A Modification of MOEA/D for Solving Multi-Objective Optimization Problems

• Published in: Journal of advanced computational intelligence and intelligent informatics Vol. 22; no. 2; pp. 214 - 223
• Main Authors: Zheng, Wei, Tan, Yanyan, Gao, Meng, Jia, Wenzhen, Wang, Qiang
• Format: Journal Article
• Language: English
• Published: 20.03.2018
• ISSN: 1343-0130; 1883-8014
• DOI: 10.20965/jaciii.2018.p0214

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.