Meta-heuristic design optimization of steel moment resisting frames subjected to natural frequency constraints. (September 2019) - Zakian, Pooya
Highlights: Feasibility of using meta-heuristic optimization algorithms in weight minimization of steel frames subject to multiple frequency constraints is investigated. Meta-heuristic algorithms are applied for the first time ever to such a design problem. Eight design examples, from small to large-scale frame structures, are presented to demonstrate capability of these algorithms in the present optimization problem. Relative merits of algorithms are studied on a statistical basis. Abstract: 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.