Multiobjective Optimization of Mixed Variable Design Problems


In this paper, a new multiobjective genetic algorithm is employed to support the design of a hydraulic actuation system. First, the proposed method is tested using benchmarks problems gathered from the literature. The method performs well and it is capable of identifying multiple Pareto frontiers in multimodal function spaces. Secondly, the method is applied to a mixed variable design problem where a hydraulic actuation system is analyzed using simulation models. The design problem constitutes of a mixture of determining continuous variables as well as selecting components from catalogs. The multi-objective optimization results in a discrete Pareto front, which illustrate the trade-off between system cost and system performance.