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A DEA-based MOEA/D algorithm for portfolio optimization

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Abstract

In this paper, we present a multi-objective genetic algorithm DEA-MOEA/D by integrating decomposition method and DEA (Data Envelopment Analysis) approach. The initial solutions are generated by the DEA approach. Difference operators are adopted as the crossover operator of the parent. We adopt the test functions and portfolio optimization problems to compare the performance of DEA-MOEA/D, FDH-MOGA, MOEA/D and NSGA II. The results show that DEA-MOEA/D performs better than other three algorithms, not only for test functions, but for the portfolio optimization.

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Acknowledgements

We gratefully acknowledge the financial support from the National Natural Science Foundation of China (Nos. 71771082, 71371067) and Hunan Provincial Natural Science Foundation of China (No. 2017JJ1012).

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Authors and Affiliations

  1. School of Business Administration, Hunan University, Changsha, 410082, China

    Zhongbao Zhou, Xianghui Liu, Helu Xiao & Yueyue Liu

  2. Business School, Hunan Normal University, Changsha, 410081, China

    Helu Xiao

  3. College of Economics and Management, Shandong University of Science and Technology, Qingdao, 266590, China

    Shijian Wu

  4. College of Entrepreneurship & Innovation, Quzhou University, Quzhou, 324000, China

    Yueyue Liu

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  1. Zhongbao Zhou
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  2. Xianghui Liu
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  3. Helu Xiao
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  4. Shijian Wu
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  5. Yueyue Liu
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Correspondence to Zhongbao Zhou.

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Zhou, Z., Liu, X., Xiao, H. et al. A DEA-based MOEA/D algorithm for portfolio optimization. Cluster Comput 22 (Suppl 6), 14477–14486 (2019). https://doi.org/10.1007/s10586-018-2316-7

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  • DOI: https://doi.org/10.1007/s10586-018-2316-7

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