Analysis of Relation between Prediction Accuracy of Surrogate Model and Search Performance on Extreme Learning Machine Assisted MOEA/D
Analysis of Relation between Prediction Accuracy of Surrogate Model and Search Performance on Extreme Learning Machine Assisted MOEA/D
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DOI：
10.23919/sice48898.2020.9240452
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
Koki Tsujino;Tomohiro Harada;R. Thawonmas
中科院分区：
文献类型：
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作者：
Koki Tsujino;Tomohiro Harada;R. Thawonmas
近年来，进化算法被用于解决许多现实问题，但由于其计算成本高，需要花费大量的计算时间才能获得最优解。为了减少优化的计算时间，研究了使用代理模型的多目标进化算法。ELMOEA/D是一种代理辅助的多目标进化算法。ELMOEA/D将MOEA/D与极限学习机（ELM）结合在一起。本文分析了代理模型的估计精度与ELMOEA/D搜索性能的关系。我们对几个著名的多目标基准问题进行了实验，并比较了不同的代数。实验结果表明，随着代数的增加，估计精度和搜索性能下降。
In recent years, evolutionary algorithms have been used for many real-world problems, but it takes enormous computation time to obtain the optimal solution due to its high calculation cost. Multi-objective evolutionary algorithms using surrogate models have been studied to reduce the computation time for the optimization. ELMOEA/D is one of the surrogate-assisted multi-objective evolutionary algorithms. ELMOEA/D combines MOEA/D with an extreme learning machine (ELM). This paper analyzes the relation between the estimation accuracy of the surrogate model and the search performance of ELMOEA/D. We experiment on several well-known multi-objective benchmark problems and compare the different number of generations. The experimental results reveal that the estimation accuracy and the search performance decrease as the number of generations increase.