Evolutionary many-objective optimisation: many once or one many?
Evolutionary many-objective optimisation: many once or one many?
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DOI：
10.1109/cec.2005.1554688
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发表时间：
2005-12
期刊：
影响因子：
--
通讯作者：
E. Hughes
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文献类型：
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作者：
E. Hughes
多目标进化算法在解决具有两三个目标的问题时已被广泛确立并得到良好发展。然而，众所周知，对于多目标优化（通常有三个以上目标），将帕累托最优性作为排序度量的算法可能会失去其有效性。本文比较了在多目标和超多目标问题上生成帕累托前沿面的三种不同方法。第一种方法是使用一种已确立的帕累托排序方法（NSGA - II），第二种方法是在单次运行中结合多个单目标优化（MSOPS），第三种方法是使用单目标优化器进行多次运行。结果表明，与重复进行单目标优化相比，在单次运行中生成整个帕累托集可获得更多优势。同样明显的是，随着问题维度的增加，NSGA - II会失去其有效性——在超多目标问题上，使用多个单目标优化比基于帕累托排序的优化器更有效。最终，“多次一次还是一次多次”取决于算法的选择，而非问题的规模。
Multi-objective evolutionary algorithms are widely established and well developed for problems with two or three objectives. However, it is known that for many-objective optimisation, where there are typically more than three objectives, the algorithms applying Pareto optimality as a ranking metric may loose their effectiveness. This paper compares three different approaches to generating Pareto surfaces on both multi and many objective problems. The first approach is using an established Pareto ranking method (NSGA II), the second combines multiple single objective optimisations in a single run (MSOPS), and the third uses multiple runs of a single objective optimiser. The results demonstrate that much can be gained by generating the entire Pareto set in a single run, when compared to repeated single objective optimisations. It is also clear that NSGA II loses its effectiveness as the problem dimensionality increases - it is more effective to use many single objective optimisations than a Pareto-ranking based optimiser on many-objective problems. Ultimately though, "many once or once many" is dependent on algorithm choice, not problem scale