Paper Info
Title
A cloud-based artificial immune network for optimization
Abstract
This paper proposes an artificial immune network based on the cloud model (AINet-CM) for complex optimization problems. By introducing the cloud model to evaluate the candidate antibodies, three major immune operators are redesigned to enhance the convergence performance of AINet-CM. These operators are the increasing half cloud-based cloning operator, the asymmetrical cloud-based mutation operator and the normal similarity cloud-based suppression, respectively. Also, a dynamic searching step length is considered. A series of numerical simulations are arranged for technical investigations and three artificial immune systems (i.e., opt-aiNet, IA-AIS and AAIS-2S) are selected for comparison with AINet-CM. The experimental results suggest that the proposed AINet-CM algorithm outperforms the other three immune algorithms in convergence speed and solution accuracy.
Year
DOI
Venue
2013
10.1109/ICNC.2013.6818052
ICNC
Keywords
Field
DocType
function optimization,immune operators,cloud model,ainet-cm,convergence speed,half cloud-based cloning operator,normal similarity cloud-based suppression,asymmetrical cloud-based mutation operator,search problems,artificial immune network,convergence,dynamic searching step length,candidate antibodies,artificial immune systems,cloud-based artificial immune network,complex optimization problems,entropy,immune system,cloning,optimization
Convergence (routing),Mathematical optimization,Artificial immune system,Immune network,Computer science,Function optimization,Operator (computer programming),Optimization problem,Mutation operator,Cloud computing
Conference
Citations 
PageRank 
References 
0
0.34
6
Authors
4
Name
Order
Citations
PageRank
Zhonghua Li1344.59
Jianming Li2232.49
Dongliang Guo362.11
Zhi Yang4625.06