LEADER 04044nam 22006375 450 001 9910760295203321 005 20240313114706.0 010 $a3-031-45561-4 024 7 $a10.1007/978-3-031-45561-2 035 $a(MiAaPQ)EBC30870265 035 $a(Au-PeEL)EBL30870265 035 $a(DE-He213)978-3-031-45561-2 035 $a(CKB)28781947400041 035 $a(EXLCZ)9928781947400041 100 $a20231106d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNew Metaheuristic Schemes: Mechanisms and Applications /$fby Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (280 pages) 225 1 $aIntelligent Systems Reference Library,$x1868-4408 ;$v246 311 08$aPrint version: Cuevas, Erik New Metaheuristic Schemes: Mechanisms and Applications Cham : Springer,c2023 9783031455605 327 $aIntroduction 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. 330 $aRecently, novel metaheuristic techniques have emerged in response to the limitations of conventional approaches, leading to enhanced outcomes. These new methods introduce interesting mechanisms and innovative collaborative strategies that facilitate the efficient exploration and exploitation of extensive search spaces characterized by numerous dimensions. The objective of this book is to present advancements that discuss novel alternative metaheuristic developments that have demonstrated their effectiveness in tackling various complex problems. This book encompasses a variety of emerging metaheuristic methods and their practical applications. The content is presented from a teaching perspective, making it particularly suitable for undergraduate and postgraduate students in fields such as science, electrical engineering, and computational mathematics. The book aligns well with courses in artificial intelligence, electrical engineering, and evolutionary computation. Furthermore, the material offers valuable insights to researchers within the metaheuristic and engineering communities. Similarly, engineering practitioners unfamiliar with metaheuristic computation concepts will recognize the pragmatic value of the discussed techniques. These methods transcend mere theoretical tools that have been adapted to effectively address the significant real-world problems commonly encountered in engineering domains. 410 0$aIntelligent Systems Reference Library,$x1868-4408 ;$v246 606 $aCooperating objects (Computer systems) 606 $aMachine learning 606 $aArtificial intelligence 606 $aComputer science 606 $aCyber-Physical Systems 606 $aMachine Learning 606 $aArtificial Intelligence 606 $aComputer Science 615 0$aCooperating objects (Computer systems) 615 0$aMachine learning. 615 0$aArtificial intelligence. 615 0$aComputer science. 615 14$aCyber-Physical Systems. 615 24$aMachine Learning. 615 24$aArtificial Intelligence. 615 24$aComputer Science. 676 $a621.38 700 $aCuevas$b Erik$0761169 701 $aZaldívar$b Daniel$0761372 701 $aPérez-Cisneros$b Marco$0761373 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910760295203321 996 $aNew Metaheuristic Schemes: Mechanisms and Applications$93601779 997 $aUNINA