Graph-Based Deep Decomposition for Overlapping Large-Scale Optimization Problems

Decomposition methods play a critical role in cooperative co-evolutionary algorithms (CCEAs) for solving large-scale optimization problems. Although some well-performing decomposition have been designed based on the interactions among variables (IaV), their grouping accuracy is still limited due to poor performance overlapping problems and computational roundoff errors of IaV implementation. To deal with these limitations, graph-based deep (GDD) method proposed obtain more accurate results, especially On one hand, GDD mines information obtains minimum vertex separator interaction graph variables, so as group deeply recursively. other has ability fault tolerance can improve accuracy. For better experimental studies problems, novel function generator random complicate overlap type, two new metrics are evaluate Comprehensive experiments show that greatly help CCEAs perform than existing algorithms, In addition, highly tolerant divide accurately even inaccurate IaV.