TY - JOUR
T1 - IMPROVING COMPUTATIONAL EFFICIENCY OF PARTICLE SWARM OPTIMIZATION FOR OPTIMAL STRUCTURAL DESIGN
TT -
JF - IUST
JO - IUST
VL - 3
IS - 4
UR - http://ijoce.iust.ac.ir/article-1-148-en.html
Y1 - 2013
SP - 563
EP - 574
KW - structural design optimization
KW - metaheuristic algorithms
KW - particle swarm optimization
KW - upper bound strategy
KW - steel frame
N2 - This paper attempts to improve the computational efficiency of the well known particle swarm optimization (PSO) algorithm for tackling discrete sizing optimization problems of steel frame structures. It is generally known that, in structural design optimization applications, PSO entails enormously time-consuming structural analyses to locate an optimum solution. Hence, in the present study it is attempted to lessen the computational effort of the algorithm, using the so called upper bound strategy (UBS), which is a recently proposed strategy for reducing the total number of structural analyses involved in the course of design optimization. In the UBS, the key issue is to identify those candidate solutions which have no chance to improve the search during the optimum design process. After identifying those non-improving solutions, they are directly excluded from the structural analysis stage, diminishing the total computational cost. The performance of the UBS integrated PSO algorithm (UPSO) is evaluated in discrete sizing optimization of a real scale steel frame to AISC-LRFD specifications. The numerical results demonstrate that the UPSO outperforms the original PSO algorithm in terms of the computational efficiency.
M3
ER -