A novel artificial immune system algorithm with social learning mechanisms (AIS-SL) is proposed in this paper. In AIS-SL,\ncandidate antibodies aremarked with an elitist swarm(ES) or a common swarm(CS). Correspondingly, these antibodies are named\nES antibodies or CS antibodies. In the mutation operator, ES antibodies experience self-learning, while CS antibodies execute two\ndifferent social learning mechanisms, that is, stochastic social learning (SSL) and heuristic social learning (HSL), to accelerate the\nconvergence process. Moreover, a dynamic searching radius update strategy is designed to improve the solution accuracy. In the\nnumerical simulations, five benchmark functions and a practical industrial application of proportional-integral-differential (PID)\ncontroller tuning is selected to evaluate the performance of the proposed AIS-SL. The simulation results indicate that AIS-SL has\nbetter solution accuracy and convergence speed than the canonical opt-aiNet, IA-AIS, and AAIS-2S.
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