Variable grouping based differential evolution using an auxiliary function for large scale global optimization

Evolutionary algorithms (EAs) are a kind of efficient and effective algorithms for global optimization problems. However, their efficiency and effectiveness will be greatly reduced for large scale problems. To handle this issue, a variable grouping strategy is first designed, in which the variables with the interaction each other are classified into one group, while the variables without interaction are classified into different groups. Then, evolution can be conducted in these groups separately. In this way, a large scale problem can be decomposed into several small scale problems and this makes the problem solving much easier. Furthermore, an auxiliary function, which can help algorithm to escape from the current local optimal solution and find a better one, is designed and integrated into EA. Based on these, a variable grouping based differential evolution algorithm (briefly, VGDE) using auxiliary function is proposed. At last, the simulations are made on the standard benchmark suite in CEC´2013, and VGDE is compared with several well performed algorithms. The results indicate the proposed algorithm VGDE is more efficient and effective.