A Danger-Theory-Based Immune Network Optimization Algorithm
Joint Authors
Li, Tao
Xiao, Xin
Shi, Yuanquan
Zhang, Ruirui
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-02-13
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Existing artificial immune optimization algorithms reflect a number of shortcomings, such as premature convergence and poor local search ability.
This paper proposes a danger-theory-based immune network optimization algorithm, named dt-aiNet.
The danger theory emphasizes that danger signals generated from changes of environments will guide different levels of immune responses, and the areas around danger signals are called danger zones.
By defining the danger zone to calculate danger signals for each antibody, the algorithm adjusts antibodies’ concentrations through its own danger signals and then triggers immune responses of self-regulation.
So the population diversity can be maintained.
Experimental results show that the algorithm has more advantages in the solution quality and diversity of the population.
Compared with influential optimization algorithms, CLONALG, opt-aiNet, and dopt-aiNet, the algorithm has smaller error values and higher success rates and can find solutions to meet the accuracies within the specified function evaluation times.
American Psychological Association (APA)
Zhang, Ruirui& Li, Tao& Xiao, Xin& Shi, Yuanquan. 2012. A Danger-Theory-Based Immune Network Optimization Algorithm. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-13.
https://search.emarefa.net/detail/BIM-1033331
Modern Language Association (MLA)
Zhang, Ruirui…[et al.]. A Danger-Theory-Based Immune Network Optimization Algorithm. The Scientific World Journal No. 2013 (2013), pp.1-13.
https://search.emarefa.net/detail/BIM-1033331
American Medical Association (AMA)
Zhang, Ruirui& Li, Tao& Xiao, Xin& Shi, Yuanquan. A Danger-Theory-Based Immune Network Optimization Algorithm. The Scientific World Journal. 2012. Vol. 2013, no. 2013, pp.1-13.
https://search.emarefa.net/detail/BIM-1033331
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1033331