|
|
|
|
| LEADER |
05491nma a2201189 u 4500 |
| 001 |
EB002043820 |
| 003 |
EBX01000000000000001187486 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
220822 ||| eng |
| 020 |
|
|
|a books978-3-0365-2715-4
|
| 020 |
|
|
|a 9783036527147
|
| 020 |
|
|
|a 9783036527154
|
| 100 |
1 |
|
|a Greiner, David
|
| 245 |
0 |
0 |
|a Evolutionary Algorithms in Engineering Design Optimization
|h Elektronische Ressource
|
| 260 |
|
|
|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2022
|
| 300 |
|
|
|a 1 electronic resource (314 p.)
|
| 653 |
|
|
|a quality control
|
| 653 |
|
|
|a mutation-selection
|
| 653 |
|
|
|a machine learning
|
| 653 |
|
|
|a Pareto front
|
| 653 |
|
|
|a performance metrics
|
| 653 |
|
|
|a Fractional Order Proportional-Integral-Derivative controller
|
| 653 |
|
|
|a distance-based
|
| 653 |
|
|
|a genetic algorithm
|
| 653 |
|
|
|a preventive maintenance scheduling
|
| 653 |
|
|
|a unscented transformation
|
| 653 |
|
|
|a trajectory optimisation
|
| 653 |
|
|
|a accuracy levels
|
| 653 |
|
|
|a optimal design
|
| 653 |
|
|
|a design
|
| 653 |
|
|
|a beam improvements
|
| 653 |
|
|
|a Automatic Voltage Regulation system
|
| 653 |
|
|
|a multi-objective optimisation
|
| 653 |
|
|
|a preference in multi-objective optimization
|
| 653 |
|
|
|a artificial neural networks (ANN) limited training data
|
| 653 |
|
|
|a aeroacoustics
|
| 653 |
|
|
|a min-max optimization
|
| 653 |
|
|
|a neural networks
|
| 653 |
|
|
|a History of engineering & technology / bicssc
|
| 653 |
|
|
|a parameter optimization
|
| 653 |
|
|
|a Gough-Stewart
|
| 653 |
|
|
|a Technology: general issues / bicssc
|
| 653 |
|
|
|a mono and multi-objective optimization
|
| 653 |
|
|
|a multi-objective evolutionary algorithms
|
| 653 |
|
|
|a evolutionary optimization
|
| 653 |
|
|
|a diversity control
|
| 653 |
|
|
|a parallel manipulator
|
| 653 |
|
|
|a uncertainty quantification
|
| 653 |
|
|
|a optimal control
|
| 653 |
|
|
|a archiving strategy
|
| 653 |
|
|
|a encoding
|
| 653 |
|
|
|a evolutionary algorithm
|
| 653 |
|
|
|a multi-objective optimization
|
| 653 |
|
|
|a experimental study
|
| 653 |
|
|
|a global optimization
|
| 653 |
|
|
|a evolutionary algorithms
|
| 653 |
|
|
|a differential evolution
|
| 653 |
|
|
|a sheet thickness distribution
|
| 653 |
|
|
|a control
|
| 653 |
|
|
|a launchers
|
| 653 |
|
|
|a reusable launch vehicle
|
| 653 |
|
|
|a global optimisation
|
| 653 |
|
|
|a Chaotic optimization
|
| 653 |
|
|
|a robust
|
| 653 |
|
|
|a bankruptcy problem
|
| 653 |
|
|
|a availability
|
| 653 |
|
|
|a two-stage method
|
| 653 |
|
|
|a worst-case scenario
|
| 653 |
|
|
|a nearly optimal solutions
|
| 653 |
|
|
|a T-junctions
|
| 653 |
|
|
|a real application
|
| 653 |
|
|
|a robust design
|
| 653 |
|
|
|a beam T-junctions models
|
| 653 |
|
|
|a spaceplanes
|
| 653 |
|
|
|a classification
|
| 653 |
|
|
|a space systems
|
| 653 |
|
|
|a multi-objective decision-making
|
| 653 |
|
|
|a plastics thermoforming
|
| 653 |
|
|
|a non-linear parametric identification
|
| 653 |
|
|
|a genetic programming
|
| 653 |
|
|
|a trailing-edge noise
|
| 653 |
|
|
|a roughness measurement
|
| 653 |
|
|
|a surrogate
|
| 653 |
|
|
|a machine vision
|
| 653 |
|
|
|a Yellow Saddle Goatfish Algorithm
|
| 653 |
|
|
|a finite elements analysis
|
| 700 |
1 |
|
|a Gaspar‐Cunha, António
|
| 700 |
1 |
|
|a Hernández-Sosa, Daniel
|
| 700 |
1 |
|
|a Minisci, Edmondo
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b DOAB
|a Directory of Open Access Books
|
| 500 |
|
|
|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
|
| 024 |
8 |
|
|a 10.3390/books978-3-0365-2715-4
|
| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/5118
|7 0
|x Verlag
|3 Volltext
|
| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/81089
|z DOAB: description of the publication
|
| 082 |
0 |
|
|a 900
|
| 082 |
0 |
|
|a 576
|
| 082 |
0 |
|
|a 700
|
| 082 |
0 |
|
|a 600
|
| 082 |
0 |
|
|a 620
|
| 520 |
|
|
|a 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.
|