Evaluating the performance of nature inspired algorithms using 52-bar steel truss subjected to dynamic load

Abstract

Linear programing problem revolutionized during the world war since it has helped the army to minimize the costs and increase the efficiency in battlefield. Since then optimization techniques have gained popularity in various fields like science & technology, biology, mathematics etc. Nature inspired algorithms mimic the nature’s behavior in order to achieve certain optimization objectives which can achieve productive results to complex problems. Classical algorithms like Genetic algorithm, Particle swarm optimization etc. are widely known but the accuracy of the solution for complex engineering problems is less. Performance evaluation of latest nature inspired algorithms i.e. Moth flame optimizer, Salp Swarm optimizer and Whale optimizer is carried out in the present study. These algorithms are proposed recently and they possess salient features like better convergence rate, avoiding the local optimum and robustness, which is the motivation behind choosing these algorithms. A 52-bar steel truss has been chosen for the present study to assess the performance of the chosen optimization techniques. The behavior of steel truss subjected to two different ground motions is also assessed using the three optimization techniques. A comparative study is done to assess the performance of the chosen techniques. MATLAB is adopted for the simulation of chosen problem statement. Based on the results it is observed that Mouth Flame Optimizer has better performance in terms of accuracy, convergence rate and computational time and is suggested for various types of mechanical and structural problems involving 52-bar trusses.