A Novel Hybrid Whale-Chimp Optimization Algorithm for Structural Damage Detection

Damage detection of structures based on swarm intelligence optimization algorithms is an effective method for structural damage detection and key parts of the field of structural health monitoring. Based on the chimp optimization algorithm (ChOA) and the whale optimization algorithm, this paper proposes a novel hybrid whale-chimp optimization algorithm (W-ChOA) for structural damage detection. To improve the identification accuracy of the ChOA, the Sobol sequence is adopted in the population initialization stage to make the population evenly fill the entire solution space. In addition, to improve the local search ability of the traditional ChOA, the bubble-net hunting mechanism and the random search mechanism of the whale optimization algorithm are introduced into the position update process of the ChOA. In this paper, the validity and applicability of the proposed method are illustrated by a two-story rigid frame model and a simply supported beam model. Simulations show that the presented method has much better performance than the ChOA, especially in dealing with multiple damage detection cases. The W-ChOA has good performance in both overcoming misjudgment and improving computational efficiency, which should be a preferred choice in adoption for structural damage detection.

Automated summary

This paper proposes a structural damage detection method based on a hybrid whale-chimp optimization algorithm. By introducing the bubble-net hunting mechanism and random search mechanism, the local search ability of the traditional chimp optimization algorithm is improved. Simulation results show that the proposed method has better performance and effectiveness in dealing with multiple damage detection cases.