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An Investigation of Decomposition-Based Metaheuristics for Resource-Constrained Multi-objective Feature Selection in Software Product Lines

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Evolutionary Multi-Criterion Optimization (EMO 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12654))

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Abstract

The multi-objective feature selection from software product lines is a problem that has attracted increasing attention in recent years. However, current studies on this problem suffer from two main limitations: (1) resource constraints are naturally adhered to the feature selection process, but they are completely ignored or inadequately handled, and (2) there is a strong preference to the use of evolutionary algorithms for the feature selection problem, and the suitability of other multi-objective metaheuristics remains to be fully explored. To address the above two limitations, this paper proposes the multi-objective feature selection with multiple linear and non-linear resource constraints, and investigates the performance of decomposition-based metaheuristics on the proposed problem. We construct a number of problem instances using both artificial and real-world software product lines, considering 2, 3 and 4 objectives. Experimental results show that, within the decomposition-based framework, reproduction operators that are based on probabilistic models (PM) perform better than genetic operators. Moreover, we demonstrate that adaptation of weight vectors can further improve the performance. Finally, we show that PMAD (the combination of PM-based reproduction operators, adaptation of weight vectors and decomposition-based framework) is better than several state-of-the-art algorithms when handling this problem.

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Notes

  1. 1.

    Source codes of PMAD are publicly available at https://doi.org/10.5281/zenodo.4275041.

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Acknowledgment

This paper is supported by National Natural Science Foundation of China (61906069, 61703183, 62006081), Guangdong Basic and Applied Basic Research Foundation (2019A1515011411, 2019A1515011700), Project Funded by China Postdoctoral Science Foundation (2019M662912, 2020M672630), Science and Technology Program of Guangzhou (202002030355, 202002030260), Public Welfare Technology Application Research Plan of Zhejiang (LGG19F030010), and Fundamental Research Funds for the Central Universities (2019MS088).

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Authors and Affiliations

  1. School of Software Engineering, South China University of Technology, Guangzhou, 510006, China

    Yi Xiang, Xianbing Meng, Sizhe Li & Han Huang

  2. School of Mathematics and Systems Science, Guangdong Polytechnic Normal University, Guangzhou, China

    Xue Peng

  3. College of Mathematics Physics and Information Engineering, Jiaxing University, Jiaxing, 314001, China

    Xiaoyun Xia

Authors
  1. Yi Xiang
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  2. Xue Peng
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  3. Xiaoyun Xia
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  4. Xianbing Meng
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  5. Sizhe Li
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  6. Han Huang
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Corresponding author

Correspondence to Han Huang .

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Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Hisao Ishibuchi

  2. City University of Hong Kong, Kowloon Tong, China

    Qingfu Zhang

  3. Southern University of Science and Technology, Shenzhen, China

    Ran Cheng

  4. University of Exeter, Exeter, UK

    Ke Li

  5. Xi'an Jiaotong University, Xi'an, China

    Hui Li

  6. Xidian University, Xi'an, China

    Handing Wang

  7. East China Normal University, Shanghai, China

    Aimin Zhou

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Xiang, Y., Peng, X., Xia, X., Meng, X., Li, S., Huang, H. (2021). An Investigation of Decomposition-Based Metaheuristics for Resource-Constrained Multi-objective Feature Selection in Software Product Lines. In: Ishibuchi, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2021. Lecture Notes in Computer Science(), vol 12654. Springer, Cham. https://doi.org/10.1007/978-3-030-72062-9_52

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  • DOI: https://doi.org/10.1007/978-3-030-72062-9_52

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  • Online ISBN: 978-3-030-72062-9

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