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Preferences and their application in evolutionary multiobjective optimization
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DOI：10.1109/4235.985691.png)
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Abstract
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The paper describes a new preference method and its use in multiobjective optimization. These preferences are developed with a goal to reduce the cognitive overload associated with the relative importance of a certain criterion within a multiobjective design environment involving large numbers of objectives. Their successful integration with several genetic-algorithm-based design search and optimization techniques (weighted sums, weighted Pareto, weighted coevolutionary methods, and weighted scenarios) are described and theoretical results relating to complexity and sensitivity of the algorithm are presented and discussed. Its usefulness has been demonstrated in a real-world project of conceptual airframe design (a joint project with British Aerospace Systems).
Keywords:
genetic algorithms
multiobjective optimization
Pareto
preferences
scenarios
weights
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12
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1.8K
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