A Danger-Theory-Based Immune Network Optimization Algorithm
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 generate...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/810320 |
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| _version_ | 1832562723897999360 |
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| author | Ruirui Zhang Tao Li Xin Xiao Yuanquan Shi |
| author_facet | Ruirui Zhang Tao Li Xin Xiao Yuanquan Shi |
| author_sort | Ruirui Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-327df0babe1e43c9bffb7caedebb590b |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-327df0babe1e43c9bffb7caedebb590b2025-02-03T01:21:49ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/810320810320A Danger-Theory-Based Immune Network Optimization AlgorithmRuirui Zhang0Tao Li1Xin Xiao2Yuanquan Shi3College of Computer Science, Sichuan University, Chengdu 610065, ChinaCollege of Computer Science, Sichuan University, Chengdu 610065, ChinaCollege of Computer Science, Sichuan University, Chengdu 610065, ChinaCollege of Computer Science, Huaihua University, Huaihua 418000, ChinaExisting 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.http://dx.doi.org/10.1155/2013/810320 |
| spellingShingle | Ruirui Zhang Tao Li Xin Xiao Yuanquan Shi A Danger-Theory-Based Immune Network Optimization Algorithm The Scientific World Journal |
| title | A Danger-Theory-Based Immune Network Optimization Algorithm |
| title_full | A Danger-Theory-Based Immune Network Optimization Algorithm |
| title_fullStr | A Danger-Theory-Based Immune Network Optimization Algorithm |
| title_full_unstemmed | A Danger-Theory-Based Immune Network Optimization Algorithm |
| title_short | A Danger-Theory-Based Immune Network Optimization Algorithm |
| title_sort | danger theory based immune network optimization algorithm |
| url | http://dx.doi.org/10.1155/2013/810320 |
| work_keys_str_mv | AT ruiruizhang adangertheorybasedimmunenetworkoptimizationalgorithm AT taoli adangertheorybasedimmunenetworkoptimizationalgorithm AT xinxiao adangertheorybasedimmunenetworkoptimizationalgorithm AT yuanquanshi adangertheorybasedimmunenetworkoptimizationalgorithm AT ruiruizhang dangertheorybasedimmunenetworkoptimizationalgorithm AT taoli dangertheorybasedimmunenetworkoptimizationalgorithm AT xinxiao dangertheorybasedimmunenetworkoptimizationalgorithm AT yuanquanshi dangertheorybasedimmunenetworkoptimizationalgorithm |