An Efficient Artificial Immune Network with Elite-Learning

Authors:
Zhonghua Li;Yunong Zhang;Hong-Zhou Tan
Affiliations:
Sun Yat-sen University, China;Sun Yat-sen University, China;Sun Yat-sen University, China
Venue:
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Year:
2007

Citing 0
Cited 4

Review Article: Recent Advances in Artificial Immune Systems: Models and Applications

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IA-AIS: An improved adaptive artificial immune system applied to complex optimization problems

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Optimal scheduling-based RFID reader-to-reader collision avoidance method using artificial immune system

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AINet-SL: Artificial immune network with social learning and its application in FIR filter designing

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Abstract

This paper proposed an efficient artificial immune network (EaiNet) for function optimization with the guide of spirit of particle swarm optimization (PSO). On the one hand, this algorithm absorbs the learning mechanism of PSO, i.e., the elite learning that each individual is capable of learning from the best in the social population. The introduction of the elite learning quickens the convergence speed of EaiNet. On the other hand, EaiNet has self-learning capability, especially when it is stick in the local optima, which will result in finer global optima. Compared to the conventional artificial immune network (aiNet), EaiNet proposed in this paper has better solution quality and faster convergence speed, which indicates that EaiNet is an effective optimization method.