04300nam a22006255i 4500 978-3-031-45561-2 DE-He213 20251030110830.0 cr nn 008mamaa 231106s2024 sz | s |||| 0|eng d 9783031455612 978-3-031-45561-2 10.1007/978-3-031-45561-2 doi TK7895.E42 TK5105.8857 TJF bicssc GPFC bicssc TEC008000 bisacsh TJF thema GPFC thema Cuevas, Erik. author. aut http://id.loc.gov/vocabulary/relators/aut 65714 New Metaheuristic Schemes: Mechanisms and Applications [electronic resource] / by Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XIV, 268 p. 77 illus., 43 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Intelligent Systems Reference Library, 1868-4408 ; 246 Introduction to Metaheuristic Schemes: Characteristics, Properties, and Importance in Solving Optimization Problems -- Exploring the potential of agent systems for metaheuristics -- Dynamic Multimodal Function Optimization: An Evolutionary-Mean Shift Approach -- Trajectory-Driven Metaheuristic Approach using a Second-Order model -- Collaborative Hybrid Grey Wolf Optimizer: Uniting Synchrony and Asynchrony -- Efficient Image Contrast Enhancement by using the Moth Swarm Algorithm. Recently, novel metaheuristic techniques have emerged in response to the limitations of conventional approaches, leading to enhanced outcomes. These new methods introduce interesting mechanisms and innovative collaborative strategies that facilitate the efficient exploration and exploitation of extensive search spaces characterized by numerous dimensions. The objective of this book is to present advancements that discuss novel alternative metaheuristic developments that have demonstrated their effectiveness in tackling various complex problems. This book encompasses a variety of emerging metaheuristic methods and their practical applications. The content is presented from a teaching perspective, making it particularly suitable for undergraduate and postgraduate students in fields such as science, electrical engineering, and computational mathematics. The book aligns well with courses in artificial intelligence, electrical engineering, and evolutionary computation. Furthermore, the material offers valuable insights to researchers within the metaheuristic and engineering communities. Similarly, engineering practitioners unfamiliar with metaheuristic computation concepts will recognize the pragmatic value of the discussed techniques. These methods transcend mere theoretical tools that have been adapted to effectively address the significant real-world problems commonly encountered in engineering domains. Cooperating objects (Computer systems). 64303 Machine learning. 55059 Artificial intelligence. 58441 Computer science. 58552 Cyber-Physical Systems. 65638 Machine Learning. 55059 Artificial Intelligence. 58441 Computer Science. 58552 Zaldívar, Daniel. author. aut http://id.loc.gov/vocabulary/relators/aut 67032 Pérez-Cisneros, Marco. author. aut http://id.loc.gov/vocabulary/relators/aut 67033 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031455605 Printed edition: 9783031455629 Printed edition: 9783031455636 Intelligent Systems Reference Library, 1868-4408 ; 246 67034 https://doi.org/10.1007/978-3-031-45561-2 ZDB-2-ENG ZDB-2-SXE 56510 56510