Evolutionary Algorithms in Engineering Design Optimization

ISBN 10: 3036527141 / ISBN 13: 9783036527147

Published by MDPI AG, 2022

About this Item

Evolutionary Algorithms in Engineering Design Optimization | Buch | Gebunden | Englisch | 2022 | MDPI AG | EAN 9783036527147 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

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Bibliographic Details

• Title: Evolutionary Algorithms in Engineering Design Optimization
• Publisher: MDPI AG
• Publication Date: 2022
• Language: English
• ISBN 10: 3036527141
• ISBN 13: 9783036527147
• Binding: Buch
• Condition: Neu
• Dimensions: 250W x 175H x 25D millimeters 250 millimeters width by 175 millimeters height by 25 millimeters depth
• Weight: 985 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc.