The determination of the location and severity of damage is a crucial task in the damage assessment of structures. In this study, 10 population-based metaheuristic techniques are applied to identify the location and severity of damage in large-scale spatial truss structures. Two examples from the relevant literature are considered under various damage settings to compare the efficacy of the recent swarm intelligence–based optimization methods. A combined natural frequencies and mode shapes–based objective function has been used in the present study for the damage detection purpose. The effect of noise is incorporated in the dynamic responses to study the efficiency of the best algorithm out of 10 possibilities in identifying damages in noisy environments. The results indicate the superiority of teaching-learning-based optimization (TLBO) technique over other optimization techniques in identifying damages with effective precision. Furthermore, with TLBO the number of function evaluations and computation time required in the optimization process is substantially reduced.