Upper bound strategy for metaheuristic based design optimization of steel frames

Optimum design of structural systems based on metaheuristic algorithms suffers from enormously time-consuming structural analyses to locate a reasonable design. In this paper an upper bound strategy (UBS) is proposed for reducing the total number of structural analyses in metaheuristic based design optimization of steel frame structures. The well-known big bang–big crunch algorithm as well as its two enhanced variants are adopted as typical metaheuristic algorithms to evaluate the effect of the UBS on computational efficiency of these techniques. The numerical results reveal that the UBS can significantly lessen the total computational cost in metaheuristic based design optimization of steel frames.

► An upper bound strategy (UBS) is proposed for reducing the computational effort in metaheuristic algorithms.
► The big bang–big crunch algorithm based techniques are adopted to evaluate the efficiency of the UBS.
► The UBS is capable of reducing the computational effort between 89.75% and 97.1%.