Fast nondominated sorting genetic algorithm II (NSGA‐II) is a popular multiobjective optimization method. However, the tournament selection strategy for crossover operator suffers from the drawback of repetitively selecting the same individuals, resulting in unsatisfying performance. To alleviate this problem, this article first proposes to employ k‐means clustering strategy to divide the candidate individuals into multiple clusters. After that, the crossover operator are redefined with three crossover individuals, where the first and second individuals are forced to selected from the same cluster and the second and third ones are from different clusters. The newly proposed crossover operator is not only able to alleviate the phenomenon above, but also able to retain the advantage of the original tournament selection strategy. The proposed method is verified on two popular test suits, including DTLZ and ZDT test suit and an industrial network topology optimization problem. Experimental results demonstrate that the proposed method exhibits excellent performance on both the two test suits and the practical network topology optimization.

中文翻译：

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工业网络拓扑优化融合策略的非支配排序遗传算法II

快速非支配排序遗传算法 II (NSGA-II) 是一种流行的多目标优化方法。然而，交叉算子的锦标赛选择策略存在重复选择相同个体的缺点，导致性能不令人满意。为了缓解这个问题，本文首先提出采用k-means聚类策略将候选个体划分为多个聚类。之后，交叉算子被重新定义为三个交叉个体，其中第一个和第二个个体被迫从同一个集群中选择，而第二个和第三个个体来自不同的集群。新提出的交叉算子不仅能够缓解上述现象，而且能够保留原有锦标赛选择策略的优势。所提出的方法在两个流行的测试集上得到了验证，包括 DTLZ 和 ZDT 测试集以及一个工业网络拓扑优化问题。实验结果表明，所提出的方法在两个测试集和实际网络拓扑优化上都表现出优异的性能。