A new grouping strategy-based hybrid algorithm for large scale global optimization problems

Large scale global optimization (LSGO) problems are a kind of very challenging problems due to their high nonlinearity, high dimensionality and too many local optimal solutions. The variable grouping strategies including black-box grouping strategies and white-box grouping strategy are the most hopeful strategies which can decompose a large scale problem into several smaller scale sub-problems and make the problem solving become easier. In this paper, we first propose a new variable grouping strategy which can be applicable to fully non-separable LSGO problems. Then, a new line search method is designed which can make a quick scan to arrive in promising regions and help the new variable grouping strategy to divide the LSGO problem properly. Furthermore, a differential evolutionary (DE) algorithm with a new mutation strategy is designed. Combining all these, a new hybrid algorithm for LSGO problems is proposed.