LAUSR

The truss structure optimization problem is of substantial importance in diverse civil engineering applications. The ultimate goal is to determine the optimal cross-section (bar) areas of elements used in construction systems by minimizing structure weights. Such structure optimization problems can be categorized into three folds: sizing, shaping, and topology optimization. A number of optimization algorithms have recently been introduced to address truss structure with sizing constraints, including evolutionary algorithms, swarm-based algorithms, and trajectory-based algorithms. Here, the problem of size optimization in truss structures is solved using a modified Grey Wolf Optimizer (GWOM) using three different mutation operators. The Grey Wolf Optimizer, a swarm-based algorithm, was recently introduced to mitigate the wolves’ natural behavior in encircling prey and in the hunting process. It has been successfully used to solve a number of optimization problems in both discrete and continuous spaces. Similarly to other optimization algorithms, the main challenge of the GWO is combinatorial and premature convergence. This is due to its navigating behavior over the search space, which is too greedy. One approach to handle greediness and proper balance between exploration and exploitation during the search is controlling mutation operators using appropriate rates. Here, this is achieved using two types of mutation approaches: 1) uniform mutation, and 2) nonuniform mutation. The proposed GWOM versions are evaluated using several benchmark examples of truss structures at 10-bars, 25-bars, 72-bars, and 200-bars. The results are compared with several state-of-the-art methods. The results show that the proposed Optimizer outperforms the comparative methods and fits well with the problem of optimization in truss structures.