Abstract
In recent years, MOEA/D algorithm has been recognized by the industry for its inherent advantages in dealing with super multi objective optimization problems, and its application is also very extensive. However, MOEA/D algorithm also has the problem of lack of population diversity during the later stage of evolution, resulting in slow convergence speed. In this paper, it makes a research on the strategy of maintaining population diversity based on MOEA/D algorithm and proposes three population diversity maintenance strategies, namely SBX-DE operator competition, mutation probability adaptive modulation, and double-faced mirrors theory boundary processing. The experiments’ result shows that all of these three strategies can effectively improve the diversity of the MOEA/D algorithm in the late evolutionary population, and contribute to the convergence speed of the MOEA/D algorithm.
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Acknowledgement
This work was supported by the Key Research and Development Project of Ganzhou, the name is “Research and Application of Key Technologies of License Plate Recognition and Parking Space Guidance in Intelligent Parking Lot”.
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Wang, W., Tao, X., Deng, L., Zeng, J. (2020). Research of Strategies of Maintaining Population Diversity for MOEA/D Algorithm. In: Li, K., Li, W., Wang, H., Liu, Y. (eds) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Science, vol 1205. Springer, Singapore. https://doi.org/10.1007/978-981-15-5577-0_16
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DOI: https://doi.org/10.1007/978-981-15-5577-0_16
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