Combining neighborhood utilization technique (NUT) has shown a tremendous benefit to differential evolution (DE) due to that the acquired neighborhood information of population is of great help in guiding the search. However, in most NUT-based DE algorithms, on the one hand, the neighborhood relationships between individuals cannot be effectively and properly learned, and on the other hand, the search roles of different individuals have not yet been fully considered in the design of the NUT. Therefore, this study develops a novel NUT, termed self-organizing neighborhood (SON), with three features: 1) the neighborhood relationships between individuals are incrementally learned and extracted by self-organizing map with the cosine similarity; 2) the neighborhood sizes for different individuals are adaptively adjusted according to their distinct roles in the search; 3) the evolution direction constructed with the neighborhood of each individual is incorporated into the mutation process to guide the search. By combining SON with DE, a SON-based DE (SON-DE) framework is proposed for global optimization. Experimental results on 58 real-parameter functions and 17 real-world problems have demonstrated the superiority of SON-DE in comparison with several state-of-the-art DE algorithms and evolutionary algorithms (EAs).

结合邻域利用技术（NUT）对差分演化（DE）显示出极大的好处，因为获得的人口邻域信息对指导搜索有很大帮助。然而，在大多数基于NUT的DE算法中，一方面，不能有效，正确地学习个体之间的邻域关系，另一方面，在设计过程中尚未充分考虑不同个体的搜索角色。坚果。因此，本研究开发了一种新颖的NUT，称为自组织邻域（SON），它具有三个特征：1）通过具有余弦相似性的自组织图逐步学习和提取个体之间的邻域关系；2）根据不同个体在搜索中的不同角色来自适应地调整其邻域大小；3）将每个个体的邻域构成的进化方向纳入突变过程以指导搜索。通过将SON与DE相结合，提出了基于SON的DE（SON-DE）框架进行全局优化。在58个实参函数和17个实际问题上的实验结果证明，与几种最新的DE算法和进化算法（EA）相比，SON-DE具有优越性。