针对目标函数估值昂贵的多目标优化问题, 提出了基于聚类的代理辅助进化算法。在MOEA/D算法的框架下, 对种群进行聚类, 并通过权重向量的邻域选出种群子集, 在子集上使用径向基插值函数辅助的差分进化算法得到新解, 对种群进行更新。在7个DTLZ标准测试问题上进行了数值实验, 计算结果表明本文提出的算法比新近提出的多目标邻域回归优化(MONRO)算法具有优势。
A clustering-based surrogate-assisted evolutionary algorithm is proposed for computationally expensive multi-objective optimization problems. Under the framework of MOEA/D, the population is partitioned into several clusters, and the population subsets are formed via the neighbourhood of the weights. Then the radial basis function surrogate-assisted differential evolution algorithm is used to generate new solution points from the formed subsets, and the population is updated using the generated new solution. Numerical experiments have been undertaken on 7 DTLZ test problems, and the computational results indicate that the proposed evolutionary algorithm has advantages over the newly developed multi-objective neighborhood regression optimization (MONRO) algorithm.
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