A two-stage multi-objective evolutionary algorithm for large-scale multi-objective optimization

In large-scale multi-objective problems, the traditional offspring generation operators is directionless and blind, which leads to the low searching capability in a huge search space. For this, we propose a two-stage multi-objective evolutionary algorithm, named MOEA-BTS to solve large-scale multi-objective problems(LSMOPs). In MOEA-BTS, the offspring generation process is divided into two stages. In the early stage, a new hybrid of local and global search direction construction method is proposed, aiming to balance the exploitation and exploration of the search. In the late stage, a series of weight vectors divide the decision space into subspaces, where the competitive swarm optimization algorithm is performed for further precise optimizations. Experiments are conducted on the LSMOPs with 500 and 1000 decision variables and results demonstrate that our proposed algorithm can perform better than several state-of-the-art evolutionary algorithms.