Eckart Zitzler1, Kalyanmoy Deb2, Lothar Thiele1
1Department of Electrical Engineering, Swiss Federal Institute of Technology 8092 Zurich, Switzerland
2Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, PIN 208 016, India
Tóm tắt
In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front (e.g., multimodality and deception). By investigating these different problem features separately, it is possible to predict the kind of problems to which a certain technique is or is not well suited. However, in contrast to what was suspected beforehand, the experimental results indicate a hierarchy of the algorithms under consideration. Furthermore, the emerging effects are evidence that the suggested test functions provide sufficient complexity to compare multiobjective optimizers. Finally, elitism is shown to be an important factor for improving evolutionary multiobjective search.
