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001432583 001__ 1432583
001432583 003__ OCoLC
001432583 005__ 20230309003452.0
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001432583 019__ $$a1241066568
001432583 020__ $$a9783030611118$$q(electronic bk.)
001432583 020__ $$a3030611116$$q(electronic bk.)
001432583 020__ $$z9783030611101
001432583 0247_ $$a10.1007/978-3-030-61111-8$$2doi
001432583 035__ $$aSP(OCoLC)1223094041
001432583 040__ $$aEBLCP$$beng$$erda$$epn$$cEBLCP$$dGW5XE$$dOCLCO$$dOCLCF$$dDKU$$dOCLCQ$$dOCLCO$$dCOM$$dOCLCO$$dOCLCQ
001432583 049__ $$aISEA
001432583 050_4 $$aQA76.9.A43
001432583 08204 $$a006.3$$223
001432583 1001_ $$aOkwu, Modestus O.,$$eauthor.
001432583 24510 $$aMetaheuristic optimization :$$bnature-inspired algorithms swarm and computational intelligence, theory and applications /$$cModestus O. Okwu, Lagouge K. Tartibu.
001432583 264_1 $$aCham :$$bSpringer,$$c[2021]
001432583 300__ $$a1 online resource (196 pages)
001432583 336__ $$atext$$btxt$$2rdacontent
001432583 337__ $$acomputer$$bc$$2rdamedia
001432583 338__ $$aonline resource$$bcr$$2rdacarrier
001432583 347__ $$atext file
001432583 347__ $$bPDF
001432583 4901_ $$aStudies in computational intelligence ;$$vvolume 927
001432583 504__ $$aIncludes bibliographical references.
001432583 5050_ $$aIntroduction To Optimization -- Particle Swarm Optimisation -- Artificial Bee Colony Algorithm -- Ant Colony Algorithm -- Grey Wolf Optimizer -- Whale Optimization Algorithm -- Bat Algorithm -- Ant Lion Optimization Algorithm -- Grasshopper Optimisation Algorithm (Goa) -- Moths-Flame Optimization Algorithm -- Genetic Algorithm -- Artificial Neural Network -- Future of Nature Inspired Algorithm, Swarm and Computational Intelligence.
001432583 506__ $$aAccess limited to authorized users.
001432583 520__ $$aThis book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of converging power and benefits. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm, ant lion optimization algorithm, grasshopper optimization algorithm, butterfly optimization algorithm and others. The algorithms have been arranged in chapters to help readers gain better insight into nature-inspired systems and swarm intelligence. All the MATLAB codes have been provided in the appendices of the book to enable readers practice how to solve examples included in all sections. This book is for experts in Engineering and Applied Sciences, Natural and Formal Sciences, Economics, Humanities and Social Sciences.
001432583 588__ $$aDescription based on print version record.
001432583 650_0 $$aMetaheuristics.
001432583 650_0 $$aComputational intelligence.
001432583 650_6 $$aMétaheuristiques.
001432583 650_6 $$aIntelligence informatique.
001432583 655_0 $$aElectronic books.
001432583 7001_ $$aTartibu, Lagouge K.,$$eauthor.
001432583 77608 $$iPrint version:$$aOkwu, Modestus O.$$tMetaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications.$$dCham : Springer International Publishing AG, ©2021$$z9783030611101
001432583 830_0 $$aStudies in computational intelligence ;$$vv. 927.
001432583 852__ $$bebk
001432583 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-61111-8$$zOnline Access$$91397441.1
001432583 909CO $$ooai:library.usi.edu:1432583$$pGLOBAL_SET
001432583 980__ $$aBIB
001432583 980__ $$aEBOOK
001432583 982__ $$aEbook
001432583 983__ $$aOnline
001432583 994__ $$a92$$bISE