In this paper, a heuristic algorithm fusing with niche identification (NIT) and Artificial Bee Colony (ABC) technique is developed to solve multimodal optimization problems, and is then applied for structural damage detection. In order to improve the detection accuracy of the proposed algorithm, the Depth First Search (DFS) is adopted, and a new particle update scheme is proposed to maintain the diversity of particle populations. The effectiveness and robustness of the algorithm for multimodal optimization are demonstrated by the well-known benchmark functions. Case studies on structural damage detection are carried out using ANSYS-powered data. Simulation results show that, even for the contaminated data or extreme damage scenarios (e.g., the adjacent damages), the DFS-based nNIT with ABC technique can lead to a satisfactory result.

本文提出了一种结合小生境识别（NIT）和人工蜂群（ABC）技术的启发式算法来解决多峰优化问题，然后将其应用于结构损伤检测。为了提高算法的检测精度，采用了深度优先搜索（Depth First Search，DFS），并提出了一种新的粒子更新方案，以保持粒子种群的多样性。多模式优化算法的有效性和鲁棒性由众所周知的基准函数证明。利用ANSYS提供的数据进行了结构损伤检测的案例研究。仿真结果表明，即使对于受污染的数据或极端损坏的情况（例如，相邻损坏），采用ABC技术的基于DFS的nNIT也可以得出令人满意的结果。