Structural optimization using recent metaheuristic algorithms

Abstract

Most of the structural optimization problems are highly non-linear, involving many design variables and various complex constraints, which cannot be solved efficiently using traditional optimization techniques (gradient-based methods). Due to the highly non-linear and non-convex aspect of structural problems, these methods may converge only to a local optimum, or may diverge when initial estimates are not well designed. Therefore, the use of stochastic techniques is more interesting and efficient. As such, the present study presents a combined artificial intelligence technique, in this case, the Crow search Algorithm (CSA), Rao's Algorithm (Rao-1) and a new hybrid algorithm (CSARao-1) which have been adopted to solve structural problems in several types of optimizations. The methodology used in this work yielded good results, and the CSARao-1 hybrid algorithm proved to be the best performer in terms of accuracy, robustness and convergence speed.