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Inhaltsangabe
Metaheuristic Optimization Algorithms: Optimizers, Analysis, and Applications presents the most recent optimization algorithms and their applications across a wide range of scientific and engineering research fields. The book provides readers with a comprehensive overview of eighteen optimization algorithms to address this complex data, including Particle Swarm Optimization Algorithm, Arithmetic Optimization Algorithm, Whale Optimization Algorithm, and Marine Predators Algorithm, along with new and emerging methods such as Aquila Optimizer, Quantum Approximate Optimization Algorithm, Manta-Ray Foraging Optimization Algorithm, and Gradient Based Optimizer, among others. Each chapter includes an introduction to the modeling concepts used to create the algorithm that is followed by the mathematical and procedural structure of the algorithm, associated pseudocode, and real-world case studies.
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Über die Autorin bzw. den Autor
Dr. Laith Abualigah is an Associate Professor at Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Jordan. He is also a distinguished researcher at the School of Computer Science, Universiti Sains Malaysia. His main research interests focus on Arithmetic Optimization Algorithms (AOA), Bio-inspired Computing, Nature-inspired Computing, Swarm Intelligence, Artificial Intelligence, Meta-heuristic Modeling, as well as Optimization Algorithms, Evolutionary Computations, Information Retrieval, Text Clustering, Feature Selection, Combinatorial Problems, Optimization, Advanced Machine Learning, Big Data, and Natural Language Processing. Dr. Abualigah currently serves as Associate Editor of the Journal of Cluster Computing (Springer), the Journal of Soft Computing (Springer), and Journal of King Saud University - Computer and Information Sciences (Elsevier).
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Metaheuristic Optimization Algorithms: Optimizers, Analysis, and Applications presents the most recent optimization algorithms and their applications across a wide range of scientific and engineering research and fields. Metaheuristic Optimization Algorithms have become indispensable tools, with applications in data analysis, text mining, classification problems, computer vision, image analysis, pattern recognition, medicine, and many others. Most complex systems problems involve a continuous flow of data which makes it impossible to manage and analyze manually. The outcome depends on the processing of high-dimensional data, most of it irregular and unordered, present in various forms such as text, images, videos, audio, and graphics. The authors of Meta-Heuristic Optimization Algorithms provide readers with a comprehensive overview of eighteen optimization algorithms to address this complex data, including Particle Swarm Optimization Algorithm, Arithmetic Optimization Algorithm, Whale Optimization Algorithm, and Marine Predators Algorithm, along with new and emerging methods such as Aquila Optimizer, Quantum Approximate Optimization Algorithm, Manta-Ray Foraging Optimization Algorithm, and Gradient Based Optimizer, among others. Each chapter includes an introduction to the modeling concepts used to create the algorithm, followed by the mathematical and procedural structure of the algorithm, associated pseudocode, and real-world case studies to demonstrate how each algorithm can be applied to a variety of scientific and engineering solutions.
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Metaheuristic Optimization Algorithms: Optimizers, Analysis, and Applications
Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
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Metaheuristic Optimization Algorithms: Optimizers, Analysis, and Applications
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
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Paperback. Zustand: Brand New. 250 pages. 9.25x7.50x0.63 inches. In Stock. Artikel-Nr. zk0443139253
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