Weighted superposition attraction-repulsion (WSAR) algorithm for truss optimization with multiple frequency constraints

Structural optimization of truss structures under multiple frequency constraints is a highly nonlinear and complex optimization problem with non-convex solution space. The optimization method used to solve mentioned problem is expected to provide a very good balance between solution accuracy and computational cost. In this work, the Weighted Superposition Attraction-Repulsion (WSAR) algorithm, which is a recent swarm intelligence based metaheuristic algorithm, is proposed for effective solution of truss optimization problems with multiple natural frequency constraints. The effectiveness and robustness of the WSAR algorithm is studied by solving several planar/space truss structures optimization problems. The optimization results reveal that the successfulness and effectiveness of WSAR in solving truss optimization problems under multiple frequency constraints where WSAR is able to generate the best results in terms of optimized weight and standard deviation compared to the other state-of-the-art metaheuristic algorithms.

Automated summary

The Weighted Superposition Attraction-Repulsion (WSAR) algorithm is proposed for optimizing truss structures under multiple frequency constraints in this work, and it has been shown to outperform other metaheuristic algorithms in terms of optimized weight and standard deviation.