Cloud Model-Based Artificial Immune Network for Complex Optimization Problem.

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 Neurosci

authors

Wang M,Feng S,Li J,Li Z,Xue Y,Guo D

doi

10.1155/2017/5901258

subject

Has Abstract

pub_date

2017-01-01 00:00:00

pages

5901258

eissn

1687-5265

issn

1687-5273

journal_volume

2017

pub_type

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