parameter selection for pso-based hybrid algorithms and its effect on crack detection in cantilever beams

The importance of the parameters of any optimization algorithm, especially meta-heuristic algorithms that have been created to simplify the solution of optimization problems, is inevitable. the optimal values of these parameters, which generally depend on the specifics of the problem in question, have a significant impact on the performance of the mentioned algorithms and a better search of the solution space. parameters selection of them will play an important role in performance and efficiency of the algorithms. this article examines the capability of various optimization algorithms and suggests dual hybrid optimization algorithms are named pso-fa, pso-ga, pso-gwo, for solving the problem of computing the depth and location of cracks in cantilever beams. the performance of particle swarm optimization (pso), genetic algorithm (ga), grey wolf optimization (gwo), firefly algorithm (fa), and hybrid of them base on pso optimizer to determine the location and depth of crack for cantilever beam are proposed. these suggested algorithms are optimization algorithms based on intelligent optimization. so, the performance of these algorithms are analyzed when the control parameters vary.