An overview of population-based algorithms for multi-objective optimisation
An overview of population-based algorithms for multi-objective optimisation
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DOI：
10.1080/00207721.2013.823526
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发表时间：
2015-07-04
影响因子：
4.3
通讯作者：
Fleming, Peter J.
中科院分区：
文献类型：
--
作者：
Giagkiozis, Ioannis;Purshouse, Robin C.;Fleming, Peter J.
在这项工作中，我们概述了最著名的基于种群的算法和用于将它们扩展到多目标问题的方法。尽管在数学意义上并不准确，但人们早就认识到，基于群体的多目标优化技术在现实世界中的应用是非常有价值和多才多艺的。这些技术通常在精确的优化方法不容易应用时使用，或者仅仅在由于纯粹的复杂性而可能非常昂贵的情况下使用。另一个优点是，由于在每一代中考虑了决策向量的总体，这些算法是隐式可并行的，并且可以在每次迭代中生成整个帕累托前沿的近似值。还对他们的能力进行了批评。
In this work we present an overview of the most prominent population-based algorithms and the methodologies used to extend them to multiple objective problems. Although not exact in the mathematical sense, it has long been recognised that population-based multi-objective optimisation techniques for real-world applications are immensely valuable and versatile. These techniques are usually employed when exact optimisation methods are not easily applicable or simply when, due to sheer complexity, such techniques could potentially be very costly. Another advantage is that since a population of decision vectors is considered in each generation these algorithms are implicitly parallelisable and can generate an approximation of the entire Pareto front at each iteration. A critique of their capabilities is also provided.