A novel artificial immune system algorithm with social learning mechanisms (AIS-SL) is proposed in this paper. In AIS-SL, candidate antibodies are marked with an elitist swarm (ES) or a common swarm (CS). Correspondingly, these antibodies are named ES antibodies or CS antibodies. In the mutation operator, ES antibodies experience self-learning, while CS antibodies execute two different social learning mechanisms, that is, stochastic social learning (SSL) and heuristic social learning (HSL), to accelerate the convergence process. Moreover, a dynamic searching radius update strategy is designed to improve the solution accuracy. In the numerical simulations, five benchmark functions and a practical industrial application of proportional-integral-differential (PID) controller tuning is selected to evaluate the performance of the proposed AIS-SL. The simulation results indicate that AIS-SL has better solution accuracy and convergence speed than the canonical opt-aiNet, IA-AIS, and AAIS-2S.
The local discharge and wall charge distribution on dielectric surface in coplanar dielectric barrier discharge have been studied experimentally by employing a segment-electrode system. The results show that the local discharge currents on the segment electrodes are different when the segments act as cathode and/or anode, but the charge transfers during the current pulses are symmetric on the correlative parts of the electrodes. The wall charge distribution and the wall voltage during afterglow are uniform on the dielectric layer above the segments near the coplanar gap, while they decrease outwards in the outer side of the electrode if the voltage supply is not high enough. The segment-electrode configuration provides a possible way to investigate the local processes of the discharge in dielectric barrier discharge.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.