Tribe–charged system search for parameter configuration of nonlinear systems with large search domains

In most cases, the standard optimization algorithms are capable of identifying the required number of parameters for a specific design problem; however, this process is difficult and inefficient in dealing with some specific complex situations such as confronting large initial search domains. In this article, the tribe–charged system search algorithm is proposed and utilized for parameter identification of magnetorheological fluid dampers. The new method is partitioned into three different phases, called the isolated, communing and united phases, and is developed to overcome the likely early convergence of the standard charged system search algorithm. Two different kinds of Bouc–Wen hysteretic model which simulate the nonlinear behaviour of dampers are used to represent the efficiency and capability of the proposed metaheuristic method. The results show that the new algorithm can be successfully used as a robust method for finding solutions to difficult problems with a large search domain.