Hybrid cooperative co-evolution for large scale optimization

In this paper, we propose the idea of hybrid cooperative co-evolution (hCC). In CC, multiple instances of the same evolutionary algorithm work in parallel, each optimizes a different subset of the problem in hand. In recent years, different approaches have been introduced to divide the problem variables into separate groups based on the property of separability. The idea is that when dependent variables are grouped together, a better optimization performance is reached. However, the same evolutionary algorithm is still applied to all groups regardless of the type of variables each group contains. In this work, we propose the use of multiple evolutionary algorithms to optimize the different subsets within the CC framework. We use one algorithm for the non-separable group(s) and another algorithm for the separable group. Experiments carried on the CEC10 benchmarks indicate the promising performance of this proposed approach.