Evolutionary Algorithms in Engineering Design Optimization

• Other Authors: Greiner, David (Editor), Gaspar‐Cunha, António (Editor), Hernández-Sosa, Daniel (Editor), Minisci, Edmondo (Editor), Zamuda, Aleš (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Subjects: Technology: general issues; History of engineering & technology; 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
• Online Access: DOAB: download the publication; DOAB: description of the publication

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:...