Multi-Objective Optimization using Artificial Intelligence Techniques

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69,54 

SpringerBriefs in Applied Sciences and Technology – SpringerBriefs in Computational Intelligence

ISBN: 3030248348
ISBN 13: 9783030248345
Autor: Mirjalili, Seyedali/Dong, Jin Song
Verlag: Springer Verlag GmbH
Umfang: xi, 58 S., 1 s/w Illustr., 25 farbige Illustr., 58 p. 26 illus., 25 illus. in color.
Erscheinungsdatum: 07.08.2019
Auflage: 1/2020
Produktform: Kartoniert
Einband: Kartoniert

Offers a concise guide to the most important multi-objective optimization techniquesDiscusses in detail several experimental resultsThe source codes for all the proposed algorithms are provided on a dedicated webpage

Artikelnummer: 7634386 Kategorie:

Beschreibung

This book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them. It then presents an in-depth explanations of the theory, literature review, and applications of several widely-used algorithms, such as Multi-objective Particle Swarm Optimizer, Multi-Objective Genetic Algorithm and Multi-objective GreyWolf Optimizer Due to the simplicity of the techniques and flexibility, readers from any field of study can employ them for solving multi-objective optimization problem. The book provides the source codes for all the proposed algorithms on a dedicated webpage.

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E-Mail: juergen.hartmann@springer.com

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