Multi-objective evolutionary design of mold temperature control using DACE for parameter optimization

Abstract

The design of mold temperature control strategies (MTCS) is a challenging multi-objective optimization task which demands for advanced optimization methods. Evolutionary algorithms (EA) are powerful stochastically driven search techniques. In this paper an EA is applied to a multi-objective problem using aggregation. The performance of the evolutionary search can be improved using systematic parameter adaptation. The DACE technique (design and analysis of computer experiments) is used to find good MOEA (multi-objective evolutionary algorithm) parameter settings to get improved solutions of the MTCS problem. SPO (sequential parameter optimization), i.e., an automatic and integrated approach, which extends DACE, is applied to find the statistically significant and most promising EA parameters.