Название: Evolutionary Algorithms in Engineering Design Optimization Автор: David Greiner, Antonio Gaspar-Cunha, Daniel Hernandez-Sosa Издательство: MDPI Год: 2022 Страниц: 316 Язык: английский Формат: pdf (true) Размер: 29.8 MB
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. To help address and resolve these engineering optimization problems, this book comprises 14 chapters that present a series of contributions in the field.
The manuscripts cover a wide spectrum in terms of type of problems, methodologies and applications. Type of problems: single-objective and multi-objective optimization (among them, analysis of archiving strategies in evolutionary multi-objective algorithms, and preference directions in multi-objective optimization problems). Methods: genetic programming, genetic algorithms, particle swarm optimization, differential evolution, estimation of distribution algorithms, memetic algorithms, among others. Applications: Identification of thermal systems, plastics thermoforming, reliability (maintenance) and design of systems, multi-objective design of general universal–prismatic–spherical Gough–Stewart structure platforms, aero-acoustical trailing-edge noise problem, surrogate modelling of beam T-junctions for characterization of tubular structures, vibration absorber, online surface roughness measurement of automobile components, daily diet design problem, bankruptcy prediction problem, optimal tuning of a fractional order proportional–integral-derivative controller for an automatic voltage regulator system, control system for an aerospace re-entry vehicle, and design of descent trajectories for spaceplane-based two-stage launch systems.
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Evolutionary Algorithms in Engineering Design Optimization 
Название: Evolutionary Algorithms in Engineering Design Optimization