Evolutionary Algorithms in Engineering Design Optimization

Edited by
March 2022
314 pages
  • ISBN978-3-0365-2714-7 (Hardback)
  • ISBN978-3-0365-2715-4 (PDF)

This book is a reprint of the Special Issue Evolutionary Algorithms in Engineering Design Optimization that was published in

Computer Science & Mathematics
Physical Sciences
Public Health & Healthcare

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.

  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
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; multi-objective optimization; preference in multi-objective optimization; aeroacoustics; trailing-edge noise; global optimization; evolutionary algorithms; multi-objective optimization; 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 algorithms; multi-objective optimization; evolutionary optimization; genetic programming; control; differential evolution; reusable launch vehicle; quality control; roughness measurement; machine vision; machine learning; evolutionary algorithm; parameter optimization; differential evolution; distance-based; mutation-selection; real application; experimental study; global optimisation; worst-case scenario; robust; min-max optimization; evolutionary algorithms; optimal control; multi-objective optimisation; robust design; trajectory optimisation; uncertainty quantification; unscented transformation; spaceplanes; space systems; launchers

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