Zhao Jiale, Zhang Huijie, Yu Huanhuan, Fei Hansheng, Huang Xiangdang, Yang Qiuling
School of Information and Communication Engineering, Hainan University, Haikou, 570228, China.
Innovation Platform for Academicians of Hainan Province, Hainan University, Haikou, 570228, China.
Sci Rep. 2024 Aug 19;14(1):19140. doi: 10.1038/s41598-024-70145-8.
In recent years, researchers have taken the many-objective optimization algorithm, which can optimize 5, 8, 10, 15, 20 objective functions simultaneously, as a new research topic. However, the current research on many-objective optimization technology also encounters some challenges. For example: Pareto resistance phenomenon, difficult diversity maintenance. Based on the above problems, this paper proposes a many-objective evolutionary algorithm based on three states (MOEA/TS). Firstly, a feature extraction operator is proposed. It can extract the features of the high-quality solution set, and then assist the evolution of the current individual. Secondly, based on Pareto front layer, the concept of "individual importance degree" is proposed. The importance degree of an individual can reflect the importance of the individual in the same Pareto front layer, so as to further distinguish the advantages and disadvantages of different individuals in the same front layer. Then, a repulsion field method is proposed. The diversity of the population in the objective space is maintained by the repulsion field, so that the population can be evenly distributed on the real Pareto front. Finally, a new concurrent algorithm framework is designed. In the algorithm framework, the algorithm is divided into three states, and each state focuses on a specific task. The population can switch freely among these three states according to its own evolution. The MOEA/TS algorithm is compared with 7 advanced many-objective optimization algorithms. The experimental results show that the MOEA/TS algorithm is more competitive in many-objective optimization problems.
近年来，研究人员将能够同时优化5个、8个、10个、15个、20个目标函数的多目标优化算法作为一个新的研究课题。然而，当前对多目标优化技术的研究也遇到了一些挑战。例如：帕累托抗性现象、难以维持多样性。基于上述问题，本文提出了一种基于三种状态的多目标进化算法（MOEA/TS）。首先，提出了一种特征提取算子。它可以提取高质量解集的特征，进而辅助当前个体的进化。其次，基于帕累托前沿层，提出了“个体重要度”的概念。个体的重要度能够反映该个体在同一帕累托前沿层中的重要性，从而进一步区分同一前沿层中不同个体的优劣。然后，提出了一种排斥场方法。通过排斥场来维持目标空间中种群的多样性，使得种群能够均匀地分布在真实的帕累托前沿上。最后，设计了一种新的并发算法框架。在该算法框架中，算法被分为三种状态，每种状态专注于一项特定任务。种群可以根据自身的进化在这三种状态之间自由切换。将MOEA/TS算法与7种先进的多目标优化算法进行了比较。实验结果表明，MOEA/TS算法在多目标优化问题中更具竞争力。
