It is particularly challenging for evolutionary algorithms to quickly converge to the Pareto front in large-scale multiobjective optimization. To tackle this problem, this article proposes a large-scale multiobjective evolutionary algorithm assisted by some selected individuals generated by directed sampling (DS). At each generation, a set of individuals closer to the ideal point is chosen for performing a DS in the decision space, and those nondominated ones of the sampled solutions are used to assist the reproduction to improve the convergence in evolutionary large-scale multiobjective optimization. In addition, elitist nondominated sorting is adopted complementarily for environmental selection with a reference vector-based method in order to maintain diversity of the population. Our experimental results show that the proposed algorithm is highly competitive on large-scale multiobjective optimization test problems with up to 5000 decision variables compared to five state-of-the-art multiobjective evolutionary algorithms.

中文翻译：

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定向采样辅助的大规模进化多目标优化

进化算法在大规模多目标优化中快速收敛到帕累托前沿尤其具有挑战性。为了解决这个问题，本文提出了一种大规模多目标进化算法，由定向采样（DS）生成的一些选定个体辅助。在每一代中，选择一组更接近理想点的个体在决策空间中执行 DS，并使用那些非支配的样本解来辅助繁殖，以提高进化大规模多目标优化的收敛性。此外，精英非支配排序与基于参考向量的方法互补地用于环境选择，以保持种群的多样性。