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This book was established after the 6th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications.
genetic programming --- driving scoring functions --- driving events --- risky driving --- intelligent transportation systems --- mixture experiments --- single component constraints --- genetic algorithm --- IV-optimality criterion --- multiobjective optimization --- optimal control --- model order reduction --- model predictive control --- location routing problem --- rubber --- modify differential evolution algorithm --- vehicle routing problem --- differential evolution algorithm --- crop planning --- economic crops --- improvement differential evolution algorithm --- averaged Hausdorff distance --- evolutionary multi-objective optimization --- power means --- metric measure spaces --- performance indicator --- Pareto front --- surrogate-based optimization --- numerical simulations --- shape morphing --- bulbous bow --- open-source framework --- U-shaped assembly line balancing --- basic differential evolution algorithm --- improved differential evolution algorithm --- optimal solutions --- Genetic Programming --- Bloat --- NEAT --- Local Search --- EvoSpace --- improved differential evolution algorithm --- flexible job shop scheduling problem --- local search and jump search --- evolutionary computation --- multi-objective optimization --- decision space diversity
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Electric power systems around the world are changing in terms of structure, operation, management and ownership due to technical, financial, and ideological reasons. Power systems keep on expanding in terms of geographical areas, asset additions, and the penetration of new technologies in generation, transmission, and distribution. The conventional methods for solving the power system design, planning, operation, and control problems have been extensively used for different applications, but these methods suffer from several difficulties, thus providing suboptimal solutions. Computationally intelligent methods can offer better solutions for several conditions and are being widely applied in electrical engineering applications. This Special Issue represents a thorough treatment of computational intelligence from an electrical power system engineer’s perspective. Thorough, well-organised, and up-to-date, it examines in detail some of the important aspects of this very exciting and rapidly emerging technology, including machine learning, particle swarm optimization, genetic algorithms, and deep learning systems. Written in a concise and flowing manner by experts in the area of electrical power systems who have experience in the application of computational intelligence for solving many complex and difficult power system problems, this Special Issue is ideal for professional engineers and postgraduate students entering this exciting field.
defect detection --- glass insulator --- localization --- self-shattering --- spatial features --- particle swarm optimization --- particle update mode --- inertia weight --- reactive power optimization --- Combustion efficiency --- NOx emissions constraints --- boiler load constraints --- least square support vector machine --- differential evolution algorithm --- model predictive control --- incipient cable failure --- VMD --- feature extraction --- CNN --- economic load dispatch --- emission dispatch --- combined economic emission/environmental dispatch --- particle swarm optimization --- genetic algorithm --- penalty factor approach --- long short term memory (LSTM) --- genetic algorithm (GA) --- short term load forecasting (STLF) --- electricity load forecasting --- multivariate time series --- grid observability --- active distribution system --- meter allocation --- parameter estimation
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Energy has been a crucial element for human beings and sustainable development. The issues of global warming and non-green energy have yet to be resolved. This book is a collection of twelve articles that provide strong evidence for the success of artificial intelligence deployment in energy research, particularly research devoted to non-intrusive load monitoring, network, and grid, as well as other emerging topics. The presented artificial intelligence algorithms may provide insight into how to apply similar approaches, subject to fine-tuning and customization, to other unexplored energy research. The ultimate goal is to fully apply artificial intelligence to the energy sector. This book may serve as a guide for professionals, researchers, and data scientists—namely, how to share opinions and exchange ideas so as to facilitate a better fusion of energy, academic, and industry research, and improve in the quality of people's daily life activities.
artificial intelligence --- demand response --- energy --- policy making --- genetic algorithm --- multiple kernel learning --- non-intrusive load monitoring --- smart grid --- smart metering --- support vector machine --- smart cities --- smart villages --- scheduling --- demand side management --- smart grid --- home energy management --- NILM --- energy disaggregation --- MCP39F511 --- Jetson TX2 --- transient signature --- decision tree --- LSTM --- wireless sensor networks --- energy efficient coverage --- distributed genetic algorithm --- smart grid --- forecasting --- load --- price --- CNN --- LR --- ELR --- RELM --- ERELM --- insulator --- Faster R-CNN --- object detection --- RPN --- deep learning --- load disaggregation --- nonintrusive load monitoring --- conditional random fields --- feature extraction --- mud rheology --- drill-in fluid --- artificial neural network --- Marsh funnel --- plastic viscosity --- yield point --- static young’s modulus --- artificial neural networks --- self-adaptive differential evolution algorithm --- sandstone reservoirs --- non-intrusive load monitoring --- home energy management systems --- ambient assisted living --- demand response --- machine learning --- internet of things --- smart grids --- artificial intelligence --- computational intelligence --- energy management --- machine learning --- optimization algorithms --- sensor network --- smart city --- smart grid --- sustainable development
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River catchments and reservoirs play a central role in water security, food supply, flood risk management, hydropower generation, and ecosystem services; however, they are now under increasing pressure from population growth, economic activities, and changing climate means and extremes in many parts of the world. Adaptive management of river catchments and reservoirs requires an in-depth understanding of the impacts of future uncertainties and thus the development of robust, sustainable solutions to meet the needs of various stakeholders and the environment. To tackle the huge challenges in moving towards adaptive catchment management, this book presents the latest developments in cutting-edge knowledge, novel methodologies, innovative management strategies, and case studies, focusing on the following themes: reservoir dynamics and impact analysis of dam construction, optimal reservoir operation, climate change impacts on hydrological processes and water management, and integrated catchment management.
Siemianówka --- hydrology --- Narew River --- dam --- reservoir --- discharge --- flow regime --- reservoir flushing --- numerical simulation --- flushing efficiency --- Kurobe River --- two-dimensional bed evolution model --- sediment flushing of empty storage --- shaft spillway pipe --- sediment flushing efficiency --- sediment regime --- suspended sediment concentration --- vertical profiles of concentration --- Jingjiang River Reach --- Yangtze River --- CO2 --- reservoirs --- general regression neural network --- back propagation neural network --- climate change --- CMIP3 --- CMIP5 --- downscaling --- runoff response --- SWAT model --- stochastic linear programming --- Markov chain --- reliability --- vulnerability --- reservoir operation --- stochastic dynamic programming --- protection zone --- nutrient uptake --- NPP --- South-to-North Water Transfer Project --- Miyun Reservoir --- reservoir operation --- optimization --- SWAT --- HEC-ResPRM --- climate change --- CORDEX-Africa --- Tekeze basin --- long distance water diversion --- inverted siphon --- sensitivity analysis --- integrated supply system modeling --- sediment regime --- suspended sediment concentration --- vertical profiles of concentration --- the Jingjiang River Reach --- the Yangtze River --- reservoir operation --- multi-stage stochastic optimization --- TB-MPC --- flood control --- real-time control --- energy --- hydropower stations --- differential evolution algorithm --- optimal scheduling --- ?-constrained method --- drinking water resources --- water environmental capacity (WEC) --- Environmental Fluid Dynamics Code (EFDC) model --- the Huangshi Reservoir --- seasonal rainfall --- upper Chao Phraya River Basin --- El Niño/Southern Oscillation --- Indian Monsoon --- sea surface temperatures --- reverse regulation --- coupling model --- aftereffect --- accompanying progressive optimality algorithm --- Dokan Dam --- runoff --- sediment load --- SWAT --- natural flow regime --- multi-objective model --- uncertainty --- genetic algorithm --- land and water resources --- system dynamics --- modeling --- scenario analysis --- Heilongjiang --- tropical reservoir --- heating impact --- Langcang-Mekong River --- Kappa distribution --- parameter relation --- partial gauged basin --- power function --- ratio curve --- ungauged basin --- reservoir operation --- integrated surface water-groundwater model --- Heihe River Basin --- environmental flow --- irrigation --- design and operation of the multipurpose reservoir --- water deficit --- reservoir simulation model --- climate change --- multi-objective optimization NSGA II --- resilience and robustness --- costs and benefits --- water energy --- multi-agent of river basin --- game theory --- water resources allocation --- optimal flood control operation --- cascade reservoirs --- dynamic programming with progressive optimality algorithm (DP-POA) --- the upper Yangtze River Basin --- parameterization --- simulation --- optimization --- direct policy search --- hedging policy --- shortage ratio: Vulnerability --- NSGA-II --- lentic habitats --- bitterling --- mussel --- floodplain vertical shape index --- sediment management --- adaptive management --- catchment modelling --- integrated management --- reservoir operation
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