000			03295nas|a2200385 i 4500
005			20260818115026.0
008			030815s2003 th uzm rtt 00| a1eng d
035			_a.b11894027
099		9	_aAIT Thesis no.ST-03-17
100	0		_aTanan Chub-uppakarn
245	1	2	_aA genetic algorithm for selecting optimal parameters in the Meshless Local Petrov-Galerkin method
260			_aBangkok : _bAsian Institute of Technology, _c2003
300			_a71 leaves
490	1		_aThesis ; _vno. ST-03-17
500			_aA thesis submitted in partial fulfillment of the thesis requirement for the degree of Master of Engineering, School of Engineering and Technology
502			_aThesis (M.Eng.) - Asian Institute of Technology, 2003
520			_aMeshless methods have become very attractive and efficient for the development of adaptive methods for solving boundary value problems because nodes can be easily added and removed without a burdensome remeshing of element. The meshless local PetrovGalerkin method approach, based on the local symmetric weak form and the moving least squares approximation, is a truly numerical meshless method for solving boundary value problems. This work employs polygonal nodal sub-domains constructed from several triangular patches rather than the typically used circular sub-domains is presented. The moving least-squares approximation and lagrange interpolation functions are used as the trial function and test functions respectively. This method aims to decrease the error of solution but many parameters of MLPG have an effect to the accuracy of the numerical solution such as x and y coordinates of nodal points, radius of influence. The presence of many parameters and complex process for obtaining the solution make finding difficult or possibility to solve using traditional algorithms. GAs has emerged as an efficient technique in handling for many parameters and complex process for obtaining the solution of elastostatic 2D problem. GAs is a search optimal solution process which is set by objective functions. The objective function consists of the L2 norm of displacement error and the ratio between gauss integration points in problem domain with node have effect on this gauss point integration because aim of this work want to reduce computation time and increase accuracy.
650		0	_aGenetic algorithms
700	1		_aBarry, William Joseph, _eChairperson
700	0		_aWorsak Kanok-Nukulchai, _eExamination Committee
700	0		_aPennung Warnitchai, _eExamination Committee
710	2		_aRTG Fellow Scholarship, _eScholarship donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. ST-03-17
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B08910
907			_a.b11894027 _bmnait _cu
902			_a240404
998			_b0 _c030815 _dm _ea _fu _g2
945			_lmnait
945			_lmnarc
942			_c22
942			_c40
909			_aBarcode : 30050120745491 _bCREATED : 2014-04-29 _cRECORD # : i12792792 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
909			_aBarcode : 30050160067632 _bCREATED : 2016-04-19 _cRECORD # : i12937642 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
999			_c49428 _d49428