Bi-Population-Enhanced Cooperative Differential Evolution for Constrained Large-Scale Optimization Problems
针对约束大规模优化问题中分解法子问题最优解可能变化的问题，提出双种群增强协同差分进化，将子问题种群分为局部和全局两个子种群独立进化，在12个基准测试上表现优于现有算法。
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.