Metamodel Assisted Evolutionary Algorithm for Multi-objective Optimization of Non-steady Metal Forming Problems

Multi-objective optimization problems are considered in the field of non-steady metal forming processes, such as forging or wire drawing. The Pareto optimal front of the problem solution set is calculated by a Genetic Algorithm. In order to reduce the inherent computational cost of such algorithms, a surrogate model is developed and replaces the exact the function simulations. It is based on the Meshless Finite Difference Method and is coupled to the NSGA-II Evolutionary Multi-objective Optimization Algorithm, in a way that uses the merit function. This function offers the best way to select new evaluation points: it combines the exploitation of obtained results with the exploration of parameter space. The algorithm is evaluated on a wide range of analytical multi-objective optimization problems, showing the importance to update the metamodel along with the algorithm convergence. The application to metal forming multi-objective optimization problems show both the efficiency of the metamodel based algorithms and the type of practical information that can be derived from a multi-objective approach.