Hua Yicun, Jin Yaochu, Hao Kuangrong
IEEE Trans Cybern. 2019 Jul;49(7):2758-2770. doi: 10.1109/TCYB.2018.2834466. Epub 2018 Jun 5.
Existing multiobjective evolutionary algorithms (MOEAs) perform well on multiobjective optimization problems (MOPs) with regular Pareto fronts in which the Pareto optimal solutions distribute continuously over the objective space. When the Pareto front is discontinuous or degenerated, most existing algorithms cannot achieve good results. To remedy this issue, a clustering-based adaptive MOEA (CA-MOEA) is proposed in this paper for solving MOPs with irregular Pareto fronts. The main idea is to adaptively generate a set of cluster centers for guiding selection at each generation to maintain diversity and accelerate convergence. We investigate the performance of CA-MOEA on 18 widely used benchmark problems. Our results demonstrate the competitiveness of CA-MOEA for multiobjective optimization, especially for problems with irregular Pareto fronts. In addition, CA-MOEA is shown to perform well on the optimization of the stretching parameters in the carbon fiber formation process.
现有的多目标进化算法（MOEA）在具有规则帕累托前沿的多目标优化问题（MOP）上表现良好，其中帕累托最优解在目标空间中连续分布。当帕累托前沿不连续或退化时，大多数现有算法无法取得良好的结果。为了解决这个问题，本文提出了一种基于聚类的自适应MOEA（CA-MOEA），用于求解具有不规则帕累托前沿的MOP。其主要思想是在每一代自适应地生成一组聚类中心，以指导选择，从而保持多样性并加速收敛。我们研究了CA-MOEA在18个广泛使用的基准问题上的性能。我们的结果证明了CA-MOEA在多目标优化方面的竞争力，特别是对于具有不规则帕累托前沿的问题。此外，CA-MOEA在碳纤维形成过程中拉伸参数的优化方面也表现良好。
