A Many-Objective Evolutionary Algorithm with Spatial Division and Angle Culling Strategy

WANG Hongbo; YANG Fan; TIAN Kena; TU Xuyan

doi:10.1049/cje.2021.03.006

Volume 30 Issue 3

May 2021

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Article Navigation > Chinese Journal of Electronics > 2021 > 30(3): 437-443

WANG Hongbo, YANG Fan, TIAN Kena, et al., “A Many-Objective Evolutionary Algorithm with Spatial Division and Angle Culling Strategy,” Chinese Journal of Electronics, vol. 30, no. 3, pp. 437-443, 2021, doi: 10.1049/cje.2021.03.006

Citation:

WANG Hongbo, YANG Fan, TIAN Kena, et al., “A Many-Objective Evolutionary Algorithm with Spatial Division and Angle Culling Strategy,” Chinese Journal of Electronics, vol. 30, no. 3, pp. 437-443, 2021, doi: 10.1049/cje.2021.03.006

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A Many-Objective Evolutionary Algorithm with Spatial Division and Angle Culling Strategy

doi: 10.1049/cje.2021.03.006

School of Computer and Communication Engineering, Beijing Key Lab. of Knowledge Engineering for Materials Science, University of Science and Technology Beijing, Beijing 100083, China

Funds:

This work is supported by the National Natural Science Foundation of China (No.61572074) and National Key Research and Development Program of China (No.2020YFB1712104).

Received Date: 2020-05-13

Abstract

Abstract

In a specific project, how to find a reasonable balance between a plurality of objectives and their optimal solutions has always been an important aspect for researchers. As a trade off between fast convergence and a rich diversity, a Many-objective evolutionary algorithm based on a spatial division and angle-culling strategy (MaOEA-SDAC) is proposed. In the reorganization stage, a restricted matching selection can enhance the reproductivity. In the environment selection stage, a space division and angle-based elimination strategy can effectively improve the convergence and diversity imbalance of its solution set. Through detailed experiments and a comparative analysis of the proposed MaOEA-SDAC with five other state-of-the-art algorithms on classical benchmark problems, the effectiveness of MaOEA-SDAC in solving high-dimensional optimization problems has been verified.
- Many-objective optimization,
- Spatial division,
- Angle culling strategy,
- Evolutionary algorithm

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References(33)

References

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