Evolutionary Algorithms in Engineering Design Optimization
Greiner, David
Evolutionary Algorithms in Engineering Design Optimization - Basel MDPI - Multidisciplinary Digital Publishing Institute 2022 - 1 electronic resource (314 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-2715-4 9783036527147 9783036527154
10.3390/books978-3-0365-2715-4 doi
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
Evolutionary Algorithms in Engineering Design Optimization - Basel MDPI - Multidisciplinary Digital Publishing Institute 2022 - 1 electronic resource (314 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-2715-4 9783036527147 9783036527154
10.3390/books978-3-0365-2715-4 doi
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