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Algorithms 2017, 10(3), 86; https://doi.org/10.3390/a10030086

An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems

1
School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
2
Institute of Data Science and Technology, Shandong Normal University, Jinan 250358, China
*
Author to whom correspondence should be addressed.
Received: 13 June 2017 / Revised: 18 July 2017 / Accepted: 24 July 2017 / Published: 26 July 2017
(This article belongs to the Special Issue Evolutionary Computation for Multiobjective Optimization)
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Abstract

MOEA/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
Keywords: many-objective optimization; multi-objective evolutionary optimization based on decomposition (MOEA/D); differential evolutionary schemes many-objective optimization; multi-objective evolutionary optimization based on decomposition (MOEA/D); differential evolutionary schemes
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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.

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