Hide/Show Apps

Upper Bound Strategy in Optimum Design of Truss Structures: A Big Bang-Big Crunch Algorithm Based Application

Kazemzadeh Azad, Saeid
Hasançebi, Oğuzhan
Kazemzadeh Azad, Sina
Erol, Osman Kaan
One main shortcoming of metaheuristic search techniques in structural optimization is the large number of time-consuming structural analyses required for convergence to a reasonable solution. This study is an attempt to apply the so-called upper bound strategy (UBS) as a simple, yet an efficient strategy to reduce the total number of structural analyses through avoiding unnecessary analyses during the course of optimization. Although, the usefulness of the UBS is demonstrated in conjunction with a big bang-big crunch algorithm developed for optimum design of truss structures, it can be integrated with any other metaheuristic technique which works on the basis of (mu+lambda) evolutionary model. The numerical investigations over three benchmark truss optimization instances reveal that the UBS can reduce the total number of required structural analyses of the standard BB-BC algorithm to a great extent.