Method for Parameter Tuning of Hybrid Optimization Algorithms for Problems with High Computational Costs of Objective Function Evaluations

In this paper, the authors focus on presenting methodology for tuning optimization algorithm parameters, with a special evolutionary applications. The problem considered concerns phenomenon of nonlinear buckling automotive shock absorber, which itself is solved using commercial application finite element method (FEM) simulation. These analyses are usually time-consuming; therefore, decided to use surrogate model, mimics behavior actual FEM Surrogate modeling (metamodeling) utilized drastically shorten simulation time, and thus study numerous parameter combinations, allowing them providing robust efficient tool optimization. example shown in paper related minimization absorber weight, taking into account stability system. presented can be used any where high computational cost objective function evaluations prevents parameters.