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Advances in metaheuristics algorithm...

Cuevas, Erik.

Advances in metaheuristics algorithmsmethods and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advances in metaheuristics algorithmsby Erik Cuevas, Daniel Zaldivar, Marco Perez-Cisneros.
其他題名:	methods and applications /
作者:	Cuevas, Erik.
其他作者:	Zaldivar, Daniel.
出版者:	Cham :Springer International Publishing :2018.
面頁冊數:	xiv, 218 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Mathematical optimization.
電子資源:	http://dx.doi.org/10.1007/978-3-319-89309-9
ISBN:	9783319893099$q(electronic bk.)

Advances in metaheuristics algorithmsmethods and applications /
Cuevas, Erik.

Advances in metaheuristics algorithmsmethods and applications /[electronic resource] :by Erik Cuevas, Daniel Zaldivar, Marco Perez-Cisneros. - Cham :Springer International Publishing :2018. - xiv, 218 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.7751860-949X ;. - Studies in computational intelligence ;v. 216..

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.

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.

ISBN: 9783319893099$q(electronic bk.)

Standard No.: 10.1007/978-3-319-89309-9doiSubjects--Topical Terms:

183292
Mathematical optimization.

LC Class. No.: QA402.5 / .C848 2018

Dewey Class. No.: 519.6

Advances in metaheuristics algorithmsmethods and applications /
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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.
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