Large-Scale Evolutionary Multiobjective Optimization Assisted by Directed Sampling
提出一种定向采样辅助的大规模多目标进化算法，通过选择接近理想点的个体进行采样，利用非支配解辅助繁殖以加速收敛，并采用精英非支配排序和参考向量法维持多样性，在高达5000决策变量的测试问题上表现优异。
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.