Active control of structures using genetic algorithm with dynamic weighting factors using in the constrained objective function

Abstract

Active control method by improving specification of well-known intelligent numerical search method i.e. genetic algorithm is developed here. This method reduces displacement of the structure by optimizing the control forces at each time step. The efficiency of the genetic algorithm as a part of nature-inspired metaheuristic methods is highly dependent on the constrained objective function. The constrained objective function is achieved by combining the constraints of the optimization problem. There are several methods to numerically combine these constraints. Using appropriate weighting factors to generate this function has been suggested by many researchers. In previous studies, the selection of these factors has been based on experimental or try and error methods and were constant throughout the control period. Proper selection of weighting factors increases the efficiency of the control method. Presenting a new genetic algorithm method in a way selecting weighting factors dynamically over the structural control period is the aim of this paper. Here, weighting factors are non-static in nature and are dynamically selected at each time step according to the memory of the previous step. Numerical results clearly prove the accuracy and efficiency of the proposed control process in comparison with Constant weighting factors methods.