Structural Optimization Using Grey Wolf Optimizer and Its Variants

Abstract

Now a days, many metaheuristic optimization algorithms are invariably used in the field of engineering and data science. In structural mechanics, many stochastic optimization algorithms are employed for solving the complex numerical problems. Suitable optimization algorithms must be selected as the structural mechanics problems are often having non-linear objective function, constraints are having complex behavior and many design variables are involved in it. In present study, the capability and competency of standard grey wolf optimizer (GWO) with modified grey wolf optimizer (mGWO) and grey wolf optimizer with variable weight (vwGWO) is observed through several benchmarked numerical case studies. To control the geometric constraints, penalty method is employed in the study. The performance of the standard GWO algorithm is compared with the results obtained by the modified grey wolf optimizer and grey wolf optimizer with variable weight.