模糊非基因信息記憶的雙克隆選擇算法

宋丹; 樊曉平; 文中華; 黃大足; 屈喜龍

doi:10.11999/JEIT160359

模糊非基因信息記憶的雙克隆選擇算法

doi: 10.11999/JEIT160359 cstr: 32379.14.JEIT160359

2.
(湖南工程學院計算機與通信學院湘潭 411104) ②(中南大學信息科學與工程學院長沙 410083) ③(湖南財政經(jīng)濟學院信息管理系長沙 410205)

基金項目:

國家自然科學基金(61272295, 61673164, 61402540)，湖南省自然科學基金(2016JJ6031, 2016JJ2040)，湖南省教育廳科學研究項目(16A049, 13A010)

計量
- 文章訪問數(shù): 1494
- HTML全文瀏覽量: 131
- PDF下載量: 561
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-04-14
- 修回日期: 2016-09-20
- 刊出日期: 2017-02-19

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

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)

摘要

摘要: 該文針對傳統(tǒng)智能優(yōu)化算法中虛擬碰撞而導致的全局搜索效率降低的問題，提出一種模糊非基因信息記憶的雙克隆選擇算法。該算法設計基于模糊非基因信息的搜索機制與克隆選擇原理相結合，對抗體進化中的非基因信息進行采集、模糊化并保存到記憶庫，運用這些信息引導該抗體后續(xù)的雙克隆搜索過程，從而減少非優(yōu)區(qū)域的虛擬碰撞，提高全局搜索效率。通過標準測試函數(shù)的仿真試驗并與其他算法比較，新算法表現(xiàn)出更快的全局收斂速度和更高的全局收斂精度。
- 克隆選擇 /
- 智能記憶 /
- 模糊信息 /
- 數(shù)值優(yōu)化
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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參考文獻(26)

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