On the Use of Problem-Specific Candidate Generators for the Hybrid Optimization of Multi-Objective Production Engineering Problems

Weinert, K.1, a; Zabel, A.1, b; Kersting, P.1, c; Michelitsch, T.1, d; Wagner, T.1, e

Institut für Spanende Fertigung, Technische Universität Dortmund, Baroper Str. 303, 44227 Dortmund

a) weinert@isf.de; b) zabel@isf.de; c) petra.kersting@isf.de; d) michelitsch@isf.de; e) wagner@isf.de


In the field of production engineering, various complex multi-objective problems are known. In this paper we focus on the design of mold temperature control systems, the reconstruction of digitized surfaces, and the optimization of NC paths for the fiveaxis milling process. For all these applications, efficient problem-specific algorithms exist that only consider a subset of the desirable objectives. In contrast, modern multiobjective evolutionary algorithms are able to cope withmany conflicting objectives, but they require a long runtime due to their general applicability. Therefore, we propose hybrid algorithms for the three applicationsmentioned. In each case, the problem-specific algorithms are used to determine promising initial solutions for the multi-objective evolutionary approach, whose variation concepts are used to generate diversity in the objective space. We show that the combination of these techniques provides great benefits. Since the final solution is chosen by a decision maker based on this Pareto front approximation, appropriate visualizations of the high-dimensional solutions are presented.


Multi-objective optimization, production engineering problems, five-axis milling, mold temperature control system design, surface reconstruction, hybrid algorithms, Pareto analysis, multi-criteria decision making


Evolutionary Computation, 17 (2009) 4, S. 527-544, doi: 10.1162/evco.2009.17.4.17405