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A Kriging-assisted multiobjective evolutionary algorithm
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AI
DOI：10.1016/j.asoc.2017.04.017.png)
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摘要
En 中文
A surrogate-assisted (SA) evolutionary algorithm for Multiobjective Optimization Problems (MOOPs) is presented as a contribution to Soft Computing (SC) in Artificial Intelligence (AI). Such algorithm is grounded on the cooperation between a pure evolutionary algorithm and a Kriging based algorithm featuring the Expected Hyper-Volume Improvement (EHVI) metric. Comparison with state-of-art pure and Kriging-assisted algorithms over two- and three-objective test functions have demonstrated that the proposed algorithm can achieve high performance in the approximation of the Pareto-optimal front mitigating the drawbacks of its parent algorithms. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Soft-computing
Evolutionary computation
Multiobjective optimization
Surrogates
Metamodels
Kriging
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期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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