Improvement of Immune Genetic Algorithm for Multi-Peak Function Optimization

Q4 Engineering
Wang Jian-chen, Jin Zong-xin
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引用次数: 1

Abstract

The biological immune system when attacked can always find the right antibodies to destroy the antigen and can maintain the diversity of antibodies. The combination of genetic and immunity in biology has been shown to be an effective approach to solving the degeneration of genetic algorithm in the late iterative optimization. According to the characteristic that the injected vaccine immune system can accomplish quickly identification the antigen, an improved immune genetic algorithm (IIGA) is proposed based on this theory for Benchmark function optimization. The results show that the IIGA can effectively prevent the algorithm degenerative during the process of optimization of the genetic algorithm, and improve the convergent speed of the algorithm.
多峰函数优化的免疫遗传算法改进
生物免疫系统在受到攻击时总能找到合适的抗体来消灭抗原,并能保持抗体的多样性。生物学中遗传与免疫的结合已被证明是解决遗传算法在后期迭代优化中退化的有效途径。针对注射疫苗免疫系统能够快速完成抗原识别的特点,提出了一种基于该理论的改进免疫遗传算法(IIGA),用于基准函数优化。结果表明,IIGA能有效地防止遗传算法在优化过程中的算法退化,提高算法的收敛速度。
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来源期刊
电子科技大学学报
电子科技大学学报 Engineering-Electrical and Electronic Engineering
CiteScore
1.40
自引率
0.00%
发文量
7228
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