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.

当今大多数控制技术的性能取决于特定设计参数的选择。在过去的五年中，用于解决发现最佳设计参数问题的一种通用方法是使用元启发式优化技术。在当前的审查中，我们探索生发中心优化算法（GCO）的使用及其在神经识别和控制中的应用。GCO是一种用于多变量优化的新型人工免疫系统，与其他群优化技术相比，GCO具有自适应领导能力，可以在线修改勘探与开发之间的平衡。当目标函数的形式未知时，这是一个很好的功能。我们还探索了其他用于控制调整的元启发式方法的最新趋势。