Many-Objective Optimization by Space Partitioning and Adaptive epsilon-Ranking on MNK-Landscapes


This work proposes a method to search effectively on many-objective problems by instantaneously partitioning the objective space into subspaces and performing one generation of the evolutionary search in each subspace. The proposed method uses a partition strategy to define a schedule of subspace sampling, so that different regions of objective space could be emphasized at different generations. In addition, it uses an adaptive c-ranking procedure to re-rank solutions in each subspace, giving selective advantage to some of the solutions initially ranked highest in the whole objective space. Adaptation works to keep the actual number of highest ranked solutions in each subspace close to a desired number. The performance of the proposed method is verified on MNK-Landscapes. Experimental results show that convergence and diversity of the solutions found can improve remarkably oil 4 <= M <= 10 objectives.