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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...
This paper proposes an artificial immune system with social learning (AIS-SoL) for optimization. In the AIS-SoL optimization, the candidate antibodies is separated into two levels i.e., the top level of elitist antibodies (TLEA) and the bottom level of common antibodies (BLCA). Different level of antibodies experience different mutation processes, i.e., a self-learning strategy is used for the TLEA...
This paper proposes an artificial immune system with heuristic social learning (AIS-HSL) for optimization. In the AIS-HSL optimization, the candidate antibodies is separated into two swarms i.e., the elitist swarm (ES) and the common swarm (CS). Different swarms experience different mutation processes, i.e., a self-learning strategy is required for ES, while a heuristic social-learning (HSL) mechanism...
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