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" Advances in metaheuristics algorithms : "
by Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros.
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BL
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| Record Number
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865891
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| Main Entry
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Cuevas, Erik
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| Title & Author
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Advances in metaheuristics algorithms : : methods and applications /\ by Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros.
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| Publication Statement
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Cham, Switzerland :: Springer,, 2018.
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| Series Statement
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Studies in computational intelligence,; volume 775
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| Page. NO
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1 online resource (xiv, 218 pages) :: illustrations (some color)
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| ISBN
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3030077365
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: 3319893092
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: 3319893106
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: 9783030077365
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: 9783319893099
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: 9783319893105
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3319893084
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9783319893082
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| Bibliographies/Indexes
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Includes bibliographical references.
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| Contents
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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.
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| Abstract
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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.
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| Subject
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Heuristic programming.
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Mathematical optimization.
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Artificial intelligence.
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Computers-- Intelligence (AI) Semantics.
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Heuristic programming.
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Mathematical optimization.
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| Dewey Classification
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519.6
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| LC Classification
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QA402.5
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| Added Entry
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Pérez-Cisneros, Marco
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Zaldívar, Daniel
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