Geng Huantong, Zhou Zhengli, Shen Junye, Song Feifei
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China.
School of Information Technology, Jiangsu Open University, Nanjing 210036, China.
Entropy (Basel). 2022 Dec 21;25(1):13. doi: 10.3390/e25010013.
The main challenge for constrained many-objective optimization problems (CMaOPs) is how to achieve a balance between feasible and infeasible solutions. Most of the existing constrained many-objective evolutionary algorithms (CMaOEAs) are feasibility-driven, neglecting the maintenance of population convergence and diversity when dealing with conflicting objectives and constraints. This might lead to the population being stuck at some locally optimal or locally feasible regions. To alleviate the above challenges, we proposed a dual-population-based NSGA-III, named DP-NSGA-III, where the two populations exchange information through the offspring. The main population based on the NSGA-III solves CMaOPs and the auxiliary populations with different environment selection ignore the constraints. In addition, we designed an ε-constraint handling method in combination with NSGA-III, aiming to exploit the excellent infeasible solutions in the main population. The proposed DP-NSGA-III is compared with four state-of-the-art CMaOEAs on a series of benchmark problems. The experimental results show that the proposed evolutionary algorithm is highly competitive in solving CMaOPs.
约束多目标优化问题（CMaOPs）面临的主要挑战是如何在可行解和不可行解之间取得平衡。现有的大多数约束多目标进化算法（CMaOEAs）都是可行性驱动的，在处理相互冲突的目标和约束时忽略了种群收敛性和多样性的维护。这可能导致种群被困在一些局部最优或局部可行区域。为了缓解上述挑战，我们提出了一种基于双种群的NSGA-III，称为DP-NSGA-III，其中两个种群通过后代交换信息。基于NSGA-III的主要种群求解CMaOPs，而具有不同环境选择的辅助种群忽略约束。此外，我们结合NSGA-III设计了一种ε-约束处理方法，旨在利用主要种群中优秀的不可行解。在一系列基准问题上，将所提出的DP-NSGA-III与四种最先进的CMaOEAs进行了比较。实验结果表明，所提出的进化算法在求解CMaOPs方面具有很强的竞争力。
