论文标题
合作协调杂交NSGA-II，具有连锁测量最小化的大规模多目标优化
Cooperative coevolutionary hybrid NSGA-II with Linkage Measurement Minimization for Large-scale Multi-objective optimization
论文作者
论文摘要
在本文中，我们提出了一种基于合作进化的可变分组方法，用于大规模多目标问题（LSMOPS），命名为链接测量最小化（LMM）。对于子问题优化阶段，提出了基于估计收敛点的高斯采样算子的混合NSGA-II。根据我们先前的研究，在变量分组阶段中，我们将可变分组问题视为组合优化问题，并且链接测量函数的设计基于非线性检查真实代码（LINC-R）的链接识别。我们将此变量分组方法扩展到LSMOPS。在子问题优化阶段，我们假设帕累托阵线（PF）周围现有更好的解决方案的可能性更高。基于这一假设，我们估计每一代优化的收敛点，并在收敛点周围进行高斯采样。具有良好客观价值的样本将作为精英的优化参与。数值实验表明，我们的变量分组方法比某些流行的变量分组方法更好，并且混合NSGA-II具有多目标问题优化的广泛前景。
In this paper, we propose a variable grouping method based on cooperative coevolution for large-scale multi-objective problems (LSMOPs), named Linkage Measurement Minimization (LMM). And for the sub-problem optimization stage, a hybrid NSGA-II with a Gaussian sampling operator based on an estimated convergence point is proposed. In the variable grouping stage, according to our previous research, we treat the variable grouping problem as a combinatorial optimization problem, and the linkage measurement function is designed based on linkage identification by the nonlinearity check on real code (LINC-R). We extend this variable grouping method to LSMOPs. In the sub-problem optimization stage, we hypothesize that there is a higher probability of existing better solutions around the Pareto Front (PF). Based on this hypothesis, we estimate a convergence point at every generation of optimization and perform Gaussian sampling around the convergence point. The samples with good objective value will participate in the optimization as elites. Numerical experiments show that our variable grouping method is better than some popular variable grouping methods, and hybrid NSGA-II has broad prospects for multi-objective problem optimization.
