Layout, Topology, and Size Optimization of Steel Frame Design Using Metaheuristic Algorithms: A Comparative Study (original) (raw)
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2017
This paper combines particle swarm optimization, grid search method and univariate method as a general optimization approach for any type of problems emphasizing on optimum design of steel frame structures. The new algorithm is denoted as the GSU-PSO. This method attempts to decrease the search space and only searches the space near the optimum point. To achieve this aim, the whole search space is divided into a series of grids by applying the grid search method. By using a method derived from the univariate method, the variables of the best particle change values. Finally, by considering an interval adjustment to the variables and generating particles randomly in new intervals, the particle swarm optimization allows us to swiftly find the optimum solution. This method causes converge to the optimum solution more rapidly and with less number of analyses involved. The proposed GSU-PSO algorithm is tested on several steel frames from the literature. The algorithm is implemented by int...
Structural Design Optimization Using Particle Swarm Optimization and Its Variants
IOP Conference Series: Earth and Environmental Science, 2020
Structural design optimization has become an extremely challenging and more complex task for most real-world practical applications. A huge number of design variables and complex constraints have contributed to the complexity and nonlinearity of the problems. Mathematical programming and gradient-based search algorithms cannot be used to solve nonlinear problems. Thus, researchers have extensively conducted many experimental studies to address the growing complexity of these problems. Metaheuristic algorithms, which typically use nature as a source inspiration, have been developed over past decades. As one of the widely used algorithms, particle swarm optimization (PSO) has been studied and expanded to deal with many complex problems. Particle swarm optimization and its variants have great accuracy in finding the best solution while maintaining its fast convergence behavior. This study aims to investigate PSO and its variants to solve a set of complex structural optimization problem...
Journal of Physics: Conference Series, 2020
A systematic approach of optimization is needed to achieve an optimal design of large and complex truss structures. In the last three decades, several researchers have developed and applied various metaheuristic optimization methods to the design of truss structures. This paper investigates a new metaheuristic algorithm called symbiotic organisms search (SOS) for member sizing optimization of relatively large steel trusses. The case studies include a 120-bar dome truss and a 942-bar tower truss. The structural analyses are carried out using the standard finite element method. The profiles of the truss members are circular hollow structural sections selected from a set of the American Institute of Steel Construction standard profiles. The design results using the SOS are then compared to those obtained using other metaheuristic methods, namely the particle swarm optimization, differential evolution, and teaching-learning-based optimization. The comparison shows the superior performan...
Study on Structural Optimization of truss members using Meta- heuristic Algorithms
IRJET, 2022
In this time of science and advancement, We are using much more technology than ever before to make our work more efficient. However this pace of tech-industry combination hasn’t been caught well in the field of structural engineering. The main aim of this study is to provide additional information about use of Genetic Algorithm and Particle Swarm Optimization to optimize various structural member sections. The objective of this study was achieved through reading and understanding various research papers related to above mentioned topic. The study clarified how the algorithm helps to optimize the structure and which optimization algorithm performs more efficiently.
gazi university journal of science, 2018
Optimum solution of an anticipated problem is generally reached through minimizing or maximizing a governing real function while sometimes should satisfy various predefined limitations. Selecting an algorithm as a main optimizer plays a key role on the solution process. In this respect, current study intends to compare the performances of two different common metaheuristic optimization algorithms as integrated particle swarm optimizer (iPSO) and teaching and learning based optimizer (TLBO). The TLBO is two-phase algorithm while the iPSO is a single-phase algorithm. Their capabilities are compared over some benchmark cases including mathematical functions and structural optimization problems. To increase the complexity of the test problems both size and topology specifications of the structural systems are simultaneously taken as the decision variables. Achieved results demonstrate the superiority of the iPSO in comparison with TLBO in both search capability and convergence rate.
2013
Steel buildings are preferred in residential as well as commercial buildings due to their high strength and ductility particularly in regions where earthquakes mostly happen. In the past, steel buildings were designed by using trial and error strategy or designer experience. However, these strategies were not economical. After improvements in computer technology, many optimization methods have been widely used in structural design problems to obtain economical solutions while satisfying design requirements. Traditional optimization methods are inadequate to find a satisfactory solution to structural optimization problems due to complicated nature and discrete design variables of these problems. Metaheuristic techniques have become efficient tools for structural optimization problems since their emergence. These techniques try to improve the solution by using certain strategies that are generally inspired by natural phenomena. Genetic algorithms, evolutionary strategies, simulating a...
Application of Metaheuristic Algorithms in Truss Structure Sizing Optimization
IOP Conference Series: Earth and Environmental Science, 2020
Field studies of structural optimization have gained increased attention due to the rapid development of metaheuristic algorithms. One widely known metaheuristic algorithm, Particle Swarm Optimization (PSO), has been extensively used to solve many problems and is reported to have fast convergence behavior and good accuracy. As many problems become more complex, studies have been focused on improving PSO searching capability. This study presents the application of PSO and its variants in optimizing truss structures. The performances of PSO and several PSO variants, namely, linearly decreasing inertia weight PSO (LDW-PSO) and bare bones PSO (BB-PSO), were compared and investigated. All optimization algorithms were tested in 72-bar and 25-bar spatial truss problems. The results indicate that BBPSO was the best algorithm in terms of optimum solution, consistency, and convergence behavior.
This work develops an augmented particle swarm optimization (AugPSO) algorithm using two new strategies,: boundary-shifting and particle-position-resetting. The purpose of the algorithm is to optimize the design of truss structures. Inspired by a heuristic, the boundary-shifting approach forces particles to move to the boundary between feasible and infeasible regions in order to increase the convergence rate in searching. The purpose of the particle-position-resetting approach, motivated by mutation scheme in genetic algorithms (GAs), is to increase the diversity of particles and to prevent the solution of particles from falling into local minima. The performance of the AugPSO algorithm was tested on four benchmark truss design problems involving 10, 25, 72 and 120 bars. The convergence rates and final solutions achieved were compared among the simple PSO, the PSO with passive congregation (PSOPC) and the AugPSO algorithms. The numerical results indicate that the new AugPSO algorithm outperforms the simple PSO and PSOPC algorithms. The AugPSO achieved a new and superior optimal solution to the 120-bar truss design problem. Numerical analyses showed that the AugPSO algorithm is more robust than the PSO and PSOPC algorithms.