Magnetorheological damper parameter identification using a novel metaheuristic algorithm

Abstract

In this study, a new optimization algorithm is proposed based on swarm intelligence theory. In the algorithm, the movement of each individual toward the best member of the population is not represented as a linear vector; instead, it can change nonlinearly according to the discrete‐time that is represented by the iteration number. By implementing this change, the speed of each member of the population can be controlled, which results in finding a better solution. As there is a dependency on the iteration number, it is named the Iteration Dependent Optimizer (IDO). The performance of the proposed algorithm is evaluated based on unconstrained unimodal and multimodal benchmark functions and constrained engineering optimization problems. Then the algorithm is used to solve a practical problem in civil engineering, in which unknown parameters of the model of a large‐scale magnetorheological (MR) damper, which is commonly used in structural control, are identified. The results of statistical study and direct comparisons demonstrate the advantages and capabilities of the proposed model and indicate that IDO can compete favorably with some other well‐studied algorithms.