Artificial Immune System Application for Solving Dynamic Optimization Problems

被引：0

作者：

Li, Zhijie ^{[1
,2
]}

Li, Yuanxiang ^{[1
]}

Kuang, Li ^{[1
]}

Yu, Fei ^{[1
]}

机构：

[1] Wuhan Univ, State Key Lab Software Engn, Wuhan 430072, Peoples R China

[2] Hunan Inst Sci & Technol, Comp Sch, Yueyang 414006, Peoples R China

来源：

PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2014年

关键词：

Artificial immune systems; diversity; immune mutation; memory mechanism; immune thermodynamic genetic algorithms; dynamic optimization;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

For the purpose of adaptation to a changing environment, immune mutation and memory mechanism in the immune system are introduced in thermodynamic genetic algorithm, which helps to prevent the diversity loss and rapidly track the optimum in dynamic environments. Experimental results on 0/1 dynamic knapsack problems demonstrate the merits of the proposed immune thermodynamic genetic algorithm (ITDGA). Compared with the existing classical primal-dual genetic algorithm (PDGA), this algorithm can maintain better diversity and be more suitable to solve 0-1 dynamic problems.

引用

页码：2906 / 2911

页数：6

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