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008 210709s2021 flua ob 001 0 eng d
040 _aOCoLC-P
_beng
_erda
_epn
_cOCoLC-P
020 _a9781000434255
_q(ePub ebook)
020 _a1000434257
020 _a9781000434248
_q(PDF ebook)
020 _a1000434249
020 _a9781003162841
_q(ebook)
020 _a1003162843
020 _z9780367753030 (hbk.)
020 _z0367753030
024 7 _a10.1201/9781003162841
_2doi
035 _a(OCoLC)1264400287
035 _a(OCoLC-P)1264400287
050 4 _aTA168
072 7 _aCOM
_x012040
_2bisacsh
072 7 _aCOM
_x044000
_2bisacsh
072 7 _aCOM
_x059000
_2bisacsh
072 7 _aUMB
_2bicssc
082 0 4 _a620.0042028563
_223
245 0 4 _aThe handbook of AI-based metaheuristics /
_cedited by Anand Kulkarni, Patrick Siarry.
250 _a1st.
264 1 _aBoca Raton :
_bCRC Press,
_c2021.
300 _a1 online resource :
_billustrations (black and white).
336 _atext
_2rdacontent
336 _astill image
_2rdacontent
337 _acomputer
_2rdamedia
338 _aonline resource
_2rdacarrier
490 0 _aAdvances in metaheuristics
500 _a<strong><em> </em></strong> <p>Section I Bio-Inspired Methods</p><p>Chapter 1 Brain Storm Optimization Algorithm</p><i><p>Marwa Sharawi, Mohammadreza Gholami,</p><p>and Mohammed El-Abd</p></i><b></b><p><b>Chapter 2 </b>Fish School Search: Account for the First Decade</p><i><p>Carmelo José Abanez Bastos-Filho, Fernando Buarque de Lima-Neto,</p><p>Anthony José da Cunha Carneiro Lins, Marcelo Gomes Pereira de</p><p>Lacerda, Mariana Gomes da Motta Macedo, Clodomir Joaquim de</p><p>Santana Junior, Hugo Valadares Siqueira, Rodrigo Cesar Lira da Silva,</p><p>Hugo Amorim Neto, Breno Augusto de Melo Menezes, Isabela Maria</p><p>Carneiro Albuquerque, João Batista Monteiro Filho, Murilo Rebelo Pontes,</p><p>and João Luiz Vilar Dias</p></i><b></b><p><b>Chapter 3 </b>Marriage in Honey Bees Optimization in Continuous Domains</p><i><p>Jing Liu, Sreenatha Anavatti, Matthew Garratt,</p><p>and Hussein A. Abbass</p></i><b></b><p><b>Chapter 4 </b>Structural Optimization Using Genetic Algorithm...</p><i><p>Ravindra Desai</p><b><p>Section II Physics and Chemistry-Based Methods</p></b></i><b></b><p><b>Chapter 5 </b>Gravitational Search Algorithm: Theory, Literature Review,</p><p>and Applications</p><i><p>Amin Hashemi, Mohammad Bagher Dowlatshahi,</p><p>and Hossein Nezamabadi-pour</p></i><b></b><p><b>Chapter 6 </b>Stochastic Diffusion Search</p><i><p>Andrew Owen Martin</p></i><p>BK-TandF-KULKARNI-9780367753030-210197-FM.indd 7 22/06/21 2:03 PM</p><b></b><p><b>viii </b>Contents</p><b><i><p>Section III Socio-inspired Methods</p></i></b><p><b>Chapter 7 </b>The League Championship Algorithm: Applications and Extensions</p><p><i>Ali Husseinzadeh Kashan, Alireza Balavand, Somayyeh Karimiyan,</i></p><i><p>and Fariba Soleimani</p></i><b></b><p><b>Chapter 8 </b>Cultural Algorithms for Optimization</p><i><p>Carlos Artemio Coello Coello and Ma Guadalupe Castillo Tapia</p></i><b></b><p><b>Chapter 9 </b>Application of Teaching-Learning-Based Optimization</p><p>on Solving of Time Cost Optimization Problems</p><i><p>Vedat Toğan, Tayfun Dede, and Hasan Basri Başağa</p></i><b></b><p><b>Chapter 10 </b>Social Learning Optimization</p><i><p>Yue-Jiao Gong</p></i><b></b><p><b>Chapter 11 </b>Constraint Handling in Multi-Cohort Intelligence Algorithm</p><p><i>Apoorva S. Shastri and Anand J. Kulkarni</i></p><i><b><p>Section IV Swarm-Based Methods</p></b></i><b></b><p><b>Chapter 12 </b>Bee Colony Optimization and Its Applications</p><i><p>Dušan Teodorović, Tatjana Davidović, Milica Šelmić,</p><p>and Miloš Nikolić</p></i><b></b><p><b>Chapter 13 </b>A Bumble Bees Mating Optimization Algorithm for the Location</p><p>Routing Problem with Stochastic Demands</p><i><p>Magdalene Marinaki and Yannis Marinakis</p></i><b></b><p><b>Chapter 14 </b>A Glowworm Swarm Optimization Algorithm for the Multi-Objective</p><p>Energy Reduction Multi-Depot Vehicle Routing Problem</p><i><p>Emmanouela Rapanaki, Iraklis-Dimitrios Psychas,</p><p>Magdalene Marinaki, and Yannis Marinakis</p></i><b></b><p><b>Chapter 15 </b>Monarch Butterfly Optimization</p><i><p>Liwen Xie and Gai-Ge Wang</p></i>
520 _aAt the heart of the optimization domain are mathematical modeling of the problem and the solution methodologies. The problems are becoming larger and with growing complexity. Such problems are becoming cumbersome when handled by traditional optimization methods. This has motivated researchers to resort to artificial intelligence (AI)-based, nature-inspired solution methodologies or algorithms. The Handbook of AI-based Metaheuristics provides a wide-ranging reference to the theoretical and mathematical formulations of metaheuristics, including bio-inspired, swarm-based, socio-cultural, and physics-based methods or algorithms; their testing and validation, along with detailed illustrative solutions and applications; and newly devised metaheuristic algorithms. Thiswill be avaluable reference for researchers in industry and academia, as well as for all Master's and PhD students working in the metaheuristics and applications domains.
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aSystems engineering
_xData processing.
_914018
650 0 _aArtificial intelligence.
_93407
650 0 _aMetaheuristics.
_94799
650 0 _aHeuristic algorithms.
_94883
650 0 _aMathematical optimization.
_94112
650 7 _aCOMPUTERS / Computer Graphics / Game Programming & Design
_2bisacsh
_912289
650 7 _aCOMPUTERS / Neural Networks
_2bisacsh
_914874
650 7 _aCOMPUTERS / Computer Engineering
_2bisacsh
_94770
700 1 _aKulkarni, Anand Jayant,
_eeditor.
_919677
700 1 _aSiarry, Patrick,
_eeditor.
_919678
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9781003162841
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942 _cEBK
999 _c72164
_d72164