Multi-populations Covariance Learning Differential Evolution Algorithm

Yongzhao DU; Yuling FAN; Peizhong LIU; Jianeng TANG; Yanmin LUO

doi:10.11999/JEIT180670

Volume 41 Issue 6

Jun. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(6): 1488-1495

Yongzhao DU, Yuling FAN, Peizhong LIU, Jianeng TANG, Yanmin LUO. Multi-populations Covariance Learning Differential Evolution Algorithm[J]. Journal of Electronics & Information Technology, 2019, 41(6): 1488-1495. doi: 10.11999/JEIT180670

Citation:

Yongzhao DU, Yuling FAN, Peizhong LIU, Jianeng TANG, Yanmin LUO. Multi-populations Covariance Learning Differential Evolution Algorithm[J]. Journal of Electronics & Information Technology, 2019, 41(6): 1488-1495. doi: 10.11999/JEIT180670

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Multi-populations Covariance Learning Differential Evolution Algorithm

doi: 10.11999/JEIT180670

1.
College of Engineering, Huaqiao University, Quanzhou 362021, China
2.
College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China
3.
College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China

Funds: The National Natural Science Foundation of China (61605048, 61231002, 51075068), The Fujian Provincial Department of Education Project (JA15035), The Quanzhou Science and Technology Bureau Project (2014Z103, 2015Z114), Huaqiao University Graduate Research Innovation Capacity Development Program Funding Project (1611422002)

Received Date: 2018-07-06
Rev Recd Date: 2019-01-28

Available Online: 2019-02-18

Publish Date: 2019-06-01

Abstract

Abstract

The diversity of the population and the crossover operator algorithm play an important role in solving global optimization problems in Differential Evolution (DE). The Multi-poplutions Covariance learning Differential Evolution (MCDE) algorithm is proposed. Firstly, the population structure is a multi-poplutions mechanism, and each subpopulation combines the corresponding mutation strategy to ensure the individual diversity in the evolutionary process. Then, the covariance learning establishes a proper rotation coordinate system for the crossover operation in the population. At the same time, the adaptive control parameters are used to balance the ability of population survey and convergence. Finally, the proposed algorithm is conducted on 25 benchmark functions including unimodal, multimodal, shifted and high-dimensional test functions and compared with the state-of-the-art evolutionary algorithms. The experimental results show that the proposed algorithm compared with other algorithms has the best effect on solving the global optimization problem.
- Differential Evolution (DE),
- Multi-poplutions,
- Covariance learning,
- Self-adaptive parameter

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References

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