000			04022nam a22004215i 4500
001			86437
003			ES-MaUEC
005			20240111050127.0
007			cr nn 008mamaa
008			151104s2016 gw a s 001 0 eng d
020			_a9783662465967
024	7		_a10.1007/978-3-662-46596-7 _2doi
040			_aES-MaUEC
050		4	_aTA345 _b.B56 2016 EB
082	0	4	_a620.0042
245	0	0	_aBionic Optimization in Structural Design : _bStochastically Based Methods to Improve the Performance of Parts and Assemblies _cedited by Rolf Steinbuch, Simon Gekeler
250			_a1st ed.
264		1	_aBerlin, Heidelberg _bSpringer Berlin Heidelberg _c2016
300			_a1 recurso en línea (XII, 160 p.) _b103 ilustraciones, 6 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_aMotivation -- Bionic Optimization Strategies -- Problems and Limitations of Bionic Optimization -- Application to CAE Problems -- Applications of Bionic Optimization -- Current Fields of Interest -- Future Tasks in Optimization.
520	3		_aThe book provides suggestions on how to start using bionic optimization methods, including pseudo-code examples of each of the important approaches and outlines of how to improve them. The most efficient methods for accelerating the studies are discussed. These include the selection of size and generations of a study's, parameters, modification of these driving parameters, switching to gradient methods when approaching local maxima, and the use of parallel working hardware. Bionic Optimization means finding the best solution to a problem using methods found in nature. As Evolutionary Strategies and Particle Swarm Optimization seem to be the most important methods for structural optimization, we primarily focus on them. Other methods such as neural nets or ant colonies are more suited to control or process studies, so their basic ideas are outlined in order to motivate readers to start using them. A set of sample applications shows how Bionic Optimization works in practice. From academic studies on simple frames made of rods to earthquake-resistant buildings, readers follow the lessons learned, difficulties encountered and effective strategies for overcoming them. For the problem of tuned mass dampers, which play an important role in dynamic control, changing the goal and restrictions paves the way for Multi-Objective-Optimization. As most structural designers today use commercial software such as FE-Codes or CAE systems with integrated simulation modules, ways of integrating Bionic Optimization into these software packages are outlined and examples of typical systems and typical optimization approaches are presented. The closing section focuses on an overview and outlook on reliable and robust as well as on Multi-Objective-Optimization, including discussions of current and upcoming research topics in the field concerning a unified theory for handling stochastic design processes.
710	2		_aSpringerLink (Online service) _0Local _9106996
942			_2lcc _cLE
988			_aEBOOK, EBSPRINGER
650		7	_aDiseño asistido por ordenador _0comprobar BNE19912510165 _2embne _9143900
650		7	_aSimulación por ordenador _0comprobar BNE19926002334 _2embne _9147541
650		7	_aInteligencia artificial _0comprobar BNE19900997218 _2embne _9413115
700	1		_aSteinbuch, Rolf _eeditor literario _9100135 _0Local
700	1		_aGekeler, Simon _eeditor literario _9100136 _0Local
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-662-46596-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783662465967
907			_a.b1295715x _b10-10-17 _c21-11-16
998			_am _a_alco _a_vill _b23-09-17 _cm _dz _ei _feng _ggw _h0
945			_aTA345 .B56 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11598955 _z06-04-17
999			_c86437 _d86437 _x1