Studying the effect of weighting factors of the objective function on the performance of the genetic algorithm in active control of structures

In recent years, the use of smart numerical search techniques in active control of structure has been considered by researchers. Genetic algorithm is one of these methods that is based on natural evolution patterns. Here, the genetic algorithm is used to minimize the constrained objective function of the structure and determine the appropriate control forces. By connecting the sensor to the degrees of freedom of the structure, nodal displacement is monitored at each time step and genetic algorithm determines the proper control forces by using constrained objective function and solving dynamic equation of the structure. Choosing appropriate constrained objective function according to the structure conditions has a great impact on the efficiency of the proposed method. This function is obtained by combining the structural constraints and the assumed weighting factors for each constraint. Then, the weighting factors are calculated from the optimization methods. Such a process, leads to achieve a new active control method based on using genetic algorithm. The efficiency and accuracy of the proposed algorithm are investigate by controlling the oscillations of several structures. The results show that the proposed method is able to reduce structural oscillations effectively.