Liu Songbai, Lin Qiuzhen, Tian Ye, Tan Kay Chen
IEEE Trans Cybern. 2022 Dec;52(12):13048-13062. doi: 10.1109/TCYB.2021.3098186. Epub 2022 Nov 18.
Large-scale multiobjective optimization problems (LMOPs) bring significant challenges for traditional evolutionary operators, as their search capability cannot efficiently handle the huge decision space. Some newly designed search methods for LMOPs usually classify all variables into different groups and then optimize the variables in the same group with the same manner, which can speed up the population's convergence. Following this research direction, this article suggests a differential evolution (DE) algorithm that favors searching the variables with higher importance to the solving of LMOPs. The importance of each variable to the target LMOP is quantized and then all variables are categorized into different groups based on their importance. The variable groups with higher importance are allocated with more computational resources using DE. In this way, the proposed method can efficiently generate offspring in a low-dimensional search subspace formed by more important variables, which can significantly speed up the convergence. During the evolutionary process, this search subspace for DE will be expanded gradually, which can strike a good balance between exploration and exploitation in tackling LMOPs. Finally, the experiments validate that our proposed algorithm can perform better than several state-of-the-art evolutionary algorithms for solving various benchmark LMOPs.
大规模多目标优化问题（LMOPs）给传统进化算子带来了重大挑战，因为它们的搜索能力无法有效处理巨大的决策空间。一些新设计的针对LMOPs的搜索方法通常将所有变量分类到不同组中，然后以相同方式对同一组中的变量进行优化，这可以加快种群的收敛速度。沿着这一研究方向，本文提出了一种差分进化（DE）算法，该算法倾向于搜索对解决LMOPs更重要的变量。对每个变量对目标LMOP的重要性进行量化，然后根据重要性将所有变量分类到不同组中。使用DE为重要性较高的变量组分配更多计算资源。通过这种方式，所提出的方法可以在由更重要变量形成的低维搜索子空间中高效地生成后代，这可以显著加快收敛速度。在进化过程中，DE的这个搜索子空间将逐渐扩展，这可以在解决LMOPs时在探索和利用之间取得良好平衡。最后，实验验证了我们提出的算法在解决各种基准LMOPs时比几种最新的进化算法表现更好。
