An Improved Decomposition-based Multi-objective Evolutionary Algorithm with Enhanced Differential Evolution Strategy
Yingbo Xie, Ying Hou, Junfei Qiao, Baocai Yin · 2019
The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is proved to have a significant advantage dispose of multi-objective optimization problems (MOPs) since its introduction. However, initial MOEA/D worsen diversity, Furthermore, it is easy to generate an inferior solution by using simulated binary crossover operation. Therefore, an improved MOEA/D with an enhanced differential evolution (MOEA/D-EDE) is proposed to solve above problems. The newness and advantages of this proposed MOEA/D-EDE include the following two aspects. First, several differential evolution operators are used to replace crossover operator in the original MOEA/D. Second, MOEA/D-EDE introduces an elite archive strategy, thereby significantly increases the convergence speed while ensuring the diversity. Finally, the proposed MOEA/D-EDE is studied on MOPs compared with several MOEA/D variants and other algorithms. Empirical results display that MOEA/D-EDE to enhance the performance.