Comparative Study of Meta-heuristic Algorithms for Damage Detection Problem.

This study presents a comprehensive comparative analysis of several meta-heuristic optimization algorithms for solving the damage detection problem in concrete plate structures. The problem is formulated as a bounded single objective optimization problem. The performance and efficiency of the algorithms are compared under various scenarios using noise-contaminated data. The results show that these meta-heuristics are powerful methods for global optimization and are suitable for solving the damage detection problem. The study compares the performance of these algorithms in: (1) identifying the location and extent of damaged elements, and (2) robustness to noisy data. The proposed meta-heuristic algorithms show promise for solving the damage detection problem. Particularly, the GSK-ALI, MRFO, and Jaya algorithms demonstrate superior performance compared to the other algorithms in identifying damaged elements within concrete plate structures.