Optimized control and neural observers with germinal center optimization: A review

摘要

The performance of most of nowadays control techniques depends on the choices of specific designed parameters. In the past five years, a common approach for solving the issue of finding the best designed parameters have been using metaheuristics optimization techniques. In the present review, we explore the use of the germinal center optimization algorithm (GCO) and its applications in neural identification and control. GCO is a novel artificial immune system for multivariate optimization, in contrast with other swarm optimization techniques, GCO have adaptive leadership, this enable to modify online the balance between exploration and exploitation. This is a good feature when the form of the objective function is unknown. We also explore the recent tendencies of other metaheuristics for control tuning. (C) 2019 Published by Elsevier Ltd.