Abstract
This article investigates the use of Harris Hawks Optimization (HHO) to solve planar and spatial trusses with design variables that are discrete. The original HHO has been used to solve continuous design variables problems. However, HHO is formulated to solve optimization problems with discrete variables in this research. HHO is a population-based metaheuristic algorithm that simulates the chasing style and the collaborative behavior of predatory birds Harris hawks. The mathematical model of HHO uses a straightforward formulation and does not require tuning of algorithmic parameters and it is a robust algorithm in exploitation. The performance of HHO is evaluated using five benchmark structural problems and the final designs are compared with ten state-of-the-art algorithms. The statistical outcomes (average and standard deviation of final designs) show that HHO is quite consistent and robust in solving truss structure optimization problems. This is an important characteristic that leads to better confidence in the final solution from a single run of the algorithm for an optimization problem.
Highlights
For several decades, the development of optimization methods for structures has attracted considerable attention to achieving economic designs
Most design applications in structural engineering; involve selecting sections available in commercial catalogs. In this type of problem, the design variables are discrete. They are integers where the value that is signed to a design variable refers to a specific selection in the design set
The algorithm is based on Particle Swarm Optimization and Genetic Algorithm
Summary
The development of optimization methods for structures has attracted considerable attention to achieving economic designs. Deterministic optimization methods (e.g., linear programming (LP), nonlinear programming (NLP), and dynamic programming (DP)) were developed more than 50 years ago They need gradient information to improve the current solution estimate and they search for designs in the proximity of the current point (Arora, 2017). The algorithm is based on Particle Swarm Optimization and Genetic Algorithm Many of these algorithms found the optimal design for the problems discussed in this study, they require many iterations to find the best design, and the mean and standard deviation of all runs show that they are not very robust. The major motivation for this work is to investigate the use of HHO to solve truss structure optimization problems with design variables selected from a set of sections.
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