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  • 1
    Online-Ressource
    Online-Ressource
    Engineering Applications of Modern Metaheuristics (2023)
    Cham : Springer International Publishing | Cham : Imprint: Springer
    Details
    ISBN: 9783031168321
    Sprache: Englisch
    Seiten: 1 Online-Ressource(VI, 209 p. 96 illus., 70 illus. in color.)
    Ausgabe: 1st ed. 2023.
    Serie: Studies in Computational Intelligence 1069
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    Schlagwort(e): Engineering—Data processing. ; Computational intelligence. ; Artificial intelligence. ; Engineering
    Kurzfassung: Empirical Comparison of Heuristic Optimisation Methods for Automated Car Setup -- Metaheuristic algorithms in IoT: Optimized Edge Node Localization -- Jaya algorithm versus differential evolution: a comparative case study on optic disc localization in eye fundus images -- Minimum transmission power control for the Internet of Things with swarm intelligence algorithms -- An Enhanced Gradient Based Optimized Controller for Load Frequency Control of a Two Area Automatic Generation Control System -- A meta-heuristic algorithm based on the happiness model -- Application of Metaheuristic Techniques for Enhancing the Financial Profitability of Wind Power Generation Systems -- Optimization of Demand Response -- Fitting curves of ruminal degradation using a metaheuristic approach -- Optimizing a Real Case Assembly Line Balancing Problem Using Various Techniques -- Multi-Circle Detection Using Multimodal Optimization.
    Kurzfassung: This book is a collection of various methodologies that make it possible for metaheuristics and hyper-heuristics to solve problems that occur in the real world. This book contains chapters that make use of metaheuristics techniques. The application fields range from image processing to transmission power control, and case studies and literature reviews are included to assist the reader. Furthermore, some chapters present cutting-edge methods for load frequency control and IoT implementations. In this sense, the book offers both theoretical and practical contents in the form of metaheuristic algorithms. The researchers used several stochastic optimization methods in this book, including evolutionary algorithms and Swarm-based algorithms. The chapters were written from a scientific standpoint. As a result, the book is primarily aimed at undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics, but it can also be used in courses on Artificial Intelligence, among other things. Similarly, the material may be beneficial to research in evolutionary computation and artificial intelligence communities.
    DOI: 10.1007/978-3-031-16832-1
    URL: lizenzpflichtig
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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    Online-Ressource
    BSZ
    MPI Ethno. Forsch.
  • 2
    Online-Ressource
    Online-Ressource
    Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems (2022)
    Cham : Springer International Publishing | Cham : Imprint: Springer
    Details
    ISBN: 9783030990794
    Sprache: Englisch
    Seiten: 1 Online-Ressource(IX, 497 p. 221 illus., 177 illus. in color.)
    Ausgabe: 1st ed. 2022.
    Serie: Studies in Computational Intelligence 1038
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    Schlagwort(e): Computational intelligence. ; Machine learning.
    Kurzfassung: Combined Optimization Algorithms for Incorporating DG in Distribution Systems -- Intelligent computational models for cancer diagnosis: A Comprehensive Review -- Elitist-Ant System metaheuristic for ITC 2021- Sports Timetabling -- Swarm intelligence algorithms-based Machine Learning Framework for Medical Diagnosis: A Comprehensive Review -- Aggregation of Semantically Similar News Articles with the help of Embedding Techniques and Unsupervised Machine Learning Algorithms: A Machine Learning Application with Semantic Technologies -- Integration of Machine Learning and Optimization Techniques for Cardiac Health Recognition -- Metaheuristics for Parameter Estimation of Solar Photovoltaic Cells: A Comprehensive Review -- Big Data Analysis using Hybrid Meta-heuristic Optimization Algorithm and MapReduce Framework -- Deep Neural Network for Virus Mutation Prediction: A Comprehensive Review -- 2D Target/Anomaly Detection in Time Series Drone Images using Deep Few-Shot Learning in Small Training Dataset -- Hybrid Adaptive Moth-Flame Optimizer and Opposition-Based Learning for Training Multilayer Perceptrons -- Early Detection of Coronary Artery Disease Using a PSO-based Neuroevolution Model -- Review for meta-heuristic optimization propels machine learning computations execution on spam comment area under digital security aegis region -- Solving reality based optimization trajectory problems with different metaphor inspired metaheuristic algorithms -- Parameter Tuning of PID controller Based on Arithmetic Optimization Algorithm in IOT systems -- Testing and Analysis of Predictive Capabilities of Machine Learning Algorithms -- AI Based Technologies for Digital and Banking Fraud During COVID -19 -- Gradient-Based Optimizer for structural optimization problems -- Aquila Optimizer based PSO Swarm Intelligence for IoT Task Scheduling Application in Cloud Computing.
    Kurzfassung: This book collects different methodologies that permit metaheuristics and machine learning to solve real-world problems. This book has exciting chapters that employ evolutionary and swarm optimization tools combined with machine learning techniques. The fields of applications are from distribution systems until medical diagnosis, and they are also included different surveys and literature reviews that will enrich the reader. Besides, cutting-edge methods such as neuroevolutionary and IoT implementations are presented in some chapters. In this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and can be used in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the material can be helpful for research from the evolutionary computation, artificial intelligence communities.
    DOI: 10.1007/978-3-030-99079-4
    URL: lizenzpflichtig
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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    Online-Ressource
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  • 3
    Online-Ressource
    Online-Ressource
    Differential Evolution: From Theory to Practice (2022)
    Singapore : Springer Singapore | Singapore : Imprint: Springer
    Details
    ISBN: 9789811680823
    Sprache: Englisch
    Seiten: 1 Online-Ressource(XIV, 381 p. 116 illus., 89 illus. in color.)
    Ausgabe: 1st ed. 2022.
    Serie: Studies in Computational Intelligence 1009
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    Schlagwort(e): Computational intelligence. ; Artificial intelligence. ; Mathematical optimization. ; Algorithms.
    Kurzfassung: Analysis of Structural Bias in Differential Evolution Configurations -- Spherical Model of Population Dynamics in Differential Evolution -- Reinforcement Learning-based Differential Evolution for Global Optimization -- Analytical Study on the Role of Scale Factor Parameter of Differential Evolution Algorithm on its Convergence Nature -- The Trap of Sisyphus Work in Differential Evolution and How to Avoid It -- Investigations on Distributed Differential Evolution Framework with Fault Tolerance Mechanisms -- Differential Evolution for Water Management Problems -- Sobol Sequence Based MOSADE Algorithm for Multi-Objective Design of Water Distribution Networks -- A Comparative Study on Parameter Estimation of Covid Epidemiological Models using Differential Evolution Algorithm -- Applications of Differential Evolution in Electric Power Systems -- Detection of Heavy Sandstorm Regions using Composite Differential Evolution Algorithm -- A Hybrid Artificial Differential Evolution Gorilla Troops Optimizer for High Dimensional Optimization Problems.
    Kurzfassung: This book addresses and disseminates state-of-the-art research and development of differential evolution (DE) and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques. Differential evolution is a population-based meta-heuristic technique for global optimization capable of handling non-differentiable, non-linear and multi-modal objective functions. Many advances have been made recently in differential evolution, from theory to applications. This book comprises contributions which include theoretical developments in DE, performance comparisons of DE, hybrid DE approaches, parallel and distributed DE for multi-objective optimization, software implementations, and real-world applications. The book is useful for researchers, practitioners, and students in disciplines such as optimization, heuristics, operations research and natural computing.
    DOI: 10.1007/978-981-16-8082-3
    URL: lizenzpflichtig
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 4
    Online-Ressource
    Online-Ressource
    Handbook of Nature-Inspired Optimization Algorithms: The State of the Art : Volume I: Solving Single Objective Bound-Constrained Real-Parameter Numerical Optimization Problems (2022)
    Cham : Springer International Publishing | Cham : Imprint: Springer
    Details
    ISBN: 9783031075124
    Sprache: Englisch
    Seiten: 1 Online-Ressource(X, 279 p. 94 illus., 73 illus. in color.)
    Ausgabe: 1st ed. 2022.
    Serie: Studies in Systems, Decision and Control 212
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    Paralleltitel: Erscheint auch als Handbook of nature-inspired optimization algorithms: the state of the art ; Volume 1: Solving single objective bound-constrained real-parameter numerical optimization problems
    RVK:
    SK 870
    Schlagwort(e): Computational intelligence. ; Artificial intelligence.
    Kurzfassung: Chaotic-SCA Salp Swarm Algorithm Enhanced with Opposition Based Learning: Application to Decrease Carbon Footprint in Patient Flow -- Design and Performance Evaluation of Objective Functions Based on Various Measures of Fuzzy Entropies for Image Segmentation using Grey Wolf Optimization -- Improved Artificial Bee Colony Algorithm with Adaptive Pursuit Based Strategy Selection -- Beetle Antennae Search Algorithm for the Motion Planning of Industrial Manipulator -- Solving Optimal Power Flow with Considering Placement of TCSC and FACTS Cost Using Cuckoo Search Algorithm.
    Kurzfassung: The introduction of nature-inspired optimization algorithms (NIOAs), over the past three decades, helped solve nonlinear, high-dimensional, and complex computational optimization problems. NIOAs have been originally developed to overcome the challenges of global optimization problems such as nonlinearity, non-convexity, non-continuity, non-differentiability, and/or multimodality which traditional numerical optimization techniques had difficulties solving. The main objective for this book is to make available a self-contained collection of modern research addressing the general bound-constrained optimization problems in many real-world applications using nature-inspired optimization algorithms. This book is suitable for a graduate class on optimization, but will also be useful for interested senior students working on their research projects.
    DOI: 10.1007/978-3-031-07512-4
    URL: lizenzpflichtig
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 5
    Online-Ressource
    Online-Ressource
    Handbook of Nature-Inspired Optimization Algorithms: The State of the Art : Volume II: Solving Constrained Single Objective Real-Parameter Optimization Problems (2022)
    Cham : Springer International Publishing | Cham : Imprint: Springer
    Details
    ISBN: 9783031075162
    Sprache: Englisch
    Seiten: 1 Online-Ressource(X, 214 p. 79 illus., 51 illus. in color.)
    Ausgabe: 1st ed. 2022.
    Serie: Studies in Systems, Decision and Control 213
    Paralleltitel: Erscheint auch als
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    Paralleltitel: Erscheint auch als Handbook of nature-inspired optimization algorithms: the state of the art ; Volume 2: Solving constrained single objective real-parameter optimization problems
    RVK:
    SK 870
    Schlagwort(e): Computational intelligence. ; Artificial intelligence.
    Kurzfassung: Particle swarm optimization based optimization for in-dustry inspection -- Ant Algorithms: from Drawback Identification to Quality and Speed Improvement -- Fault location techniques based on traveling waves with application in the protection of distribution systems with renewable energy and particle swarm optimization -- Improved Particle Swarm Optimization and Non-Quadratic Penalty Method for Non-Linear Programming Problems with Equality Constraints -- Recent Trends in Face Recognition Using Metaheuristic Optimization.
    Kurzfassung: This book presents recent contributions and significant development, advanced issues, and challenges. In real-world problems and applications, most of the optimization problems involve different types of constraints. These problems are called constrained optimization problems (COPs). The optimization of the constrained optimization problems is considered a challenging task since the optimum solution(s) must be feasible. In their original design, evolutionary algorithms (EAs) are able to solve unconstrained optimization problems effectively. As a result, in the past decade, many researchers have developed a variety of constraint handling techniques, incorporated into (EAs) designs, to counter this deficiency. The main objective for this book is to make available a self-contained collection of modern research addressing the general constrained optimization problems in many real-world applications using nature-inspired optimization algorithms. This book is suitable for a graduate class on optimization, but will also be useful for interested senior students working on their research projects.
    DOI: 10.1007/978-3-031-07516-2
    URL: lizenzpflichtig
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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    Online-Ressource
    BSZ
    MPI Ethno. Forsch.
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