A novel cooperative coevolution for large scale global optimization

For large scale global optimization problems, the efficiency and effectiveness of evolutionary algorithms (EAs) will be much reduced with the dimension increasing. In this paper, a novel evolutionary algorithm is proposed in order to improve the performance of EAs. In the proposed algorithm, on one hand, a variable grouping strategy is introduced. It can group all variables into several subcomponents, while the variables in each subcomponent are non-separable. In this way, a large scale problem can be decomposed into several small scale problems. On the other hand, a filled function with one parameter is integrated into EAs, which can help algorithm to escape from the current local optimal solution and find a better one. The simulations are made on the standard benchmark suite in CEC´2013, and the proposed algorithm is compared with several well performed algorithms. The results indicate the proposed algorithm is more efficient and effective.