Identification of structural models using a modified Artificial Bee Colony algorithm

A modified version of the Artificial Bee Colony (ABC) algorithm is presented to identify structural systems. ABC is a heuristic algorithm with simple structure, ease of implementation and robustness. A nonlinear factor for convergence control is introduced in the algorithm to enhance the balance of global and local searches. To investigate the applicability of this proposed technique to system identification, three examples are studied under different conditions regarding data availability, noise pollution level, priori knowledge of parameters, etc. Simulation results show the proposed technique produces excellent parameter estimation, even with few measurements and high noise corruptions.

► A modified Artificial Bee Colony algorithm is proposed to identify structural models. ► A nonlinear factor is introduced for convergence control in the ABC algorithm. ► The algorithm effectiveness, robustness and efficiency are verified by three examples. ► The proposed algorithm outperforms other heuristic algorithms in terms of accuracy. ► Good parameter estimation can be obtained even with partial noise polluted data.