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010   $a3-030-16936-7
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035   $a(MiAaPQ)EBC5771270
035   $a(DE-He213)978-3-030-16936-7
035   $a(PPN)236523600
035   $a(EXLCZ)994100000008153893
100   $a20190508d2019      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aMathematical Foundations of Nature-Inspired Algorithms /$fby Xin-She Yang, Xing-Shi He
205   $a1st ed. 2019.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019.
215   $a1 online resource (114 pages)
225 1 $aSpringerBriefs in Optimization,$x2190-8354
311   $a3-030-16935-9 
327   $a1 Introduction to Optimization -- 2 Nature-Inspired Algorithms -- 3 Mathematical Foundations -- 4 Mathematical Analysis I -- 5 Mathematical Analysis II.
330   $aThis book presents a systematic approach to analyze nature-inspired algorithms. Beginning with an introduction to optimization methods and algorithms, this book moves on to provide a unified framework of mathematical analysis for convergence and stability. Specific nature-inspired algorithms include: swarm intelligence, ant colony optimization, particle swarm optimization, bee-inspired algorithms, bat algorithm, firefly algorithm, and cuckoo search. Algorithms are analyzed from a wide spectrum of theories and frameworks to offer insight to the main characteristics of algorithms and understand how and why they work for solving optimization problems. In-depth mathematical analyses are carried out for different perspectives, including complexity theory, fixed point theory, dynamical systems, self-organization, Bayesian framework, Markov chain framework, filter theory, statistical learning, and statistical measures. Students and researchers in optimization, operations research, artificial intelligence, data mining, machine learning, computer science, and management sciences will see the pros and cons of a variety of algorithms through detailed examples and a comparison of algorithms.
410  0$aSpringerBriefs in Optimization,$x2190-8354
606   $aMathematical optimization
606   $aNumerical analysis
606   $aMarkov processes
606   $aAlgorithms
606   $aOptimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26008
606   $aNumerical Analysis$3https://scigraph.springernature.com/ontologies/product-market-codes/M14050
606   $aMarkov model$3https://scigraph.springernature.com/ontologies/product-market-codes/M27010
606   $aAlgorithms$3https://scigraph.springernature.com/ontologies/product-market-codes/M14018
615  0$aMathematical optimization.
615  0$aNumerical analysis.
615  0$aMarkov processes.
615  0$aAlgorithms.
615 14$aOptimization.
615 24$aNumerical Analysis.
615 24$aMarkov model.
615 24$aAlgorithms.
676   $a004.678015118
676   $a004.678
700   $aYang$b Xin-She$4aut$4http://id.loc.gov/vocabulary/relators/aut$0781375
702   $aHe$b Xing-Shi$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910338246303321
996   $aMathematical Foundations of Nature-Inspired Algorithms$92507101
997   $aUNINA