Meta-heuristic design optimization of steel moment resisting frames subjected to natural frequency constraints

摘要

Natural frequencies of a structure play a key role for finding underlying structural properties. Therefore, structural design considering natural frequencies is a very important issue for engineers. While there are many studies on optimization of truss structures under multiple frequency constraints, just a few studies have been published on steel frames for which only gradient based methods were applied. In this paper, meta-heuristic algorithms are applied for the first time ever to optimal design of steel frame structures with frequency constraints. Several benchmark design examples are solved with five algorithms including particle swarm optimization (PSO), charged system search (CSS), teaching-learning based optimization (TLBO), grey wolf optimizer (GWO) and a recently developed improved grey wolf optimizer (IGWO). Optimization results of these algorithms are compared in terms of statistical indices, convergence and optimum solutions. Various types of planar and space steel frames ranging from small to large scale cases are considered for illustrating merits and applicability of meta-heuristic algorithms to these sizing optimization problems wherein the large scale cases are studied in both continuous and discrete forms.