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An improved MOEA/D design for many-objective optimization problems

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Abstract

MOEA/D is one of the most popular multi-objective evolutionary algorithms. To extend the effective application scope of MOEA/D for high-dimensional objectives, an improved MOEA/D design for many-objective optimization problems, named I-MOEA/D, is proposed in this paper. Comparing with the original MOEA/D, we redesigned the weight vectors used in the subproblems, making the distribution of the weight vectors broader and more effective to ensure the diversity and convergence of solutions in the objective space. Moreover, a new decomposition approach, called the weighted mixture-style method, which combines the advantages of the weighted sum decomposition and the Tchebycheff decomposition approaches, is adopted in I-MOEA/D to improve the effectiveness of the algorithm. A three-part experimental comparison using DTLZ1-DTLZ4, with the number of objectives ranging from three to fifteen, is performed. Experimental results verify the effectiveness of each strategy and reveal that the proposed I-MOEA/D method achieves better performance than the other related state-of-the-art algorithms in solving this type of many-objective optimization problems.

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Acknowledgments

The work is partially supported by the National Natural Science Foundation of China (Nos. 61401260, 61373081, 61402268, 61572298, 61602283, and 61702310), the Natural Science Foundation of Shandong, China (Nos. BS2014DX006, BS2015DX016, ZR2014FM012, and ZR2016FB10) and the Taishan Scholar Project of Shandong, China.

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China

    Wei Zheng, Yanyan Tan, Lili Meng & Huaxiang Zhang

  2. Institute of Data Science and Technology, Shandong Normal University, Jinan, 250358, Shandong, China

    Yanyan Tan, Lili Meng & Huaxiang Zhang

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  1. Wei Zheng
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Correspondence to Yanyan Tan.

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Zheng, W., Tan, Y., Meng, L. et al. An improved MOEA/D design for many-objective optimization problems. Appl Intell 48, 3839–3861 (2018). https://doi.org/10.1007/s10489-018-1183-5

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