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An Improved Selection Method Based on Crowded Comparison for Multi-Objective Optimization Problems in Intelligent Computing

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Abstract

The main method of dealing with multi-objective optimization problems (MOPs) is the improvements of non-dominated sorting genetic algorithm II (NSGA-II), which have obtained a great success for solving MOPs. It mainly uses a crowded comparison method (CCM) to select the suitable individuals for enter the next generation. However, the CCM requires to need calculate the crowding distance of each individual, which needs to sort the population according to each objective function and it exhausts a lot of computational burdens. To better deal with this problem, we proposes an improved crowded comparison method (ICCM), which combines CCM with the random selection method (RSM) based on the number of selected individuals. The RSM is an operator that randomly selects the suitable individuals for the next generation according to the number of needed individuals, which can reduce the computational burdens significantly. The performance of ICCM is tested on two different benchmark sets (the ZDT test set and the UF test set). The results show that ICCM can reduce the computational burdens by controlling two different selection methods (i.e., CCM and RSM).

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Acknowledgments

This work was partially supported by National Natural Science Foundation of China, CAS Key Technology Talent Program, Shenzhen Technology Project(JSGG20170413171746130), Shenzhen Engineering Laboratory for 3D Content Generating Technologies (NO. [2017] 476). This work was also supported by The Industrial Internet Innovation and Development Project in 2018 Grant MIZ1824020, Guangzhou People's Livelihood Science and Technology Project under Grant 201803010097.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, South China, University of Technology, Guangzhou, 510006, China

    Ying Gao, Binjie Song & Hong Zhao

  2. Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Wuhan, China

    Xiping Hu

  3. Zhengzhou, China

    Yekui Qian & Xinpeng Chen

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  1. Ying Gao
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  2. Binjie Song
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Gao, Y., Song, B., Zhao, H. et al. An Improved Selection Method Based on Crowded Comparison for Multi-Objective Optimization Problems in Intelligent Computing. Mobile Netw Appl 27, 1880–1890 (2022). https://doi.org/10.1007/s11036-019-01403-7

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