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U-NSGA-III: An Improved Evolutionary Many-Objective Optimization Algorithm

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Bio-inspired Computing: Theories and Applications (BIC-TA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 951))

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Abstract

The Non-dominated Sorting Genetic Algorithm III (NSGA-III) uses a niche selection strategy based on reference points to maintain the population diversity. However, in an evolutionary process, areas near certain reference points which have no solution attached cannot be searched. To ensure the algorithm searching the entire solution space, and in particular, to avoid some areas not being explored due to no solution existing in the regions currently, we propose a uniform pool reservation strategy based on reference points in this paper. The strategy uses the individuals which are the closest to each reference point to guarantee population diversity. The improved algorithm is compared with classical algorithms based on decomposition and other improved algorithms based on NSGA-III respectively. The performance of each algorithm is evaluated by using inverted generational distance (IGD) and spread. The experimental results show the performance of the improved algorithm.

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Acknowledgments

We would like to acknowledge the support from the National Science Foundation of China (61472095), Heilongjiang Province Natural Science Foundation (F2016039) and Research Foundation of Education Department of Heilongjiang (1352MSYYB016). This paper is also funded by the International Exchange Program of Harbin Engineering University for Innovation oriented Talents Cultivation.

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Harbin Engineering University, Harbin, China

    Rui Ding, Hongbin Dong, Xiaodong Yu & Lijie Li

  2. College of Computer Science and Information Technology, Mudanjiang Normal University, Mudanjiang, China

    Rui Ding & Xianbin Feng

  3. School of Science and Technology, Nottingham Trent University, Nottingham, UK

    Jun He

Authors
  1. Rui Ding
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  2. Hongbin Dong
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  3. Jun He
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  4. Xianbin Feng
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  5. Xiaodong Yu
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  6. Lijie Li
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Corresponding author

Correspondence to Hongbin Dong .

Editor information

Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Jianyong Qiao

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Xinchao Zhao

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

    Linqiang Pan

  4. Beijing University of Posts and Telecommunications, Beijing, China

    Xingquan Zuo

  5. Anhui University, Hefei, China

    Xingyi Zhang

  6. City University of Hong Kong, Kowloon, Hong Kong

    Qingfu Zhang

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Shanguo Huang

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Ding, R., Dong, H., He, J., Feng, X., Yu, X., Li, L. (2018). U-NSGA-III: An Improved Evolutionary Many-Objective Optimization Algorithm. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 951. Springer, Singapore. https://doi.org/10.1007/978-981-13-2826-8_3

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  • DOI: https://doi.org/10.1007/978-981-13-2826-8_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2825-1

  • Online ISBN: 978-981-13-2826-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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