TY  - JOUR
AV  - none
N1  - cited By 0
TI  - Optimization of pressure vessel design using pyopt
SP  - 14264
SN  - 18196608
PB  - Asian Research Publishing Network
EP  - 14268
IS  - 24
N2  - pyOpt is an open source python based object oriented framework for nonlinear constrained optimization problems. In this study, we used pyOpt to solve pressure vessel design problem. Among the available optimizers in pyOpt, SLSQP (Sequential least squares programming), COBYLA (Constrained Optimization by Linear Approximation), ALPSO (Augmented Lagrangian Particle Swarm Optimizer), NSGAII (Non Sorting Genetic Algorithm II), MIDACO (Mixed Integer Distributed Ant Colony Optimization), and ALGENCAN (Augmented Lagrangian with GENCAN) were used. The effect of initial design variables on convergence was investigated for six different regions. The initial design variables for MIDACO and SLSQP should be within the design variable bound while COBYLA and ALPSO provide good result when the initial point is greater than the upper bound. On the other hand, NSGAII and ALGENCAN converge to the optimum value regardless of the initial value. The optimum results from all optimizers were compared with published literatures. Except for ALPSO with mixed discrete variables, the results are in good agreement with maximum percentage error of less than 5. Therefore, pyOpt can be considered as an alternative option to solve engineering design optimization problems. © 2006-2016 Asian Research Publishing Network (ARPN). All rights reserved.
ID  - scholars7625
Y1  - 2016///
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009183740&partnerID=40&md5=cfa543737626069c0601b08aaf96a189
JF  - ARPN Journal of Engineering and Applied Sciences
A1  - Woldemichael, D.E.
A1  - Woldeyohannes, A.D.
VL  - 11
ER  -