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A Differential Evolution Algorithm for Multi-objective Mixed-Variable Optimization Problems

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Bio-Inspired Computing: Theories and Applications (BIC-TA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1565))

Abstract

Multi-objective mixed-variable optimization problems (MO-MVOPs) are common and complex practical design optimization problems. MO-MVOPs often include multiple complex functions, constraints and mixed types of decision variables. Compared with single objective mixed-variable optimization problems (MVOPs), the decision space of MO-MVOPs presents more complex spatial distribution features. These features of MO-MVOPs make solving such problems face a big challenge. In this paper, fundamental advancements are made to MCDEmv which is previously proposed for single objective MVOPs. This improved version can solve MO-MVOPs, which can be named as MO-MCDEmv. In MO-MCDEmv, the best solution in the population is no longer the solution with the best fitness value, but a random solution in the first rank after executing the fast non-dominated sorting approach in NSGA-II. The generation of offsprings is generated by using the selection operator in NSGA-II. In addition, the local search in MCDEmv is utilized to improve the parents. The quality of the newly generated individual depends on the dominance relationship between itself and its parent. The experimental results of two actual MO-MVOPs are obtained by using two advanced multi-objective algorithms, i.e., CMOEA/D and NSGA-II, and the proposed MO-MCDEmv. The experimental results show that the MO-MCDEmv has better performance than the two advanced multi-objective algorithms.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (61763019, 61966018) and the Science and Technology Foundation of Jiangxi Province (20202BABL202019).

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Authors and Affiliations

  1. School of Information Management, Jiangxi University of Finance and Economics, Nanchang, 330013, People’s Republic of China

    Yupeng Han & Hu Peng

  2. School of Computer and Big Data Science, Jiujiang University, Jiujiang, 332005, People’s Republic of China

    Yupeng Han, Hu Peng, Cong Wang & Fanrong Kong

  3. School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 330022, People’s Republic of China

    Aiwen Jiang & Mengmeng Li

Authors
  1. Yupeng Han
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  2. Hu Peng
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  3. Aiwen Jiang
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  4. Cong Wang
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  5. Fanrong Kong
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  6. Mengmeng Li
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Corresponding author

Correspondence to Hu Peng .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Taiyuan University of Science and Technology, Taiyuan, China

    Zhihua Cui

  3. Taiyuan University of Science and Technology, Taiyuan, China

    Jianghui Cai

  4. Huazhong University of Science and Technology, Wuhan, China

    Lianghao Li

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Han, Y., Peng, H., Jiang, A., Wang, C., Kong, F., Li, M. (2022). A Differential Evolution Algorithm for Multi-objective Mixed-Variable Optimization Problems. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1565. Springer, Singapore. https://doi.org/10.1007/978-981-19-1256-6_11

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  • DOI: https://doi.org/10.1007/978-981-19-1256-6_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1255-9

  • Online ISBN: 978-981-19-1256-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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