A Fast Incremental Hypervolume Algorithm
A Fast Incremental Hypervolume Algorithm
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DOI：
10.1109/tevc.2008.919001
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发表时间：
2008-12
影响因子：
14.3
通讯作者：
L. Bradstreet;Lyndon While;L. Barone
中科院分区：
文献类型：
--
作者：
L. Bradstreet;Lyndon While;L. Barone
当超体积被用作多目标进化算法中的选择或存档过程的一部分时，有必要确定哪些解决方案对前沿贡献最少的超体积。很少有人关注快速确定这些解决方案的算法，也没有专门为此目的设计的快速算法。我们描述了一个算法，IHSO，快速确定解决方案的贡献。此外，我们描述和分析了重新排序的目标，以尽量减少IHSO计算解决方案的贡献所需的工作。最后，我们描述和分析搜索技术，减少工作量所需的解决方案，而不是最少的贡献之一。这些技术相结合，允许多目标进化算法在许多目标中越来越复杂和大的前沿中计算超体积内联。
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.