A Many-objective Evolutionary Algorithm Based on Angle Penalized Distance

BI Xiaojun; WANG Chao

doi:10.11999/JEIT170454

Volume 40 Issue 2

Feb. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(2): 314-322

BI Xiaojun, WANG Chao. A Many-objective Evolutionary Algorithm Based on Angle Penalized Distance[J]. Journal of Electronics & Information Technology, 2018, 40(2): 314-322. doi: 10.11999/JEIT170454

Citation:

BI Xiaojun, WANG Chao. A Many-objective Evolutionary Algorithm Based on Angle Penalized Distance[J]. Journal of Electronics & Information Technology, 2018, 40(2): 314-322. doi: 10.11999/JEIT170454

BI Xiaojun, WANG Chao. A Many-objective Evolutionary Algorithm Based on Angle Penalized Distance[J]. Journal of Electronics & Information Technology, 2018, 40(2): 314-322. doi: 10.11999/JEIT170454

Citation:

BI Xiaojun, WANG Chao. A Many-objective Evolutionary Algorithm Based on Angle Penalized Distance[J]. Journal of Electronics & Information Technology, 2018, 40(2): 314-322. doi: 10.11999/JEIT170454

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A Many-objective Evolutionary Algorithm Based on Angle Penalized Distance

doi: 10.11999/JEIT170454

BI Xiaojun¹,
WANG Chao¹

Funds:

The National Natural Science Foundation of China (61175126), The Fundamental Research Funds for the Central Universities (HEUCFP201709)

Received Date: 2017-05-12
Rev Recd Date: 2017-09-22
Publish Date: 2018-02-19

Abstract

Abstract

In order to balance between convergence and distribution in Multi-Objective Evolutionary Algorithms (MOEAs), a Many-Objective Evolutionary Algorithm based on Angle Penalized Distance (MaOEA-APD) is proposed. Firstly, considering the importance of convergence and diversity in the different stages of the evolutionary process, an angle penalized distance is constructed to dynamically balance between them. Then, the environmental selection based on removing the worse individual is designed to maintain the distribution and improve the convergence. Finally, the mating selection is designed based on the principle of the environmental selection. Both are complement and coordinated to each other for improving the evolutionary efficiency of the algorithm. Compared with three state-of-the-art many-objective evolutionary algorithms (MaOEAs), the experimental results on WFG test suite show that MaOEA-APD has more advantage than other algorithms in terms of the overall performance.
- Many-objective optimization,
- Evolutionary algorithm,
- Elimination strategy,
- Angle penalized distance

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References(23)

References

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