An Improved MOEA/D Framework with Multoperator Strategies for Multi-objective Optimization Problems with a Large Scale of Variables

摘要

A novel multi-objective evolutionary algorithm based on decomposition is proposed, in which a multi-population meta-heuristic algorithm named phase based optimization (PBO) is utilized as an effective search engine for enhancing the performance of the original MOEA/D, and is consequently termed MOEA/D-PBO. Three different sub-populations are divided by using the distance between the individuals and the reference point in accordance with the principle from high to low. On this basis, three different search strategies are executed to generate the better solutions according to the position characteristic of individuals with the reference point as the center. The empirical results demonstrate that MOEA/D-PBO can provide much better performance on large scale bi-objective and tri-objective optimization problems than MOEA/D-DE and IM-MOEA.