Advances in metaheuristics algorithms : methods and applications /

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Bibliographic Details
Author / Creator:Cuevas, Erik, author.
Imprint:Cham, Switzerland : Springer, 2018.
Description:1 online resource (xiv, 218 pages) : illustrations (some color)
Language:English
Series:Studies in computational intelligence, 1860-949X ; volume 775
Studies in computational intelligence ; v. 775.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11654341
Hidden Bibliographic Details
Other authors / contributors:Zaldívar, Daniel, author.
Pérez-Cisneros, Marco, author.
ISBN:9783319893099
3319893092
9783319893105
3319893106
9783030077365
3030077365
9783319893082
3319893084
Digital file characteristics:text file PDF
Notes:Includes bibliographical references.
Online resource; title from PDF title page (SpringerLink, viewed April 17, 2018).
Summary:This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip those of the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems.
Other form:Print version: Cuevas, Erik. Advances in metaheuristics algorithms. Cham, Switzerland : Springer, 2018 3319893084 9783319893082
Standard no.:10.1007/978-3-319-89309-9

MARC

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264 1 |a Cham, Switzerland :  |b Springer,  |c 2018. 
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490 1 |a Studies in computational intelligence,  |x 1860-949X ;  |v volume 775 
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505 0 |a Introduction -- The metaheuristic algorithm of the social-spider -- Calibration of Fractional Fuzzy Controllers by using the Social-spider method -- The metaheuristic algorithm of the Locust-search -- Identification of fractional chaotic systems by using the Locust Search Algorithm -- The States of Matter Search (SMS) -- Multimodal States of Matter search -- Metaheuristic algorithms based on Fuzzy Logic. 
520 |a This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip those of the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems. 
650 0 |a Mathematical optimization.  |0 http://id.loc.gov/authorities/subjects/sh85082127 
650 0 |a Heuristic programming.  |0 http://id.loc.gov/authorities/subjects/sh85060557 
650 7 |a Artificial intelligence.  |2 bicssc 
650 7 |a Computers  |x Intelligence (AI) & Semantics.  |2 bisacsh 
650 7 |a Heuristic programming.  |2 fast  |0 (OCoLC)fst00955815 
650 7 |a Mathematical optimization.  |2 fast  |0 (OCoLC)fst01012099 
655 4 |a Electronic books. 
700 1 |a Zaldívar, Daniel,  |e author. 
700 1 |a Pérez-Cisneros, Marco,  |e author.  |0 http://id.loc.gov/authorities/names/ns2011000627 
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