A development on multimodal optimization technique and its application in structural damage detection

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. (C) 2020 Elsevier B.V. All rights reserved.