| Tag |
First Indicator |
Second Indicator |
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| LEADER |
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| 001 |
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| 006 |
m o d |
| 007 |
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| 008 |
211013s2021 flua fob 001 0 eng d |
| 005 |
20241111204743.4 |
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|
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|a (OCoLC)on1289817882
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| 040 |
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|a UKAHL
|b eng
|e rda
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|d OCLCO
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|a GBC1H4503
|2 bnb
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| 016 |
7 |
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|a 020364242
|2 Uk
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| 020 |
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|a 9781003245193
|q (electronic book)
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| 020 |
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|a 1003245196
|q (electronic book)
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| 020 |
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|a 9781000520514
|q (electronic book)
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|a 100052051X
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| 020 |
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|a 100052048X
|q (electronic book)
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|a 9781000520484
|q (electronic bk.)
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| 020 |
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|z 9781032150758
|q (hardcover)
|
| 024 |
7 |
|
|a 10.1201/9781003245193
|2 doi
|
| 035 |
|
|
|a (OCoLC)1289817882
|
| 037 |
|
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|a 9781003245193
|b Taylor & Francis
|
| 050 |
|
4 |
|a TA347.A78
|b K35 2021
|
| 072 |
|
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|a BUS
|x 049000
|2 bisacsh
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| 072 |
|
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|a COM
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|a COM
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| 072 |
|
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|a UMB
|2 bicssc
|
| 082 |
0 |
4 |
|a 006.3
|2 23
|
| 049 |
|
|
|a TXAM
|
| 100 |
1 |
|
|a Kale, Ishaan R.,
|e author.
|
| 245 |
1 |
0 |
|a Constraint handling in cohort intelligence algorithm /
|c Ishaan R. Kale, Anand J. Kulkarni.
|
| 264 |
|
1 |
|a Boca Raton, FL :
|b CRC Press,
|c 2021.
|
| 300 |
|
|
|a 1 online resource :
|b illustrations
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
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|a computer
|b c
|2 rdamedia
|
| 338 |
|
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|a online resource
|b cr
|2 rdacarrier
|
| 490 |
0 |
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|a Advances in metaheuristics
|
| 520 |
|
|
|a Mechanical Engineering domain problems are generally complex, consisting of different design variables and constraints. These problems may not be solved using gradient-based optimization techniques. The stochastic nature-inspired optimization techniques have been proposed in this book to efficiently handle the complex problems. The nature-inspired algorithms are classified as bio-inspired, swarm, and physics/chemical-based algorithms. Socio-inspired is one of the subdomains of bio-inspired algorithms, and Cohort Intelligence (CI) models the social tendencies of learning candidates with an inherent goal to achieve the best possible position. In this book, CI is investigated by solving ten discrete variable truss structural problems, eleven mixed variable design engineering problems, seventeen linear and nonlinear constrained test problems and two real-world applications from manufacturing domain. Static Penalty Function (SPF) is also adopted to handle the linear and nonlinear constraints, and limitations in CI and SPF approaches are examined. Constraint Handling in Cohort Intelligence Algorithm is a valuable reference to practitioners working in the industry as well as to students and researchers in the area of optimization methods.
|
| 545 |
0 |
|
|a Ishaan R. Kale is a researcher for the Optimization and Agent Technology Research (OAT Research) Lab. Anand J. Kulkarni is an Associate Professor at the Institute of Artificial Intelligence, MIT World Peace University, India.
|
| 504 |
|
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|a Includes bibliographical references and index.
|
| 650 |
|
0 |
|a Artificial intelligence.
|
| 650 |
|
0 |
|a Computational intelligence.
|
| 650 |
|
0 |
|a Algorithms.
|
| 650 |
|
2 |
|a Artificial Intelligence
|
| 650 |
|
2 |
|a Algorithms
|
| 650 |
|
6 |
|a Intelligence artificielle.
|
| 650 |
|
6 |
|a Intelligence informatique.
|
| 650 |
|
6 |
|a Algorithmes.
|
| 650 |
|
7 |
|a artificial intelligence.
|2 aat
|
| 650 |
|
7 |
|a algorithms.
|2 aat
|
| 650 |
|
7 |
|a BUSINESS & ECONOMICS
|x Operations Research.
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|
| 650 |
|
7 |
|a COMPUTERS
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|2 bisacsh
|
| 650 |
|
7 |
|a COMPUTERS
|x Artificial Intelligence.
|2 bisacsh
|
| 650 |
|
7 |
|a Algorithms
|2 fast
|
| 650 |
|
7 |
|a Artificial intelligence
|2 fast
|
| 650 |
|
7 |
|a Computational intelligence
|2 fast
|
| 655 |
|
7 |
|a Electronic books.
|2 local
|
| 700 |
1 |
|
|a Kulkarni, Anand Jayant,
|e author.
|
| 710 |
2 |
|
|a Taylor & Francis
|
| 758 |
|
|
|i has work:
|a CONSTRAINT HANDLING IN COHORT INTELLIGENCE ALGORITHM (Text)
|1 https://id.oclc.org/worldcat/entity/E39PD3WPTmvvHq6Tg8qxYJQcyd
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|z 9781032150758
|
| 856 |
4 |
0 |
|u http://proxy.library.tamu.edu/login?url=https://www.taylorfrancis.com/books/9781003245193
|z Connect to the full text of this electronic book
|t 0
|
| 955 |
|
|
|a Taylor and Francis MATHnetBASE
|
| 994 |
|
|
|a 92
|b TXA
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| 999 |
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|s 334d374d-a2c9-453b-bcd6-007e5c1f42ae
|i 6713d6c2-fb3b-449c-8c24-997b8809405d
|t 0
|
| 952 |
f |
f |
|a Texas A&M University
|b College Station
|c Electronic Resources
|d Available Online
|t 0
|e TA347.A78 K35 2021
|h Library of Congress classification
|
| 998 |
f |
f |
|a TA347.A78 K35 2021
|t 0
|l Available Online
|