A Modification of MOEA/D for Solving Multi-Objective Optimization Problems
Wei Zheng*,**, Yanyan Tan*,**,†, Meng Gao*,**, Wenzhen Jia*,**, and Qiang Wang*,**
*School of Information Science and Engineering, Shandong Normal University
No. 1, University Road, Changqing District, Jinan 250358, China
**Institute of Data Science and Technology, Shandong Normal University
Jinan, Shandong 250358, China
In this paper, a novel modified algorithm based on MOEA/D, abbreviated as mMOEA/D, is proposed for well solving the multi-objective optimization problems. Our proposed mMOEA/D inherits from MOEA/D. In mMOEA/D, a novel elastic weight vectors design method is introduced and adopted to make those weight vectors spread more widely. On the other hand, a flexible and efficient trail DE operator is designed and used in mMOEA/D for further enhancing the performance of MOEA/D. Three groups of experimental studies are carried out. Proposed mMOEA/D is compared with the four state-the-art multi-objective optimization evolutionary algorithms on solving the multi-objective optimization problems with many objectives, and the other is that mMOEA/D is compared with MOEA/D-DE, an improved version of MOEA/D, on solving the multi-objective optimization problems with complicated PS shapes. The versions of mMOEA/D with the improvement of weight vector and DE operator are compared with MOEA/D-DE to solve multi-objective optimization problems at last. The experimental results show that mMOEA/D performs the best on almost all test instances. In other words, our proposed modification of MOEA/D is effective.
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