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Comparison of Multiobjective Evolutionary Algorithms: Empirical Results (2000)
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@MISC{Zitzler00comparisonof,
author = {Eckart Zitzler and Lothar Thiele and Kalyanmoy Deb},
title = {Comparison of Multiobjective Evolutionary Algorithms: Empirical Results},
year = {2000}
}
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Abstract
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.
Keyphrases
test function multiobjective optimizers certain technique various evolutionary approach particular feature sufficient complexity different problem feature evolutionary optimization process evolutionary multiobjective search pareto-optimal front systematic comparison experimental result suggested test function important factor
