The article proposes a multi-objective optimization method called Generalized Differential Evolution (GDE3) for a Variable Number of Dimensions (VND). The well-known generalized differential evolution is adapted to handle problems where the number of decision space variables is not a priori known. The performance of the method is assessed on a set of benchmark problems based on standard multi-objective test cases modified to depend on the number of decision space variables. Experimental results include a study of the setting of controlling parameters and a comparison with the state-of-the-art Variable-Length GDE3 (VLGDE3) algorithm. Finally, the novel approach is compared to the standard approach in a linear antenna array design problem.

文章提出了一种多目标优化方法，称为可变维数 (VND) 的广义差分进化 (GDE3)。众所周知的广义差分进化适用于处理决策空间变量的数量不是先验已知的问题。该方法的性能是在一组基准问题上评估的，这些问题基于标准多目标测试用例，修改为取决于决策空间变量的数量。实验结果包括对控制参数设置的研究以及与最先进的可变长度 GDE3 (VLGDE3) 算法的比较。最后，将新方法与线性天线阵列设计问题中的标准方法进行比较。