A Comparison of Multiobjective Evolutionary Algorithms

In this paper, a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions is given. 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.