Complementary surrogate-assisted differential evolution algorithm for expensive multi-objective problems under a limited computational budget
Complementary surrogate-assisted differential evolution algorithm for expensive multi-objective problems under a limited computational budget
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DOI：
10.1016/j.ins.2023.03.005
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Xiwen Cai;Gan Ruan;Bo Yuan;Liang Gao
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文献类型：
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作者：
Xiwen Cai;Gan Ruan;Bo Yuan;Liang Gao
不同的代理辅助策略对代理辅助多目标进化算法的优化效率有很大影响。该研究通过混合两种互补的代理辅助策略，提出了一种高效的代理辅助差分进化算法，可以在有限的计算预算下优化昂贵的多目标问题。这两个建议的代理人辅助策略平衡全局和局部搜索多目标优化。具体而言，一种策略是基于最小角距离顺序采样的改进的基于代理的多目标局部搜索方法。与以往基于欧氏距离采样的局部搜索方法相比，改进的局部搜索方法能够有效地减小不同目标的尺度差异，提高了搜索效率。另一种替代辅助策略是基于多样性增强的预期改进矩阵填充准则的预筛选。提出的填充准则的目的是提高近似Pareto最优解的多样性，考虑候选个体在目标空间中的填充函数的分布。在一个有限的计算负担，所提出的算法的性能证明了一个大的多目标基准问题，以及现实世界的翼型设计问题。实验结果表明，该算法的性能显着优于一些现有的算法在这项研究中调查的大多数问题。
Different surrogate-assisted strategies can greatly influence the optimization efficiency of surrogate-assisted multi-objective evolutionary algorithms. By hybridizing two complementary surrogate-assisted strategies, this study proposed an efficient surrogate-assisted differential evolution algorithm to optimize expensive multi-objective problems under a limited computational budget. The two proposed surrogate-assisted strategies balance global and local search for multi-objective optimization. Specifically, one strategy is an improved surrogate-based multi-objective local search method that is based on maximin angle-distance sequential sampling. Compared with the previous local search method that is based on Euclidian distance-based sampling, the improved local search method is more efficient because it can mitigate the scale difference of different objectives. The other surrogate-assisted strategy is prescreening based on a diversity-enhanced expected improvement matrix infill criterion. The proposed infill criterion aims to improve the diversity of approximate Pareto optimal solutions by considering distribution of candidate individuals in the objective space in the infill function. Within a limited computational burden, the performance of the proposed algorithm is demonstrated on a large set of multi-objective benchmark problems, as well as a real-world airfoil design problem. Experimental results show that the proposed algorithm performs significantly better than some existing algorithms on most problems investigated in this study.