Optimal design of planar steel frame structures utilizing meta-heuristic optimization algorithms

Abstract

Meta-heuristic algorithms are applied in optimization problems in a variety of fields, including engineering, economics, and computer science. In this paper, seven population-based meta-heuristic algorithms are employed for size optimization of two-dimensional steel frame structures. These algorithms consist of the Artificial Bee Colony algorithm, Big Bang-Big Crunch algorithm, Cyclical Parthenogenesis Algorithm, Cuckoo Search algorithm, Thermal Exchange Optimization algorithm, Teaching-Learning-Based Optimization algorithm, and Water Evaporation Optimization algorithm. Optimization aims to minimize the weight of rigid-jointed steel frame structures while satisfying some constraints on displacement and stress limits. The design is based on the requirements of the AISC Load and Resistance Factor Design (LRFD). The capability and robustness of the algorithms are investigated through three well-known steel frame benchmarks.