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Evolutionary Algorithms in Engineering Design Optimization

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:...

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Materialtyp: Online
Språk:engelska
Utgiven: MDPI - Multidisciplinary Digital Publishing Institute 2022
Ämnen:
Automatic Voltage Regulation system
Chaotic optimization
Fractional Order Proportional-Integral-Derivative controller
Yellow Saddle Goatfish Algorithm
two-stage method
mono and multi-objective optimization
multi-objective optimization
optimal design
Gough–Stewart
parallel manipulator
performance metrics
diversity control
genetic algorithm
bankruptcy problem
classification
T-junctions
neural networks
finite elements analysis
surrogate
beam improvements
beam T-junctions models
artificial neural networks (ANN) limited training data
multi-objective decision-making
Pareto front
preference in multi-objective optimization
aeroacoustics
trailing-edge noise
global optimization
evolutionary algorithms
nearly optimal solutions
archiving strategy
evolutionary algorithm
non-linear parametric identification
multi-objective evolutionary algorithms
availability
design
preventive maintenance scheduling
encoding
accuracy levels
plastics thermoforming
sheet thickness distribution
evolutionary optimization
genetic programming
control
differential evolution
reusable launch vehicle
quality control
roughness measurement
machine vision
machine learning
parameter optimization
distance-based
mutation-selection
real application
experimental study
global optimisation
worst-case scenario
robust
min-max optimization
optimal control
multi-objective optimisation
robust design
trajectory optimisation
uncertainty quantification
unscented transformation
spaceplanes
space systems
launchers
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
Länkar:ONIX_20220506_9783036527147_155
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