Fuzzy Classification Surrogate-assisted Evolutionary Algorithm Based on Adaptive Sampling Strategy
[1]LI Erchao,WU Yu.Fuzzy Classification Surrogate-assisted Evolutionary Algorithm Based on Adaptive Sampling Strategy[J].Journal of Zhengzhou University (Engineering Science),2025,46(02):51-59.[doi:10.13705/j.issn.1671-6833.2025.02.010]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
46
Number of periods:
2025 02
Page number:
51-59
Column:
Public date:
2025-03-10
- Title:
- Fuzzy Classification Surrogate-assisted Evolutionary Algorithm Based on Adaptive Sampling Strategy
- Keywords:
- surrogate-assisted evolutionary algorithm; surrogate model; expensive many-objective optimization problems; model management
- CLC:
- TP18
- Abstract:
- In order to solve the problem of low management efficiency of classification surrogate-assisted evolutionary algorithms based on fuzzy classification and how to effectively reduce the number of real function evaluations, in this study a fuzzy classification surrogate-assisted evolutionary algorithm was proposed based on an adaptive sampling strategy. Firstly, in the algorithm the agent model was constructed by screening samples through the Pareto dominance relationship. Then, the selection pressure was improved by using a transfer-based density estimation strategy, which balanced convergence and diversity. At the same time, ten-fold cross-validation was used to obtain accuracy information to divide the states. Finally, an adaptive model management strategy was designed. Considering the convergence, diversity, and uncertainty of the current population, targeted sampling methods was adopted according to different accuracy states. The algorithm could ensure overall performance while rationally reducing the number of real evaluations. To verify the performance of the proposed algorithm, it was compared with four other algorithms on the MaF and WFG test sets and real-world engineering problems of automotive side impact design and driving cabin design. The experimental results showed that the proposed algorithm, which could prove that the algorithm had good competitiveness in solving expensive multi-objective optimization problems with the limited number of real evaluations.
- References:
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Last Update:
2025-03-13