A Hybrid Replacement Strategy for MOEA/D

In MOEA/D, the replacement strategy plays a key role in balancing diversity and convergence. However, existing adaptive replacement strategies either focus on neighborhood or global replacement strategy, which may have no obvious effects on balance of diversity and convergence in tackling complicated MOPs. In order to overcome this shortcoming, we propose a hybrid mechanism balancing neighborhood and global replacement strategy. In this mechanism, a probability threshold is applied to determine whether to execute a neighborhood or global replacement strategy, which could balance diversity and convergence. Furthermore, we design an offspring generation method to generate the high-quality solution for each subproblem, which can ease mismatch between subproblems and solutions. Based on the classic MOEA/D, we design a new algorithm framework, called MOEA/D-HRS. Compared with other state-of-the-art MOEAs, experimental results show that the proposed algorithm obtains the best performance.