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Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms.

Technology: general issues --- global optimization --- cuckoo search algorithm --- Q-learning --- mutation --- self-adaptive step size --- evolutionary computation --- playtesting --- game feature --- game simulation --- game trees --- playtesting metric --- validation --- Pareto optimality --- h-index --- ranking --- dominance --- Pareto-front --- multi-indicators --- multi-metric --- multi-resources --- citation --- universities ranking --- swarm intelligence --- simulated annealing --- krill herd --- particle swarm optimization --- quantum --- elephant herding optimization --- engineering optimization --- metaheuristic --- constrained optimization --- multi-objective optimization --- single objective optimization --- differential evolution --- success-history --- premature convergence --- turning-based mutation --- opposition-based learning --- ant colony optimization --- opposite path --- traveling salesman problems --- whale optimization algorithm --- WOA --- binary whale optimization algorithm --- bWOA-S --- bWOA-V --- feature selection --- classification --- dimensionality reduction --- menu planning problem --- evolutionary algorithm --- decomposition-based multi-objective optimisation --- memetic algorithm --- iterated local search --- diversity preservation --- single-objective optimization --- knapsack problem --- travelling salesman problem --- seed schedule --- many-objective optimization --- fuzzing --- bug detection --- path discovery --- evolutionary algorithms (EAs) --- coevolution --- dynamic learning --- performance indicators --- magnetotelluric --- one-dimensional inversions --- geoelectric model --- optimization problem --- multi-task optimization --- multi-task evolutionary computation --- knowledge transfer --- assortative mating --- unified search space --- quantum computing --- grey wolf optimizer --- 0-1 knapsack problem --- green shop scheduling --- fuzzy hybrid flow shop scheduling --- discrete artificial bee colony algorithm --- minimize makespan --- minimize total energy consumption

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The book “Assessment of Renewable Energy Resources with Remote Sensing" focuses on disseminating scientific knowledge and technological developments for the assessment and forecasting of renewable energy resources using remote sensing techniques. The eleven papers inside the book provide an overview of remote sensing applications on hydro, solar, wind and geothermal energy resources and their major goal is to provide state of art knowledge to contribute with the renewable energy resource deployment, especially in regions where energy demand is rapidly expanding. Renewable energy resources have an intrinsic relationship with local environmental features and the regional climate. Even small and fast environment and/or climate changes can cause significant variability in power generation at different time and space scales. Methodologies based on remote sensing are the primary source of information for the development of numerical models that aim to support the planning and operation of an electric system with a substantial contribution of intermittent energy sources. In addition, reliable data and knowledge on renewable energy resource assessment are fundamental to ensure sustainable expansion considering environmental, financial and energetic security.

Research & information: general --- metaheuristic --- parameter extraction --- solar photovoltaic --- whale optimization algorithm --- cloud detection --- digitized image processing --- artificial neural networks --- solar irradiance estimation --- solar irradiance forecasting --- solar energy --- sky camera --- remote sensing --- CSP plants --- coastal wind measurements --- scanning LiDAR --- plan position indicator --- velocity volume processing --- Hazaki Oceanographical Research Station --- cloud coverage --- image processing --- total sky imagery --- geothermal energy --- geophysical prospecting --- time domain electromagnetic method --- electrical resistivity tomography --- potential well field location --- GES-CAL software --- smart island --- solar radiation forecasting --- light gradient boosting machine --- multistep-ahead prediction --- feature importance --- voxel-design approach --- shading envelopes --- point cloud data --- computational design method --- passive design strategy --- lake breeze influence --- hydropower reservoir --- solar irradiance enhancement --- solar energy resource --- wind speed --- extreme value analysis --- scatterometer --- feature engineering --- forecasting --- graphical user interface software --- machine learning --- photovoltaic power plant --- surface solar radiation --- global radiation --- satellite --- Baltic area --- coastline --- cloud --- convection --- climate --- renewable energy resource assessment and forecasting --- remote sensing data acquisition --- data processing --- statistical analysis --- machine learning techniques

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The book “Assessment of Renewable Energy Resources with Remote Sensing" focuses on disseminating scientific knowledge and technological developments for the assessment and forecasting of renewable energy resources using remote sensing techniques. The eleven papers inside the book provide an overview of remote sensing applications on hydro, solar, wind and geothermal energy resources and their major goal is to provide state of art knowledge to contribute with the renewable energy resource deployment, especially in regions where energy demand is rapidly expanding. Renewable energy resources have an intrinsic relationship with local environmental features and the regional climate. Even small and fast environment and/or climate changes can cause significant variability in power generation at different time and space scales. Methodologies based on remote sensing are the primary source of information for the development of numerical models that aim to support the planning and operation of an electric system with a substantial contribution of intermittent energy sources. In addition, reliable data and knowledge on renewable energy resource assessment are fundamental to ensure sustainable expansion considering environmental, financial and energetic security.

Research & information: general --- metaheuristic --- parameter extraction --- solar photovoltaic --- whale optimization algorithm --- cloud detection --- digitized image processing --- artificial neural networks --- solar irradiance estimation --- solar irradiance forecasting --- solar energy --- sky camera --- remote sensing --- CSP plants --- coastal wind measurements --- scanning LiDAR --- plan position indicator --- velocity volume processing --- Hazaki Oceanographical Research Station --- cloud coverage --- image processing --- total sky imagery --- geothermal energy --- geophysical prospecting --- time domain electromagnetic method --- electrical resistivity tomography --- potential well field location --- GES-CAL software --- smart island --- solar radiation forecasting --- light gradient boosting machine --- multistep-ahead prediction --- feature importance --- voxel-design approach --- shading envelopes --- point cloud data --- computational design method --- passive design strategy --- lake breeze influence --- hydropower reservoir --- solar irradiance enhancement --- solar energy resource --- wind speed --- extreme value analysis --- scatterometer --- feature engineering --- forecasting --- graphical user interface software --- machine learning --- photovoltaic power plant --- surface solar radiation --- global radiation --- satellite --- Baltic area --- coastline --- cloud --- convection --- climate --- renewable energy resource assessment and forecasting --- remote sensing data acquisition --- data processing --- statistical analysis --- machine learning techniques

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This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book.

flexible job-shop scheduling problem --- combinatorial optimization --- genetic algorithm --- candidate order-based genetic algorithm --- multichromosome --- facility layout --- optimization --- metaheuristic algorithm --- cell formation --- design of experiments --- digital platforms --- decision-making --- insurance --- Baltic --- customization --- personalization --- assembly line balancing --- group technology --- cluster algorithm --- bottleneck station --- output rate --- tolerance allocation --- machine and process selection --- heuristic approach --- univariate search method --- whale optimization algorithm --- selective assembly --- overrunning clutch assembly --- harmony search algorithm --- Hastelloy X --- turning --- cutting force --- surface roughness --- liquid nitrogen --- grass-hooper optimization algorithm --- moth-flame optimization algorithm --- hybrid bat algorithm --- optimization problem --- the distributed assembly permutation flowshop scheduling problem --- variable neighborhood descent --- multi-criteria assessment --- cell manufacturing design --- operational complexity --- makespan --- production line balancing rate --- electrochemical machining (ECM) --- material removal rate (MRR) --- nickel presence (NP) --- grey wolf optimizer (GWO) --- moth-flame optimization algorithm (MFO) --- Monel 400 alloys --- n/a

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Image analysis is a fundamental task for extracting information from images acquired across a range of different devices. Since reliable quantitative results are requested, image analysis requires highly sophisticated numerical and analytical methods—particularly for applications in medicine, security, and remote sensing, where the results of the processing may consist of vitally important data. The contributions to this book provide a good overview of the most important demands and solutions concerning this research area. In particular, the reader will find image analysis applied for feature extraction, encryption and decryption of data, color segmentation, and in the support new technologies. In all the contributions, entropy plays a pivotal role.

keyframes --- time-delay --- whale optimization algorithm --- multilevel thresholding --- multi-exposure image fusion --- additive manufacturing --- patch structure decomposition --- ultra-sound images --- 3D scanning --- Arimoto entropy --- contrast enhancement --- spatial filling factor --- depth maps --- image processing --- 3D prints --- differential evolution --- field of experts --- normalized divergence measure --- image privacy --- multiscale top-hat transform --- q-exponential --- texture information entropy --- diffusion --- hybrid algorithm --- Weibull statistics --- adaptive selection --- nonextensive entropy --- computer aided diagnostics --- fatty liver --- random forest --- DNA encoding --- low contrast --- entropy --- Minkowski island --- fuzzy entropy --- free-form deformations --- person re-identification --- chaotic system --- DNA computing --- pavement --- information entropy --- discrete entropy --- Tsallis statistics --- video skimming --- prime-indexed primes --- natural scene statistics (NSS) --- Hénon map --- q-sigmoid --- image entropy --- Shannon entropy --- macrotexture --- Shannon’s entropy --- binary image --- multi-feature fusion --- image analysis --- uncertainty assessment --- non-rigid registration --- hash layer --- Cantor set --- dynamic filtering --- deep neural network --- security analysis --- multiple-image encryption --- Hamming distance --- blind image quality assessment (BIQA) --- q-Gaussian --- remote sensing --- decay trend --- chaotic cryptography --- chaotic strategy --- cross-entropy loss --- random insertion --- metabolic syndrome --- sign languages --- generalized entropies --- relevance feedback --- image retrieval --- two-dimensional chaotic economic map --- cryptanalysis --- infrared images --- 3D Latin cube --- SHA-256 hash value --- gradient distributions --- structural entropy --- discrete cosine transform (DCT) --- chaotic map --- hepatic steatosis --- machine vision --- electromagnetic field optimization --- security --- image segmentation --- quantization loss --- colonoscopy --- video summarization --- permutation --- Kapur’s entropy --- surface quality assessment --- permutation-diffusion --- Ramanujan primes --- Rényi entropies --- chosen-plaintext attack --- image encryption --- dynamic index --- color image segmentation --- ultrasound --- Otsu method --- sigmoid --- reconstruction --- image information entropy --- 3-D digital imaging --- positron emission tomography --- medical imaging