http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Metaheuristic and machine learning optimization strategies for complex systems
"In contemporary engineering domains, optimization and decision-making issues are crucial. Given the vast amounts of available data, processing times and memory usage can be substantial. Developing and implementing novel heuristic algorithms is time-consuming, yet even minor improvements in solutions can significantly reduce computational costs. In such scenarios, the creation of heuristics and metaheuristic algorithms has proven advantageous. The convergence of machine learning and metaheuristic algorithms offers a promising approach to address these challenges.Metaheuristic and Machine Learning Optimization Strategies for Complex Systems covers all areas of comprehensive information about hyper-heuristic models, hybrid meta-heuristic models, nature-inspired computing models, and meta-heuristic models. The key contribution of this book is the construction of a hyper-heuristic approach for any general problem domain from a meta-heuristic algorithm. Covering topics such as cloud computing, internet of things, and performance evaluation, this book is an essential resource for researchers, postgraduate students, educators, data scientists, machine learning engineers, software developers and engineers, policy makers, and more."--
Chapter 1. Experimental investigation and comparative analysis of an efficient machine learning algorithm for distribution system reconfiguration -- Chapter 2. Integrated approach for network intrusion detection: combining KNN algorithm, Karhunen-Loève transform, and genetic algorithm optimization -- Chapter 3. Machine learning for the detection of uniform resource locator phishing -- Chapter 4. Machine learning in industrial IoT applications for safety, security, asset localization, quality assurance, and sustainability in smart production -- Chapter 5. Machine learning in industry IoT (IIoT) -- Chapter 6. Machine learning in IoT for smart home automation -- Chapter 7. Meta-heuristics algorithm for computer communications -- Chapter 8. Meta-heuristic and machine learning modelling and optimizing complex systems -- Chapter 9. Meta-heuristic algorithms for optimal sizing of HRES -- Chapter 10. Meta-heuristic algorithms for optimal sizing of hybrid renewable energy systems -- Chapter 11. Meta-heuristic algorithms for optimal sizing of hybrid renewable energy systems -- Chapter 12. Metaheuristic algorithms for robot path planning: a comprehensive exploration -- Chapter 13. Meta-heuristic optimization for enhanced sensor-based health monitoring in cloud computing environments -- Chapter 14. Meta-heuristics and machine learning applications in complex systems -- Chapter 15. Metaheuristics and machine learning convergence: a comprehensive survey and future prospects -- Chapter 16. Optimizing optical fiber path in wavelength division multiplexing networks using particle swarm optimization -- Chapter 17. Performance evaluation of simulation-driven metaheuristic algorithms: efficiency and effectiveness measures in the search for quality solutions.
Thanigaivelan R., Suchithra M., Kaliappan S., T. Mothilal, editors.
Includes bibliographical references and index.
Simulation-Driven Metaheuristic Algorithms.