Systematic Analyses of Multi-objective Evolutionary Algorithms Applied to Real-world Problems Using Statistical Design of Experiments

Solving multi-objective optimization problems is a challenging task that demands efficient software tools and systematic analytical approaches. In this paper two evolutionary multi-objective optimization algorithms – namely the evolution strategy (ES) and the NSGA II – are applied to two complex real-world problems. The parameter settings of the evolutionary algorithms have been chosen and optimized according to statistical design plans. A new ranking method for measuring the quality of pareto-fronts is introduced. The layout of mold temperature control systems and the scheduling of elevators show typical complexity aspects that are necessary to illustrate a systematic approach of solving real-world multi-objective optimization problems.