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Comparison of Multiobjective Evolutionary Algorithms: Empirical Results

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Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
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. Keywords Evolutionary algorithms, multiobjective optimization, Pareto opti...
Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele
Added 18 Dec 2010
Updated 18 Dec 2010
Type Journal
Year 2000
Where EC
Authors Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele
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