May 7, 2022 · First, the SLMEA explores the mapping relationship between data and labels by constructing a pseudo label matrix, and at the same time, it combines learning in sparse low …

Mar 4, 2024 · 近年来，为解决大规模优化问题，出现了许多多目标进化算法（MOEAs），这些算法基于变量分组、问题转换、新变异操作和概率模型。 这些MOEAs从多种角度接近高维决策 …

SLMEA serves as the first attempt for super-large-scale multi-objective optimization; SLMEA solves problems with up to one million variables in the experiments; SLMEA is a totally black …

Jul 1, 2024 · SLMEA (Tian et al., 2023) is an algorithm customized for dealing with sparse LSMOPs whose search space is extremely large. It achieves the dimensionality reduction by …

dimensional representation with maximum entropy adaptive graph (SLMEA). Firstly, the SLMEA combi-nes the sparse transform representation with pseudo-label matrix learning to optimize,...

Jun 30, 2020 · 多目标优化问题（MaOP）是指涉及三个以上同时要优化的冲突目标的问题，这些问题广泛存在于实际应用中，例如工程设计 [1]，空中交通管制 [2]，地下水监测 [3]和分子设计 …

Feb 1, 2025 · Therefore, this paper proposes a sparse large-scale multi-objective evolutionary algorithm based on sparsity detection. The proposed algorithm uses the two-layer encoding …

The comparative results, based on four performance indicators—hypervolume (HV), inverted generational distance (IGD), averaged Hausdorff distance $\left ({{ \Delta _{p} }}\right)$ , and …

Feb 16, 2025 · SLMEA算法，即Self-Organizing Localized Feature Extraction and Mining Algorithm，是一种新颖的高效数据挖掘算法。 它结合了自组织映射（Self-Organizing Map, …

May 8, 2024 · SLMEA is specialized for super-large-scale sparse multi-objective problems. For fair comparisons, all algorithms adopt the maximum number of function evaluations ( MaxFE ) …