| Author Name | Affiliation | Postcode | | Yi Zhao* | School of Economics and Management, Taiyuan University of Science and Technology, Taiyuan 030024, Shanxi, China | 030024 | | Jianchao Zeng | Department of Computer Science and Control Engineering, North University of China, Shanx 030051, China | 030051 | | Ying Tan | Department of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, Shanxi, China | 030024 |
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| Abstract: |
| In recent years, surrogate models derived from genuine data samples have proven to be efficient in addressing optimization challenges that are costly or time-intensive. However, the individuals in the population become indistinguishable as the curse of dimensionality increase in the objective space and the accumulation of surrogate approximated errors. Therefore, in this paper, each objective function is modeled using a radial basis function approach, and the optimal solution set of the surrogate model is located by the multi-objective evolutionary algorithm of strengthened dominance relation. The original objective function values of the true evaluations are converted to two indicator values, and then the surrogate models are set up for the two performance indicators. Finally, an adaptive infill sampling strategy that relies on approximate performance indicators is proposed to assist in selecting individuals for real evaluations from the potential optimal solution set. The algorithm is contrasted against several advanced surrogate-assisted evolutionary algorithms on two suites of test cases, and the experimental findings prove that the approach is competitive in solving expensive many-objective optimization problems. |
| Key words: expensive multi-objective optimization problems, infill sample strategy, evolutionary optimization algorithm |
| DOI：10.11916/j.issn1005-9113.2024081 |
| Clc Number:TP18 |
| Fund: |
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| Descriptions in Chinese: |
| In recent years, surrogate models derived from genuine data samples have proven to be efficient in addressing optimization challenges that are costly or time-intensive. However, the individuals in the population become indistinguishable as the curse of dimensionality increase in the objective space and the accumulation of surrogate approximated errors. Therefore, in this paper, each objective function is modeled using a radial basis function approach, and the optimal solution set of the surrogate model is located by the multi-objective evolutionary algorithm of strengthened dominance relation. The original objective function values of the true evaluations are converted to two indicator values, and then the surrogate models are set up for the two performance indicators. Finally, an adaptive infill sampling strategy that relies on approximate performance indicators is proposed to assist in selecting individuals for real evaluations from the potential optimal solution set. The algorithm is contrasted against several advanced surrogate-assisted evolutionary algorithms on two suites of test cases, and the experimental findings prove that the approach is competitive in solving expensive many-objective optimization problems. |
