Improved adaptive artificial immune algorithm for solving function optimization problems

被引：0
|
作者：
Meng Y. [1 ]
Wang T. [2 ]
Li Z. [3 ]
Cai J. [1 ]
Zhu S. [1 ]
Han C. [1 ]
机构：
[1] Department of Electronic and Optical Engineering, Army Engineering University, Shijiazhuang
[2] 63769 Unit of PLA, Xi'an
[3] Military Representative Bureau of Army Equipment Department in Xi'an, Xi'an
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2021年 / 47卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Adaptive; Artificial Immune Algorithm (AIA); Function optimization; Immune operator; Immune system;
D O I：
10.13700/j.bh.1001-5965.2020.0058
中图分类号：
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号：
100102 ;
摘要：
In order to overcome the shortcomings of Artificial Immune Algorithm (AIA) used in the function optimization process, such as huge calculation amount, low convergence accuracy and slow convergence speed, multiple adaptive immune operators are introduced, and an Improved Adaptive Artificial Immune Algorithm (IAAIA) is proposed. In the classic AIA, antibody excitation calculation operator is adaptively designed by introducing the number of iterations, and immune selection operator, clone operator, mutation operator and clonal inhibitory operator are adaptively designed by introducing antibody population average excitation and antibody excitation, which can improve the convergence accuracy, convergence speed and stability of AIA. Nine kinds of typical and widely used functions are chosen as experiment function, and four kinds of typical AIAs are selected as comparative algorithms to optimize the experiment functions. The comparative experiment results indicate the effectiveness and superiority of the IAAIA for solving function optimization problems. © 2021, Editorial Board of JBUAA. All right reserved.
引用
收藏
页码：894 / 903
页数：9
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