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...
| Publicado en: | Scientific World Journal pp. 810320 - 810321 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104251495&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104251495 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2013 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104251495 NLM23483853 2012046526 10.1155/2013/810320 NLM23483853 PMC3590445 104251495 ppf: 810320 ppct: 1 formats: tig: atl: A danger-theory-based immune network optimization algorithm. aug: au: Zhang, Ruirui Li, Tao Xiao, Xin Shi, Yuanquan affil: College of Computer Science, Sichuan University, Chengdu 610065, China. sug: subj: Algorithms Immune System Physiology Immunity, Cellular Immunology Models, Biological Neural Networks (Computer) Antibodies Immunology Cells Human Reproducibility of Results ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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