Hybrid advanced optimization methods (7 Ergebnisse)

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Zustand: New. KlappentextrnrnMore accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, a.

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Taschenbuch. Zustand: Neu. Neuware - Advanced Optimization Methods with Hybrid Metaheuristic and Intelligent TechniquesOptimization lies at the heart of modern engineering, artificial intelligence, data science, operations research, and industrial decision-making. Advanced Optimization Methods with Hybrid Metaheuristic and Intel…ligent Techniques provides a comprehensive and practical guide to both classical and modern optimization approaches used to solve complex real-world problems.This book takes readers from fundamental optimization concepts to advanced hybrid intelligent algorithms. It covers mathematical modeling, linear and nonlinear programming, dynamic programming, convex optimization, evolutionary computation, swarm intelligence, and state-of-the-art hybrid optimization techniques. Special emphasis is placed on integrating artificial intelligence, machine learning, fuzzy systems, neural networks, and reinforcement learning with optimization frameworks.Designed for undergraduate, postgraduate, and research scholars in engineering, computer science, data science, and operations research, the book combines rigorous theory with practical implementation. Readers will find numerous engineering examples, case studies, solved numerical problems, Python-based implementations, optimization workflows, review questions, programming exercises, and mini-projects that bridge the gap between theory and real-world applications.Key Features- Comprehensive coverage of classical, metaheuristic, and hybrid optimization methods.- Detailed mathematical formulations and optimization theory.- Python implementations using modern scientific computing libraries.- Engineering-focused case studies and industrial applications.- Multi-objective optimization and Pareto-based decision-making techniques.- Integration of AI, machine learning, fuzzy logic, and intelligent systems.- Practice problems, review questions, and project-based learning activities.- Suitable for academic coursework, competitive examinations, research, and industrial practice.Whether you are a student learning optimization for the first time, a researcher exploring advanced algorithms, or a professional seeking practical optimization solutions, this book provides the knowledge, tools, and techniques needed to tackle challenging optimization problems in modern engineering and intelligent systems.

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Taschenbuch. Zustand: Neu. Neuware - More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasona…lity, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers.This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy.