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The Scientific World Journal
Volume 2013 (2013), Article ID 810320, 13 pages
http://dx.doi.org/10.1155/2013/810320
Research Article

A Danger-Theory-Based Immune Network Optimization Algorithm

1College of Computer Science, Sichuan University, Chengdu 610065, China
2College of Computer Science, Huaihua University, Huaihua 418000, China

Received 20 November 2012; Accepted 26 December 2012

Academic Editors: C. W. Ahn and P. Melin

Copyright © 2013 Ruirui Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.