Hysteresis is a common phenomenon arising in many engineering applications. It describes a memory-based relation between the restoring force and the displacement. Identification of the hysteretic parameters is central to practical application of the hysteretic models. To proceed so, a noteworthy thing is that the hysteretic models are often complex and non-differentiable so that getting the gradients is never straightforward and therefore, the swarm-based algorithm is often preferable to inverse hysteretic parameter identification. Along these lines, an improved artificial bee colony algorithm is developed in this paper for general hysteretic parameter identification. On the one hand, several hysteretic models along with the extensions to tackle the degradation and pinching behaviours are considered and how to model a structure with hysteretic components is also elaborated. As a result, the governing equation for the direct problem is established. On the other hand, the differential evolution mechanism is introduced to improve the original artificial bee colony algorithm. Numerical examples are conducted to testify the feasibility and accuracy of the proposed method in nonlinear hysteretic parameter identification.

迟滞是许多工程应用中出现的常见现象。它描述了恢复力和位移之间基于记忆的关系。滞后参数的识别是滞后模型实际应用的核心。为此，值得注意的是滞后模型通常是复杂且不可微的，因此获得梯度从来都不是直接的，因此，基于群的算法通常比逆滞后参数识别更可取。沿着这些思路，本文开发了一种改进的人工蜂群算法，用于一般的滞后参数识别。一方面，考虑了几个滞后模型以及用于解决退化和收缩行为的扩展，并且还详细说明了如何对具有滞后分量的结构进行建模。因此，直接问题的控制方程成立。另一方面，引入差分进化机制对原有的人工蜂群算法进行改进。数值算例验证了所提方法在非线性滞后参数辨识中的可行性和准确性。