模糊非基因信息记忆的双克隆选择算法

doi:10.11999/JEIT160359

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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.

Key words： Clonal selection Intelligent memory Fuzzy information Numerical optimization

Received: 14 April 2016 Published: 14 November 2016

PACS:

TP391

Fund:

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)

Corresponding Authors: SONG Dan E-mail: s1020d@126.com

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	SONG Dan
	FAN Xiaoping
	WEN Zhonghua
	HUANG Dazu
	QU Xilong

Cite this article:

SONG Dan,FAN Xiaoping,WEN Zhonghua等. Double Clonal Selection Algorithm Based on Fuzzy Non-genetic Information Memory[J]. JEIT, 2017, 39(2): 255-262.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT160359 OR http://jeit.ie.ac.cn/EN/Y2017/V39/I2/255

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