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A Fast Incremental Hypervolume Algorithm
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AI
DOI：10.1109/TEVC.2008.919001.png)
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Abstract
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When hypervolume is used as part of the selection or archiving process in a multiobjective evolutionary algorithm, it is necessary to determine which solutions contribute the least hypervolume to a front. Little focus has been placed on algorithms that quickly determine these solutions and there are no fast algorithms designed specifically for this purpose. We describe an algorithm, IHSO, that quickly determines a solution's contribution. Furthermore, we describe and analyse heuristics that reorder objectives to minimize the work required for IHSO to calculate a solution's contribution. Lastly, we describe and analyze search techniques that reduce the amount of work required for solutions other than the least contributing one. Combined, these techniques allow multiobjective evolutionary algorithms to calculate hypervolume inline in increasingly complex and large fronts in many objectives.
Keywords:
Diversity
evolutionary computation
hypervolume
multiobjective optimization
performance metrics
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12
Papers:
1.8K
Citations:
2.4W
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