Adapting Reference Vectors and Scalarizing Functions by Growing Neural Gas to Handle Irregular Pareto Fronts
Adapting Reference Vectors and Scalarizing Functions by Growing Neural Gas to Handle Irregular Pareto Fronts
复制标题
通过增长神经气体来调整参考向量和标量化函数来处理不规则帕累托前沿
DOI：
10.1109/tevc.2019.2926151
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发表时间：
2019
影响因子：
14.3
通讯作者：
Yusuke Nojima
中科院分区：
文献类型：
--
作者：
Yiping Liu;Hisao Ishibuchi;Naoki Masuyama;Yusuke Nojima
基于分解的多目标进化算法在求解具有不规则Pareto前沿的多目标优化问题时，其性能往往会明显恶化。其主要原因是参考向量和标量化函数设置不当。在本文中，我们提出了一种基于分解的MOEA指导下的神经气体网络，学习的PF的拓扑结构。参考向量和标度化函数都适应的拓扑结构的基础上，以提高进化算法的搜索能力。所提出的算法进行了比较，与8个国家的最先进的优化34个测试问题。实验结果表明，该方法在处理不规则PF时具有较强的竞争力。
The performance of decomposition-based multiobjective evolutionary algorithms (MOEAs) often deteriorates clearly when solving multiobjective optimization problems with irregular Pareto fronts (PFs). The main reason is the improper settings of reference vectors and scalarizing functions. In this paper, we propose a decomposition-based MOEA guided by a growing neural gas network, which learns the topological structure of the PF. Both reference vectors and scalarizing functions are adapted based on the topological structure to enhance the evolutionary algorithm’s search ability. The proposed algorithm is compared with eight state-of-the-art optimizers on 34 test problems. The experimental results demonstrate that the proposed method is competitive in handling irregular PFs.