A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization
A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization
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DOI：
10.1109/tevc.2016.2622301
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发表时间：
2018-02-01
影响因子：
14.3
通讯作者：
Sindhya, Karthik
中科院分区：
文献类型：
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作者：
Chugh, Tinkle;Jin, Yaochu;Sindhya, Karthik
我们提出了一种替代辅助参考矢量引导的进化算法（EA），以实现超过三个目标的计算昂贵优化问题。所提出的算法基于最近开发的EA，用于多个目标优化，该优化依赖于一组自适应参考向量进行选择。拟议的替代辅助EA（SAEA）使用Kriging近似每个目标函数以降低计算成本。在管理Kriging模型时，该算法通过利用Kriging模型给出的近似客观值，参考向量的分布以及个人位置中的不确定性信息来关注多样性和收敛的平衡。此外，我们设计了一种选择用于训练Kriging模型的数据以限制计算时间而不损害近似准确性的策略。将新算法与最先进的SAEA在许多基准问题上进行比较的经验结果证明了拟议算法的竞争力。
We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed EA for many-objective optimization that relies on a set of adaptive reference vectors for selection. The proposed surrogate-assisted EA (SAEA) uses Kriging to approximate each objective function to reduce the computational cost. In managing the Kriging models, the algorithm focuses on the balance of diversity and convergence by making use of the uncertainty information in the approximated objective values given by the Kriging models, the distribution of the reference vectors as well as the location of the individuals. In addition, we design a strategy for choosing data for training the Kriging model to limit the computation time without impairing the approximation accuracy. Empirical results on comparing the new algorithm with the state-of-the-art SAEAs on a number of benchmark problems demonstrate the competitiveness of the proposed algorithm.