A novel hybrid immune algorithm and its convergence based on the steepest descent algorithm

被引:11
作者
Liu, X. Y. [1 ]
Zhang, A. L. [1 ,2 ]
Gao, Y. L. [1 ]
Zhao, W. [1 ]
机构
[1] Inner Mongolia Univ Technol, Coll Sci, Hohhot 010051, Peoples R China
[2] Changzhi Coll, Dept Math, Changzhi 046011, Peoples R China
关键词
Artificial immune algorithm; Steepest descent algorithm; Quasi-descent method; Convergence; TURNING OPERATIONS; GENETIC ALGORITHM; OPTIMIZATION; DESIGN;
D O I
10.1016/j.amc.2011.06.010
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
This paper proposes a novel hybrid immune algorithm (HIA) that can overcome the typical drawback of the artificial immune algorithm (AIA), which runs slowly and experiences slow convergence. The HIA combines the adaptive AIA based on the steepest descent algorithm. The HIA fully displays global search ability and the global convergence of the immune algorithm. At the same time, it inserts a quasi-descent operator to strengthen its local search ability. A good convergence of the HIA with the quasi-descent idea is shown as well. Numerical experiment results show that the HIA successfully improves running speed and convergence performance. (C) 2011 Elsevier Inc. All rights reserved.
引用
收藏
页码:1291 / 1296
页数:6
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