Bi-Population-Enhanced Cooperative Differential Evolution for Constrained Large-Scale Optimization Problems

By decomposing the problem into a series of low-dimensional subproblems, cooperative coevolution is an effective method for large-scale optimization problems. This work reveals that when constraints are introduced in decomposition-based methods, the optima of a subproblem might change during the evolution process. Therefore, it is essential to maintain the population diversity in cooperative coevolution. This work proposes a bi-population-enhanced cooperative differential evolution to address this issue. In the proposed method, the population of a subproblem is divided into two subpopulations (local and global) according to a specific strategy. The global and local subpopulations evolve independently, using different differential mutation operators to generate offspring separately without interference. The local subpopulation aims to track and improve the previous optima, while the global subpopulation attempts to find and locate the potential emerging optima. The proposed algorithm is tested on 12 constrained large-scale benchmarks and the experiments show that it can provide highly competitive performance compared to state-of-the-art algorithms. The proposed bi-population strategy is more effective at the lower dimensionality of the subproblem.