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Metaheuristics: Outlines, MATLAB Codes and Examples [electronic resource] / by Ali Kaveh, Taha Bakhshpoori.

By: Kaveh, Ali [author.].
Contributor(s): Bakhshpoori, Taha [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Cham : Springer International Publishing : Imprint: Springer, 2019Edition: 1st ed. 2019.Description: XII, 190 p. 116 illus., 112 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030040673.Subject(s): Engineering mathematics | Engineering—Data processing | Mathematical optimization | Mechanical engineering | Building construction | Mathematical and Computational Engineering Applications | Optimization | Mechanical Engineering | Solid ConstructionAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 620 Online resources: Click here to access online
Contents:
Introduction -- Preliminaries and frameworks -- Artificial bee colony algorithm -- Big bang big crunch algorithm -- Teaching Learning Based Optimization Algorithm -- Imperialist Competitive Algorithm -- Cuckoo search -- Charged system search Algorithm -- Ray Optimization Algorithm -- Colliding Bodies Optimization Algorithm -- Tug-of-war optimization Algorithm -- Water Evaporation Optimization Algorithm -- Vibrating particles system algorithm -- Cyclical parthenogenesis algorithm -- Thermal exchange optimizaton algorithm.
In: Springer Nature eBookSummary: The book presents eight well-known and often used algorithms besides nine newly developed algorithms by the first author and his students in a practical implementation framework. Matlab codes and some benchmark structural optimization problems are provided. The aim is to provide an efficient context for experienced researchers or readers not familiar with theory, applications and computational developments of the considered metaheuristics. The information will also be of interest to readers interested in application of metaheuristics for hard optimization, comparing conceptually different metaheuristics and designing new metaheuristics.
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Introduction -- Preliminaries and frameworks -- Artificial bee colony algorithm -- Big bang big crunch algorithm -- Teaching Learning Based Optimization Algorithm -- Imperialist Competitive Algorithm -- Cuckoo search -- Charged system search Algorithm -- Ray Optimization Algorithm -- Colliding Bodies Optimization Algorithm -- Tug-of-war optimization Algorithm -- Water Evaporation Optimization Algorithm -- Vibrating particles system algorithm -- Cyclical parthenogenesis algorithm -- Thermal exchange optimizaton algorithm.

The book presents eight well-known and often used algorithms besides nine newly developed algorithms by the first author and his students in a practical implementation framework. Matlab codes and some benchmark structural optimization problems are provided. The aim is to provide an efficient context for experienced researchers or readers not familiar with theory, applications and computational developments of the considered metaheuristics. The information will also be of interest to readers interested in application of metaheuristics for hard optimization, comparing conceptually different metaheuristics and designing new metaheuristics.

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