Double Clonal Selection Algorithm Based on Fuzzy Non-genetic Information Memory

SONG Dan; FAN Xiaoping; WEN Zhonghua; HUANG Dazu; QU Xilong

doi:10.11999/JEIT160359

Volume 39 Issue 2

Feb. 2017

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SONG Dan, FAN Xiaoping, WEN Zhonghua, HUANG Dazu, QU Xilong. Double Clonal Selection Algorithm Based on Fuzzy Non-genetic Information Memory[J]. Journal of Electronics & Information Technology, 2017, 39(2): 255-262. doi: 10.11999/JEIT160359

Citation:

SONG Dan, FAN Xiaoping, WEN Zhonghua, HUANG Dazu, QU Xilong. Double Clonal Selection Algorithm Based on Fuzzy Non-genetic Information Memory[J]. Journal of Electronics & Information Technology, 2017, 39(2): 255-262. doi: 10.11999/JEIT160359

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Double Clonal Selection Algorithm Based on Fuzzy Non-genetic Information Memory

doi: 10.11999/JEIT160359

1.
2.
(College of Computer and Communication, Hunan Institute of Engineering, Xiangtan 411104, China)

Funds:

The National Natural Science Foundation of China (61272295, 61673164, 61402540), The Natural Science Foundation of Hunan Province (2016JJ6031, 2016JJ2040), The Scientific Research Fund of Hunan Provincial Education Department (16A049, 13A010)

Received Date: 2016-04-14
Rev Recd Date: 2016-09-20
Publish Date: 2017-02-19

Abstract

Abstract

To provide a better solution for search efficiency reduction problem caused by pseudo collision in the traditional intelligent optimization algorithms, this paper proposes a double clonal selection algorithm based on fuzzy non-genetic information memory. By combing with clonal selection theory, the search mechanism based on fuzzy non-genetic information memory is well performed. The non-genetic information in antibody evolution is collected, fuzzified and stored in the memory. Using this information to guide the subsequent double cloning search process, it can reduce the pseudo collision in non-optimal area, thus the global search efficiency is improved greatly. Extensive simulations show that the proposed algorithm has fast global convergence rate and high global convergence accuracy. Comparative results further demonstrate that it performs better than existing algorithms.
- Clonal selection,
- Intelligent memory,
- Fuzzy information,
- Numerical optimization

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References(26)

References

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