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The increase in decentralized, volatile electricity generation as result of the German energy system transformation causes congestions in electricity grids. IT networking and coordination of producers and consumers in smart grids promises a solution to these problems. In this context, this work presents an energy management approach that approximates specified, aggregated power profiles. The approach uses power forecasts and flexibilities and takes network restrictions into account.

Energiemanagement --- load forecast --- smart grid --- multikriterielle Optimierung --- energy management architecture --- Lastprognoseenergy management --- multi-objective optimization --- Energiemanagement-Architektur --- Smart Grid

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Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, to AI system parameter optimization and the design of artificial neural networks and feature selection in machine learning systems. This volume will present recent results of applications of the most successful metaheuristics, from differential evolution and particle swarm optimization to artificial neural networks, loT allocation, and multi-objective optimization problems. It will also provide a broad view of the role and the potential of evolutionary algorithms as service components in Al systems.

Information technology industries --- multi-objective optimization problems --- particle swarm optimization (PSO) --- Gaussian mutation --- improved learning strategy --- big data --- interval concept lattice --- horizontal union --- sequence traversal --- evolutionary algorithms --- multi-objective optimization --- parameter puning --- parameter analysis --- particle swarm optimization --- differential evolution --- global continuous optimization --- wireless sensor networks --- task allocation --- stochastic optimization --- social network optimization --- memetic particle swarm optimization --- adaptive local search operator --- co-evolution --- PSO --- formal methods in evolutionary algorithms --- self-adaptive differential evolutionary algorithms --- constrained optimization --- ensemble of constraint handling techniques --- hybrid algorithms --- association rules --- mining algorithm --- vertical union --- neuroevolution --- neural networks --- n/a

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Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, to AI system parameter optimization and the design of artificial neural networks and feature selection in machine learning systems. This volume will present recent results of applications of the most successful metaheuristics, from differential evolution and particle swarm optimization to artificial neural networks, loT allocation, and multi-objective optimization problems. It will also provide a broad view of the role and the potential of evolutionary algorithms as service components in Al systems.

Information technology industries --- multi-objective optimization problems --- particle swarm optimization (PSO) --- Gaussian mutation --- improved learning strategy --- big data --- interval concept lattice --- horizontal union --- sequence traversal --- evolutionary algorithms --- multi-objective optimization --- parameter puning --- parameter analysis --- particle swarm optimization --- differential evolution --- global continuous optimization --- wireless sensor networks --- task allocation --- stochastic optimization --- social network optimization --- memetic particle swarm optimization --- adaptive local search operator --- co-evolution --- PSO --- formal methods in evolutionary algorithms --- self-adaptive differential evolutionary algorithms --- constrained optimization --- ensemble of constraint handling techniques --- hybrid algorithms --- association rules --- mining algorithm --- vertical union --- neuroevolution --- neural networks

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Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc.

Technology: general issues --- History of engineering & technology --- Automatic Voltage Regulation system --- Chaotic optimization --- Fractional Order Proportional-Integral-Derivative controller --- Yellow Saddle Goatfish Algorithm --- two-stage method --- mono and multi-objective optimization --- multi-objective optimization --- optimal design --- Gough–Stewart --- parallel manipulator --- performance metrics --- diversity control --- genetic algorithm --- bankruptcy problem --- classification --- T-junctions --- neural networks --- finite elements analysis --- surrogate --- beam improvements --- beam T-junctions models --- artificial neural networks (ANN) limited training data --- multi-objective decision-making --- Pareto front --- preference in multi-objective optimization --- aeroacoustics --- trailing-edge noise --- global optimization --- evolutionary algorithms --- nearly optimal solutions --- archiving strategy --- evolutionary algorithm --- non-linear parametric identification --- multi-objective evolutionary algorithms --- availability --- design --- preventive maintenance scheduling --- encoding --- accuracy levels --- plastics thermoforming --- sheet thickness distribution --- evolutionary optimization --- genetic programming --- control --- differential evolution --- reusable launch vehicle --- quality control --- roughness measurement --- machine vision --- machine learning --- parameter optimization --- distance-based --- mutation-selection --- real application --- experimental study --- global optimisation --- worst-case scenario --- robust --- min-max optimization --- optimal control --- multi-objective optimisation --- robust design --- trajectory optimisation --- uncertainty quantification --- unscented transformation --- spaceplanes --- space systems --- launchers

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Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc.

Technology: general issues --- History of engineering & technology --- Automatic Voltage Regulation system --- Chaotic optimization --- Fractional Order Proportional-Integral-Derivative controller --- Yellow Saddle Goatfish Algorithm --- two-stage method --- mono and multi-objective optimization --- multi-objective optimization --- optimal design --- Gough–Stewart --- parallel manipulator --- performance metrics --- diversity control --- genetic algorithm --- bankruptcy problem --- classification --- T-junctions --- neural networks --- finite elements analysis --- surrogate --- beam improvements --- beam T-junctions models --- artificial neural networks (ANN) limited training data --- multi-objective decision-making --- Pareto front --- preference in multi-objective optimization --- aeroacoustics --- trailing-edge noise --- global optimization --- evolutionary algorithms --- nearly optimal solutions --- archiving strategy --- evolutionary algorithm --- non-linear parametric identification --- multi-objective evolutionary algorithms --- availability --- design --- preventive maintenance scheduling --- encoding --- accuracy levels --- plastics thermoforming --- sheet thickness distribution --- evolutionary optimization --- genetic programming --- control --- differential evolution --- reusable launch vehicle --- quality control --- roughness measurement --- machine vision --- machine learning --- parameter optimization --- distance-based --- mutation-selection --- real application --- experimental study --- global optimisation --- worst-case scenario --- robust --- min-max optimization --- optimal control --- multi-objective optimisation --- robust design --- trajectory optimisation --- uncertainty quantification --- unscented transformation --- spaceplanes --- space systems --- launchers