An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems
AbstractMOEA/D is a promising multi-objective evolutionary algorithm based on decomposition, and it has been used to solve many multi-objective optimization problems very well. However, there is a class of multi-objective problems, called many-objective optimization problems, but the original MOEA/D cannot solve them well. In this paper, an improved MOEA/D with optimal differential evolution (oDE) schemes is proposed, called MOEA/D-oDE, aiming to solve many-objective optimization problems. Compared with MOEA/D, MOEA/D-oDE has two distinguishing points. On the one hand, MOEA/D-oDE adopts a newly-introduced decomposition approach to decompose the many-objective optimization problems, which combines the advantages of the weighted sum approach and the Tchebycheff approach. On the other hand, a kind of combination mechanism for DE operators is designed for finding the best child solution so as to do the a posteriori computing. In our experimental study, six continuous test instances with 4–6 objectives comparing NSGA-II (nondominated sorting genetic algorithm II) and MOEA/D as accompanying experiments are applied. Additionally, the final results indicate that MOEA/D-oDE outperforms NSGA-II and MOEA/D in almost all cases, particularly in those problems that have complicated Pareto shapes and higher dimensional objectives, where its advantages are more obvious. View Full-Text
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Zheng, W.; Tan, Y.; Fang, X.; Li, S. An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems. Algorithms 2017, 10, 86.
Zheng W, Tan Y, Fang X, Li S. An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems. Algorithms. 2017; 10(3):86.Chicago/Turabian Style
Zheng, Wei; Tan, Yanyan; Fang, Xiaonan; Li, Shengtao. 2017. "An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems." Algorithms 10, no. 3: 86.
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