An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
An Efficient Artificial Immune Network with Elite-Learning
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Associative classification with artificial immune system
IEEE Transactions on Evolutionary Computation
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
Omni-aiNet: an immune-inspired approach for omni optimization
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
An artificial immune system architecture for computer securityapplications
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Revisiting the Foundations of Artificial Immune Systems for Data Mining
IEEE Transactions on Evolutionary Computation
Population-Based Incremental Learning With Associative Memory for Dynamic Environments
IEEE Transactions on Evolutionary Computation
A Novel Immune Clonal Algorithm for MO Problems
IEEE Transactions on Evolutionary Computation
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This paper proposes an artificial immune network with social learning (AINet-SL) for complex optimization problems. In AINet-SL, antibodies are divided into two swarms. One is an elitist swarm (ES) where antibodies experience self-learning and the other is a common swarm (CS) where antibodies experience social-learning with different mechanisms, i.e., stochastic social-learning (SSL) and heuristic social-learning (HSL). The elitist antibody to be learned is selected randomly in SSL, while it is determined by the affinity measure in HSL. In order to obtain more accurate solutions, a dynamic searching step length updating strategy is proposed. A series of comparative numerical simulations are arranged among the proposed AINet-SL optimization, Differential Evolution (DE), opt-aiNet, IA-AIS and AAIS-2S. Five benchmark functions and a practical application of finite impulse response (FIR) filter designing are selected as testbeds. The simulation results indicate that the proposed AINet-SL is an efficient method and outperforms DE, opt-aiNet, IA-AIS and AAIS-2S in convergence speed and solution accuracy.