04367nam 22006375 450 991098613610332120250307115229.09783031810138303181013910.1007/978-3-031-81013-8(MiAaPQ)EBC31947415(Au-PeEL)EBL31947415(CKB)37788184300041(OCoLC)1507695414(DE-He213)978-3-031-81013-8(EXLCZ)993778818430004120250307d2025 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierOptimization Strategies: A Decade of Metaheuristic Algorithm Development /by Erik Cuevas, Angel Chavarin-Fajardo, Cesar Ascencio-Piña, Sonia Garcia-De-Lira1st ed. 2025.Cham :Springer Nature Switzerland :Imprint: Springer,2025.1 online resource (804 pages)Intelligent Systems Reference Library,1868-4408 ;2669783031810121 3031810120 1.Introductory concepts of metaheuristic techniques -- 2.An algorithm for global optimization inspired by collective animal behavior -- 3.A swarm optimization algorithm inspired in the behavior of the social-spider -- 4.An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation -- 5.Harnessing Locust Swarm Dynamics for Optimization Algorithms -- 6.Improving Function Evaluation Efficiency with an Enhanced Evolutionary Algorithm -- 7.A Fuzzy Logic-Inspired Metaheuristic Method for Enhanced Optimization -- 8.Modeling Optimization Techniques Inspired by Yellow Saddle Goatfish Behavior -- 9.An optimization algorithm guided by a machine learning approach -- 10.An improved Simulated Annealing algorithm based on ancient metallurgy techniques -- 11.Agent-based modeling approaches as metaheuristic methods -- 12.Evolutionary-Mean shift algorithm for dynamic multimodal function optimization.This book is to explore the development of metaheuristic algorithms over the past decade, focusing on key advancements in their components and structural features, which have driven progress in search techniques. This analysis aims to provide readers with a thorough understanding of the fundamental aspects of these methods, which are essential for their practical application. To offer a broad perspective on the evolution of metaheuristic algorithms, this book reviews 11 specific algorithms developed by the evolutionary computation group at the University of Guadalajara over the past 10 years. These algorithms illustrate the most significant mechanisms and structures discussed in the academic and research communities during their development. By studying these examples, readers will gain valuable insights into the innovative methods and strategic improvements that have shaped the field. The book is designed from a teaching standpoint, making it suitable for undergraduate and postgraduate students in science, electrical engineering, or computational mathematics. Moreover, engineering practitioners unfamiliar with metaheuristic computation will appreciate how these techniques have been adapted to address complex real-world engineering problems, moving beyond theoretical constructs.Intelligent Systems Reference Library,1868-4408 ;266Computational intelligenceArtificial intelligenceEngineeringData processingComputational IntelligenceArtificial IntelligenceData EngineeringComputational intelligence.Artificial intelligence.EngineeringData processing.Computational Intelligence.Artificial Intelligence.Data Engineering.006.3Cuevas Erik761169Chavarin-Fajardo Angel1790725Ascencio-Piña Cesar1790726Garcia-De-Lira Sonia1790727MiAaPQMiAaPQMiAaPQBOOK9910986136103321Optimization Strategies: A Decade of Metaheuristic Algorithm Development4349903UNINA