Giagkiozis Ioannis, Fleming Peter J
School of Mathematics and Statistics, University of Sheffield, Sheffield, S3 7RH, UK
Evol Comput. 2014 Winter;22(4):651-78. doi: 10.1162/EVCO_a_00128.
The set of available multi-objective optimisation algorithms continues to grow. This fact can be partially attributed to their widespread use and applicability. However, this increase also suggests several issues remain to be addressed satisfactorily. One such issue is the diversity and the number of solutions available to the decision maker (DM). Even for algorithms very well suited for a particular problem, it is difficult-mainly due to the computational cost-to use a population large enough to ensure the likelihood of obtaining a solution close to the DM's preferences. In this paper we present a novel methodology that produces additional Pareto optimal solutions from a Pareto optimal set obtained at the end run of any multi-objective optimisation algorithm for two-objective and three-objective problem instances.
可用的多目标优化算法集持续增加。这一事实部分可归因于它们的广泛使用和适用性。然而，这种增加也表明仍有几个问题有待令人满意地解决。其中一个问题是决策者（DM）可获得的解决方案的多样性和数量。即使对于非常适合特定问题的算法，主要由于计算成本，也很难使用足够大的种群来确保获得接近DM偏好的解决方案的可能性。在本文中，我们提出了一种新颖的方法，该方法可以从任何多目标优化算法针对双目标和三目标问题实例的最终运行中获得的帕累托最优集中生成额外的帕累托最优解。
