Wave Energy Converters

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    Ocean renewable energy, particularly wave energy, has emerged as a pivotal component for diversifying the global energy portfolio, reducing dependence on fossil fuels, and mitigating climate change impacts. This study delves into the optimization of power take-off (PTO) parameters and the site selection process for an offshore oscillating surge wave energy converter (OSWEC). However, the intrinsic dynamics of these interactions, coupled with the multi-modal nature of the optimization landscape, make this a daunting challenge. Addressing this, we introduce the novel Hill Climb - Explorative Gray Wolf Optimizer (HC-EGWO). This new methodology blends a local search method with a global optimizer, incorporating dynamic control over exploration and exploitation rates. This balance paves the way for an enhanced exploration of the solution space, ensuring the identification of superior-quality solutions. Further anchoring our approach, a feasibility landscape analysis based on linear water wave theory assumptions and the flap's maximum angular motion is conducted. This ensures the optimized OSWEC consistently operates within safety and efficiency parameters. Our findings hold significant promise for the development of more streamlined OSWEC power take-off systems. They provide insights for selecting the prime offshore site, optimizing power output, and bolstering the overall adoption of ocean renewable energy sources. Impressively, by employing the HC-EGWO method, we achieved an upswing of up to 3.31% in power output compared to other methods. This substantial increment underscores the efficacy of our proposed optimization approach. Conclusively, the outcomes offer invaluable knowledge for deploying OSWECs in the South Caspian Sea, where unique environmental conditions intersect with considerable energy potential.Comment: 35 pages, 22 Figures, 7 Table

    Big Data Analysis application in the renewable energy market: wind power

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    Entre as enerxías renovables, a enerxía eólica e unha das tecnoloxías mundiais de rápido crecemento. Non obstante, esta incerteza debería minimizarse para programar e xestionar mellor os activos de xeración tradicionais para compensar a falta de electricidade nas redes electricas. A aparición de técnicas baseadas en datos ou aprendizaxe automática deu a capacidade de proporcionar predicións espaciais e temporais de alta resolución da velocidade e potencia do vento. Neste traballo desenvólvense tres modelos diferentes de ANN, abordando tres grandes problemas na predición de series de datos con esta técnica: garantía de calidade de datos e imputación de datos non válidos, asignación de hiperparámetros e selección de funcións. Os modelos desenvolvidos baséanse en técnicas de agrupación, optimización e procesamento de sinais para proporcionar predicións de velocidade e potencia do vento a curto e medio prazo (de minutos a horas)

    Demand-side management in industrial sector:A review of heavy industries

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    Entropy in Image Analysis III

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    Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally unsupervised manner in future, thanks to their artificial intelligence. The articles published in the book clearly show such a future

    Additive Manufacturing of Bio and Synthetic Polymers

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    Additive manufacturing technology offers the ability to produce personalized products with lower development costs, shorter lead times, less energy consumed during manufacturing and less material waste. It can be used to manufacture complex parts and enables manufacturers to reduce their inventory, make products on-demand, create smaller and localized manufacturing environments, and even reduce supply chains. Additive manufacturing (AM), also known as fabricating three-dimensional (3D) and four-dimensional (4D) components, refers to processes that allow for the direct fabrication of physical products from computer-aided design (CAD) models through the repetitious deposition of material layers. Compared with traditional manufacturing processes, AM allows the production of customized parts from bio- and synthetic polymers without the need for molds or machining typical for conventional formative and subtractive fabrication.In this Special Issue, we aimed to capture the cutting-edge state-of-the-art research pertaining to advancing the additive manufacturing of polymeric materials. The topic themes include advanced polymeric material development, processing parameter optimization, characterization techniques, structure–property relationships, process modelling, etc., specifically for AM

    Product-Service development for circular economy and sustainability course

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    ABSTRACT: This book is an output of the ERASMUS+ KATCH_e project. KATCH_e stands for “Knowledge Alliance on Product-Service Development towards Circular Economy and Sustainability in Higher Education”. This was a 3-year project (2017-2019), aiming to address the challenge of reinforcing skills and competences in Higher Education and within the business community, in the field of product-service development for the circular economy and sustainability, with a particular focus on the construction and furniture sectors.info:eu-repo/semantics/publishedVersio

    Reticulate Evolution: Symbiogenesis, Lateral Gene Transfer, Hybridization and Infectious heredity

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    Strategic Latency Unleashed: The Role of Technology in a Revisionist Global Order and the Implications for Special Operations Forces

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    The article of record may be found at https://cgsr.llnl.govThis work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory in part under Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344. The views and opinions of the author expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC. ISBN-978-1-952565-07-6 LCCN-2021901137 LLNL-BOOK-818513 TID-59693This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory in part under Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344. The views and opinions of the author expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC. ISBN-978-1-952565-07-6 LCCN-2021901137 LLNL-BOOK-818513 TID-5969
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