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The aim of this book was to collect the most recent methods developed for NSO and its practical applications. The book contains seven papers: The first is the foreword by the Guest Editors giving a brief review of NSO and its real-life applications and acknowledging the outstanding contributions of Professor Adil Bagirov to both the theoretical and practical aspects of NSO. The second paper introduces a new and very efficient algorithm for solving uncertain unit-commitment (UC) problems. The third paper proposes a new nonsmooth version of the generalized damped Gauss–Newton method for solving nonlinear complementarity problems. In the fourth paper, the abs-linear representation of piecewise linear functions is extended to yield simultaneously their DC decomposition as well as the pair of generalized gradients. The fifth paper presents the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and nonsmooth optimization problems in many practical applications. In the sixth paper, a problem concerning the scheduling of nuclear waste disposal is modeled as a nonsmooth multiobjective mixed-integer nonlinear optimization problem, and a novel method using the two-slope parameterized achievement scalarizing functions is introduced. Finally, the last paper considers binary classification of a multiple instance learning problem and formulates the learning problem as a nonconvex nonsmooth unconstrained optimization problem with a DC objective function.

Information technology industries --- multiple instance learning --- support vector machine --- DC optimization --- nonsmooth optimization --- achievement scalarizing functions --- interactive method --- multiobjective optimization --- spent nuclear fuel disposal --- non-smooth optimization --- biased-randomized algorithms --- heuristics --- soft constraints --- DC function --- abs-linearization --- DCA --- Gauss-Newton method --- nonsmooth equations --- nonlinear complementarity problem --- B-differential --- superlinear convergence --- global convergence --- stochastic programming --- stochastic hydrothermal UC problem --- parallel computing --- asynchronous computing --- level decomposition --- multiple instance learning --- support vector machine --- DC optimization --- nonsmooth optimization --- achievement scalarizing functions --- interactive method --- multiobjective optimization --- spent nuclear fuel disposal --- non-smooth optimization --- biased-randomized algorithms --- heuristics --- soft constraints --- DC function --- abs-linearization --- DCA --- Gauss-Newton method --- nonsmooth equations --- nonlinear complementarity problem --- B-differential --- superlinear convergence --- global convergence --- stochastic programming --- stochastic hydrothermal UC problem --- parallel computing --- asynchronous computing --- level decomposition

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The aim of this book was to collect the most recent methods developed for NSO and its practical applications. The book contains seven papers: The first is the foreword by the Guest Editors giving a brief review of NSO and its real-life applications and acknowledging the outstanding contributions of Professor Adil Bagirov to both the theoretical and practical aspects of NSO. The second paper introduces a new and very efficient algorithm for solving uncertain unit-commitment (UC) problems. The third paper proposes a new nonsmooth version of the generalized damped Gauss–Newton method for solving nonlinear complementarity problems. In the fourth paper, the abs-linear representation of piecewise linear functions is extended to yield simultaneously their DC decomposition as well as the pair of generalized gradients. The fifth paper presents the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and nonsmooth optimization problems in many practical applications. In the sixth paper, a problem concerning the scheduling of nuclear waste disposal is modeled as a nonsmooth multiobjective mixed-integer nonlinear optimization problem, and a novel method using the two-slope parameterized achievement scalarizing functions is introduced. Finally, the last paper considers binary classification of a multiple instance learning problem and formulates the learning problem as a nonconvex nonsmooth unconstrained optimization problem with a DC objective function.

multiple instance learning --- support vector machine --- DC optimization --- nonsmooth optimization --- achievement scalarizing functions --- interactive method --- multiobjective optimization --- spent nuclear fuel disposal --- non-smooth optimization --- biased-randomized algorithms --- heuristics --- soft constraints --- DC function --- abs-linearization --- DCA --- Gauss–Newton method --- nonsmooth equations --- nonlinear complementarity problem --- B-differential --- superlinear convergence --- global convergence --- stochastic programming --- stochastic hydrothermal UC problem --- parallel computing --- asynchronous computing --- level decomposition --- n/a --- Gauss-Newton method

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Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these research areas. The deployment of enabling technologies (e.g., smart meters) has made high-granularity data available for many customer segments and to approach many issues, for instance, to make forecasting tasks feasible at several demand aggregation levels. The first challenge is the improvement of STLF models and their performance at new aggregation levels. Moreover, the mix of renewables in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the 2030–50 horizon. Many techniques have been proposed and applied for STLF, including traditional statistical models and AI techniques. Besides, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new sources of uncertainty in the power system is giving more importance to probabilistic load forecasting. This Special Issue deals with both fundamental research and practical application research on STLF methodologies to face the challenges of a more distributed and customer-centered power system.

History of engineering & technology --- short-term load forecasting --- demand-side management --- pattern similarity --- hierarchical short-term load forecasting --- feature selection --- weather station selection --- load forecasting --- special days --- regressive models --- electric load forecasting --- data preprocessing technique --- multiobjective optimization algorithm --- combined model --- Nordic electricity market --- electricity demand --- component estimation method --- univariate and multivariate time series analysis --- modeling and forecasting --- deep learning --- wavenet --- long short-term memory --- demand response --- hybrid energy system --- data augmentation --- convolution neural network --- residential load forecasting --- forecasting --- time series --- cubic splines --- real-time electricity load --- seasonal patterns --- Load forecasting --- VSTLF --- bus load forecasting --- DBN --- PSR --- distributed energy resources --- prosumers --- building electric energy consumption forecasting --- cold-start problem --- transfer learning --- multivariate random forests --- random forest --- electricity consumption --- lasso --- Tikhonov regularization --- load metering --- preliminary load --- short term load forecasting --- performance criteria --- power systems --- cost analysis --- day ahead --- feature extraction --- deep residual neural network --- multiple sources --- electricity

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Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these research areas. The deployment of enabling technologies (e.g., smart meters) has made high-granularity data available for many customer segments and to approach many issues, for instance, to make forecasting tasks feasible at several demand aggregation levels. The first challenge is the improvement of STLF models and their performance at new aggregation levels. Moreover, the mix of renewables in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the 2030–50 horizon. Many techniques have been proposed and applied for STLF, including traditional statistical models and AI techniques. Besides, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new sources of uncertainty in the power system is giving more importance to probabilistic load forecasting. This Special Issue deals with both fundamental research and practical application research on STLF methodologies to face the challenges of a more distributed and customer-centered power system.

History of engineering & technology --- short-term load forecasting --- demand-side management --- pattern similarity --- hierarchical short-term load forecasting --- feature selection --- weather station selection --- load forecasting --- special days --- regressive models --- electric load forecasting --- data preprocessing technique --- multiobjective optimization algorithm --- combined model --- Nordic electricity market --- electricity demand --- component estimation method --- univariate and multivariate time series analysis --- modeling and forecasting --- deep learning --- wavenet --- long short-term memory --- demand response --- hybrid energy system --- data augmentation --- convolution neural network --- residential load forecasting --- forecasting --- time series --- cubic splines --- real-time electricity load --- seasonal patterns --- Load forecasting --- VSTLF --- bus load forecasting --- DBN --- PSR --- distributed energy resources --- prosumers --- building electric energy consumption forecasting --- cold-start problem --- transfer learning --- multivariate random forests --- random forest --- electricity consumption --- lasso --- Tikhonov regularization --- load metering --- preliminary load --- short term load forecasting --- performance criteria --- power systems --- cost analysis --- day ahead --- feature extraction --- deep residual neural network --- multiple sources --- electricity --- short-term load forecasting --- demand-side management --- pattern similarity --- hierarchical short-term load forecasting --- feature selection --- weather station selection --- load forecasting --- special days --- regressive models --- electric load forecasting --- data preprocessing technique --- multiobjective optimization algorithm --- combined model --- Nordic electricity market --- electricity demand --- component estimation method --- univariate and multivariate time series analysis --- modeling and forecasting --- deep learning --- wavenet --- long short-term memory --- demand response --- hybrid energy system --- data augmentation --- convolution neural network --- residential load forecasting --- forecasting --- time series --- cubic splines --- real-time electricity load --- seasonal patterns --- Load forecasting --- VSTLF --- bus load forecasting --- DBN --- PSR --- distributed energy resources --- prosumers --- building electric energy consumption forecasting --- cold-start problem --- transfer learning --- multivariate random forests --- random forest --- electricity consumption --- lasso --- Tikhonov regularization --- load metering --- preliminary load --- short term load forecasting --- performance criteria --- power systems --- cost analysis --- day ahead --- feature extraction --- deep residual neural network --- multiple sources --- electricity

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This book was established after the 8th 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.

Research & information: general --- Mathematics & science --- robust optimization --- differential evolution --- ROOT --- optimization framework --- drainage rehabilitation --- overflooding --- pipe breaking --- VCO --- CMOS differential pair --- PVT variations --- Monte Carlo analysis --- multi-objective optimization --- Pareto Tracer --- continuation --- constraint handling --- surrogate modeling --- multiobjective optimization --- evolutionary algorithms --- kriging method --- ensemble method --- adaptive algorithm --- liquid storage tanks --- base excitation --- artificial intelligence --- Multi-Gene Genetic Programming --- computational fluid dynamics --- finite volume method --- JSSP --- CMOSA --- CMOTA --- chaotic perturbation --- fixed point arithmetic --- FP16 --- pseudo random number generator --- incorporation of preferences --- multi-criteria classification --- decision-making process --- multi-objective evolutionary optimization --- outranking relationships --- decision maker profile --- profile assessment --- region of interest approximation --- optimization using preferences --- hybrid evolutionary approach --- forecasting --- Convolutional Neural Network --- LSTM --- COVID-19 --- deep learning --- trust region methods --- multiobjective descent --- derivative-free optimization --- radial basis functions --- fully linear models --- decision making process --- cognitive tasks --- recommender system --- project portfolio selection problem --- usability evaluation --- multi-objective portfolio optimization problem --- trapezoidal fuzzy numbers --- density estimators --- steady state algorithms --- protein structure prediction --- Hybrid Simulated Annealing --- Template-Based Modeling --- structural biology --- Metropolis --- optimization --- linear programming --- energy central --- robust optimization --- differential evolution --- ROOT --- optimization framework --- drainage rehabilitation --- overflooding --- pipe breaking --- VCO --- CMOS differential pair --- PVT variations --- Monte Carlo analysis --- multi-objective optimization --- Pareto Tracer --- continuation --- constraint handling --- surrogate modeling --- multiobjective optimization --- evolutionary algorithms --- kriging method --- ensemble method --- adaptive algorithm --- liquid storage tanks --- base excitation --- artificial intelligence --- Multi-Gene Genetic Programming --- computational fluid dynamics --- finite volume method --- JSSP --- CMOSA --- CMOTA --- chaotic perturbation --- fixed point arithmetic --- FP16 --- pseudo random number generator --- incorporation of preferences --- multi-criteria classification --- decision-making process --- multi-objective evolutionary optimization --- outranking relationships --- decision maker profile --- profile assessment --- region of interest approximation --- optimization using preferences --- hybrid evolutionary approach --- forecasting --- Convolutional Neural Network --- LSTM --- COVID-19 --- deep learning --- trust region methods --- multiobjective descent --- derivative-free optimization --- radial basis functions --- fully linear models --- decision making process --- cognitive tasks --- recommender system --- project portfolio selection problem --- usability evaluation --- multi-objective portfolio optimization problem --- trapezoidal fuzzy numbers --- density estimators --- steady state algorithms --- protein structure prediction --- Hybrid Simulated Annealing --- Template-Based Modeling --- structural biology --- Metropolis --- optimization --- linear programming --- energy central