| Author Name | Affiliation | | Yi Zhao | School of Economics and Management, Taiyuan University of Science and Technology, Taiyuan 030024, Shanxi, China | | Jianchao Zeng | Department of Computer Science and Control Engineering, North University of China, Taiyuan 030051, Shanxi,China | | Ying Tan | Department of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, Shanxi, China |
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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 increases 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.issn.1005-9113.2024081 |
| Clc Number:TP18 |
| Fund: |
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| Descriptions in Chinese: |
| 双性能指标辅助的填充策略用于昂贵的多目标优化
赵昳1，曾建潮2， 谭瑛3
（1. 太原科技大学 经管学院，太原 030024；
2. 中北大学 计算机科学与控制工程学院，太原 030051；
3.太原科技大学 计算机学院，太原 030024）
摘要：近年来，基于真实数据样本构建的代理模型已被证明能有效解决计算成本高昂或耗时严重的优化挑战。然而，随着目标空间维度灾难的加剧以及代理模型近似误差的累积，种群中个体的区分度会逐渐降低。为此，本文采用径向基函数方法对各目标函数进行建模，并通过基于强化支配关系的多目标进化算法定位代理模型的最优解集。将真实评估样本的原目标函数值转换为两个性能指标值，进而为这两个性能指标建立代理模型。最终，提出一种基于近似性能指标的自适应代理管理策略，用于从潜在最优解集中筛选待进行真实评估的个体。通过在两套测试集上将本算法与多种先进代理辅助进化算法进行对比，实验结果表明该方法在求解昂贵高维多目标优化问题时具有显著竞争力。
关键词： 昂贵多目标优化问题；代理管理策略；进化优化算法 |
