Determining the optimum placement of braces in steel frames has always been one of the most challenging issues in structural engineering. In this paper, the size and placement of the X-braces in planar frame structures is determined in a way
Keywords layout optimization, planar braced frames, colliding bodies optimization, modified dolphin monitoring operator, metaheuristics
IntroductionLayout or topology optimization deals with the selection of the best configuration for structural systems and constitutes one of the newest and most rapidly expanding fields of structural design, although some of its basic concepts were established almost a century ago [1]. In other words, structural layout optimization is a technique which enables automatic identification of optimal arrangements of structural elements in frames [2].In the field of structural engineering design, the main objectives include efforts to find design methods with optimum weight, cost of the construction, geometry, design and optimal topology along with satisfying the design constraints. For example, the optimal design of steel frames requires the selection of suitable steel sections for frame members from a set of standard steel sections. This choice should be made in such a way that not only the steel has the minimum weight, but also the strength constraints and serviceability of the structure are within the limits specified by the design specifications. In building frames, lateral loads are mainly supported by the lateral system. One of the commonly used structural systems for providing the lateral reinforcement of steel structures is the combination of a moment resisting frame with a braced frame forming a dual building frame system. Since determining the best location of bracings is not easy, one of the steps that can be taken to achieve this goal is using trial and error methods. This can be done by using metaheuristic optimization algorithms having an appropriate accuracy and speed.Some metaheuristic algorithms for designing structures consist of Genetic algorithms which is based on the evolution of living organisms [3]; Ant colony optimization inspired by rules governing the behavior of the real ants to find the shortest path between a nest and food for the prediction of best solution [4]; Particle swarm optimization (PSO) that is a population based stochastic optimization technique inspired by nature and social behavior of bird flocking or fish schooling [5]; Imperialist competitive algorithm (ICA) that is inspired by the political model and competition between empires [6]; Harmony search (HS) algorithm that is based on process which takes place when a musician searches for a better state of harmony [7,8]; Charged