Enhancing Cooperative Coevolution with Selective Multiple Populations for Large-Scale Global Optimization

The cooperative coevolution (CC) algorithm features a “divide-and-conquer” problem-solving process. This feature has great potential for large-scale global optimization (LSGO) while inducing some inherent problems of CC if a problem is improperly decomposed. In this work, a novel CC named selective multiple population- (SMP-) based CC (CC-SMP) is proposed to enhance the cooperation of subproblems by addressing two challenges: finding informative collaborators whose fitness and diversity are qualified and adapting to the dynamic landscape. In particular, a CMA-ES-based multipopulation procedure is employed to identify local optima which are then shared as potential informative collaborators. A restart-after-stagnation procedure is incorporated to help the child populations adapt to the dynamic landscape. A biobjective selection is also incorporated to select qualified child populations according to the criteria of informative individuals (fitness and diversity). Only selected child populations are active in the next evolutionary cycle while the others are frozen to save computing resource. In the experimental study, the proposed CC-SMP is compared to 7 state-of-the-art CC algorithms on 20 benchmark functions with 1000 dimensionality. Statistical comparison results figure out significant superiority of the CC-SMP. In addition, behavior of the SMP scheme and sensitivity to the cooperation frequency are also analyzed.