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.
Keywords
Evolutionary algorithmMulti-objective optimizationComputer scienceMathematical optimizationEvolutionary computationMathematicsAlgorithm
MeSH Terms
AlgorithmsBiological EvolutionComputer SimulationEvaluation Studies as TopicModelsGeneticPopulation Density
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Publication Info
- Year
- 2000
- Type
- article
- Volume
- 8
- Issue
- 2
- Pages
- 173-195
- Citations
- 5476
- Access
- Closed
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Cite This
Eckart Zitzler,
Kalyanmoy Deb,
Lothar Thiele
(2000).
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results.
Evolutionary Computation
, 8
(2)
, 173-195.
https://doi.org/10.1162/106365600568202
Identifiers
- DOI
- 10.1162/106365600568202
- PMID
- 10843520