期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 49, 期 7, 页码 2758-2770出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2018.2834466
关键词
Carbon fiber; evolutionary multiobjective optimization; hierarchical clustering; irregular Pareto front
类别
资金
- Fundamental Research Funds for the Central Universities [2232016D-32]
- National Nature Science Foundation of China [61473078, 61503075, 61603090]
- International Collaborative Project of the Shanghai Committee of Science and Technology [16510711100]
- Agricultural Project of the Shanghai Committee of Science and Technology [16391902800]
- Shanghai Science and Technology Promotion Project from Shanghai Municipal Agriculture Commission [2016-1-5-12]
- U. K. EPSRC [EP/M017869/1]
- EPSRC [EP/M017869/1] Funding Source: UKRI
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.
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