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Large-scale evolutionary optimization: a survey and experimental comparative study

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Abstract

In the last decades, global optimization problems are very common in many research fields of science and engineering and lots of evolutionary computation algorithms have been used to deal with such problems, such as differential evolution (DE) and particle swarm optimization (PSO). However, the algorithms performance rapidly decreases as the increasement of the problem dimension. In order to solve large-scale global optimization problems more efficiently, a lot of improved evolutionary computation algorithms, especially the improved DE or improved PSO algorithms have been proposed. In this paper, we want to analyze the differences and characteristics of various large-scale evolutionary optimization (LSEO) algorithms on some benchmark functions. We adopt the CEC2010 and the CEC2013 large-scale optimization benchmark functions to compare the performance of seven well-known LSEO algorithms. Then, we try to figure out which algorithms perform better on different types of benchmark functions based on simulation results. Finally, we give some potential future research directions of LSEO algorithms and make a conclusion.

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Acknowledgements

This work was supported in part by the Outstanding Youth Science Foundation under Grant 61822602, in part by the National Natural Science Foundations of China (NSFC) under Grant 61772207 and Grant 61873097, in part by the Guangdong Natural Science Foundation Research Team under Grant 2018B030312003, and in part by the Guangdong-Hong Kong Joint Innovation Platform under Grant 2018B050502006.

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Authors and Affiliations

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

    Jun-Rong Jian, Zhi-Hui Zhan & Jun Zhang

  2. State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou, 510006, China

    Jun-Rong Jian, Zhi-Hui Zhan & Jun Zhang

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  1. Jun-Rong Jian
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Correspondence to Zhi-Hui Zhan.

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Jian, JR., Zhan, ZH. & Zhang, J. Large-scale evolutionary optimization: a survey and experimental comparative study. Int. J. Mach. Learn. & Cyber. 11, 729–745 (2020). https://doi.org/10.1007/s13042-019-01030-4

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