Abstract:
:This paper proposes an artificial immune network based on cloud model (AINet-CM) for complex function optimization problems. Three key immune operators-cloning, mutation, and suppression-are redesigned with the help of the cloud model. To be specific, an increasing half cloud-based cloning operator is used to adjust the dynamic clone multipliers of antibodies, an asymmetrical cloud-based mutation operator is used to control the adaptive evolution of antibodies, and a normal similarity cloud-based suppressor is used to keep the diversity of the antibody population. To quicken the searching convergence, a dynamic searching step length strategy is adopted. For comparative study, a series of numerical simulations are arranged between AINet-CM and the other three artificial immune systems, that is, opt-aiNet, IA-AIS, and AAIS-2S. Furthermore, two industrial applications-finite impulse response (FIR) filter design and proportional-integral-differential (PID) controller tuning-are investigated and the results demonstrate the potential searching capability and practical value of the proposed AINet-CM algorithm.
journal_name
Comput Intell Neuroscijournal_title
Computational intelligence and neuroscienceauthors
Wang M,Feng S,Li J,Li Z,Xue Y,Guo Ddoi
10.1155/2017/5901258subject
Has Abstractpub_date
2017-01-01 00:00:00pages
5901258eissn
1687-5265issn
1687-5273journal_volume
2017pub_type
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