LEADER 05695nam 2201273z- 450 001 9910566486903321 005 20220506 035 $a(CKB)5680000000037510 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/81089 035 $a(oapen)doab81089 035 $a(EXLCZ)995680000000037510 100 $a20202205d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aEvolutionary Algorithms in Engineering Design Optimization 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (314 p.) 311 08$a3-0365-2714-1 311 08$a3-0365-2715-X 330 $aEvolutionary 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. 606 $aHistory of engineering & technology$2bicssc 606 $aTechnology: general issues$2bicssc 610 $aaccuracy levels 610 $aaeroacoustics 610 $aarchiving strategy 610 $aartificial neural networks (ANN) limited training data 610 $aAutomatic Voltage Regulation system 610 $aavailability 610 $abankruptcy problem 610 $abeam improvements 610 $abeam T-junctions models 610 $aChaotic optimization 610 $aclassification 610 $acontrol 610 $adesign 610 $adifferential evolution 610 $adistance-based 610 $adiversity control 610 $aencoding 610 $aevolutionary algorithm 610 $aevolutionary algorithms 610 $aevolutionary optimization 610 $aexperimental study 610 $afinite elements analysis 610 $aFractional Order Proportional-Integral-Derivative controller 610 $agenetic algorithm 610 $agenetic programming 610 $aglobal optimisation 610 $aglobal optimization 610 $aGough-Stewart 610 $alaunchers 610 $amachine learning 610 $amachine vision 610 $amin-max optimization 610 $amono and multi-objective optimization 610 $amulti-objective decision-making 610 $amulti-objective evolutionary algorithms 610 $amulti-objective optimisation 610 $amulti-objective optimization 610 $amutation-selection 610 $anearly optimal solutions 610 $aneural networks 610 $anon-linear parametric identification 610 $aoptimal control 610 $aoptimal design 610 $aparallel manipulator 610 $aparameter optimization 610 $aPareto front 610 $aperformance metrics 610 $aplastics thermoforming 610 $apreference in multi-objective optimization 610 $apreventive maintenance scheduling 610 $aquality control 610 $areal application 610 $areusable launch vehicle 610 $arobust 610 $arobust design 610 $aroughness measurement 610 $asheet thickness distribution 610 $aspace systems 610 $aspaceplanes 610 $asurrogate 610 $aT-junctions 610 $atrailing-edge noise 610 $atrajectory optimisation 610 $atwo-stage method 610 $auncertainty quantification 610 $aunscented transformation 610 $aworst-case scenario 610 $aYellow Saddle Goatfish Algorithm 615 7$aHistory of engineering & technology 615 7$aTechnology: general issues 700 $aGreiner$b David$4edt$01309575 702 $aGaspar?Cunha$b António$4edt 702 $aHernández-Sosa$b Daniel$4edt 702 $aMinisci$b Edmondo$4edt 702 $aZamuda$b Ale?$4edt 702 $aGreiner$b David$4oth 702 $aGaspar?Cunha$b António$4oth 702 $aHernández-Sosa$b Daniel$4oth 702 $aMinisci$b Edmondo$4oth 702 $aZamuda$b Ale?$4oth 906 $aBOOK 912 $a9910566486903321 996 $aEvolutionary Algorithms in Engineering Design Optimization$93029408 997 $aUNINA