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Population Sizing of Evolutionary Large-Scale Multiobjective Optimization

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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

Large-scale multiobjective optimization problems (LSMOPs) are emerging and widely existed in real-world applications, which involve a large number of decision variables and multiple conflicting objectives. Evolutionary algorithms (EAs) are naturally suitable for multiobjective optimization due to their population-based property, allowing the search of optima simultaneously. Nevertheless, LSMOPs are challenging for conventional EAs, mainly due to the huge volume of search space in LSMOPs. Thus, it is important to explore the impact of the population sizing on the performance of conventional multiobjective EAs (MOEAs) in solving LSMOPs. In this work, we compare several representative MOEAs with different settings of population sizes on some transformer ratio error estimation (TREE) problems in the power system. These test cases are defined on combinations of three population sizes, three TREE problems, and five MOEAs. Our results indicate that the performances of conventional MOEAs with different population sizes in solving LSMOPs are different. The impact of population sizing is most significant for differential evolution based and particle swarm based MOEAs.

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Acknowledgment

This work was supported in part by the National Natural Science Foundation of China under Grant 61903178 and Grant 61906081; in part by the Program for Guangdong Introducing Innovative and Entrepreneurial Teams under Grant 2017ZT07X386; in part by the Shenzhen Peacock Plan under Grant KQTD2016112514355531; and in part by the Program for University Key Laboratory of Guangdong Province under Grant 2017KSYS008.

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Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

    Cheng He & Ran Cheng

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  1. Cheng He
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  2. Ran Cheng
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Editor information

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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He, C., Cheng, R. (2021). Population Sizing of Evolutionary Large-Scale Multiobjective Optimization. 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_4

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  • DOI: https://doi.org/10.1007/978-3-030-72062-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72061-2

  • Online ISBN: 978-3-030-72062-9

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