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100 1 _aCuevas, Erik.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aNew Metaheuristic Schemes: Mechanisms and Applications
_h[electronic resource] /
_cby Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXIV, 268 p. 77 illus., 43 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aIntelligent Systems Reference Library,
_x1868-4408 ;
_v246
505 0 _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.
520 _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.
541 _fUABC ;
_cPerpetuidad
650 0 _aCooperating objects (Computer systems).
650 0 _aMachine learning.
650 0 _aArtificial intelligence.
650 0 _aComputer science.
650 1 4 _aCyber-Physical Systems.
650 2 4 _aMachine Learning.
650 2 4 _aArtificial Intelligence.
650 2 4 _aComputer Science.
700 1 _aZaldívar, Daniel.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aPérez-Cisneros, Marco.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031455605
776 0 8 _iPrinted edition:
_z9783031455629
776 0 8 _iPrinted edition:
_z9783031455636
830 0 _aIntelligent Systems Reference Library,
_x1868-4408 ;
_v246
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-45561-2
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cLIBRO_ELEC
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