Comparison of Two Metaheuristic Algorithms on Sizing and Topology Optimization of Trusses and Mathematical Functions

WOS: 000434277000008Optimal solution of a desired optimization problem is mostly obtained via minimizing or maximizing a real function considering several predefined limitations. Selection of proper optimization algorithm as an optimizer tool plays a key role on the solution process. In this respect, current study intends to compare the performances of two different prevalent metaheuristic optimization algorithms. These are integrated particle swarm optimizer (iPSO) and teaching and learning based optimizer (TLBO). The former method is a single-phase algorithm while the latter one is the double-phase algorithm. Capabilities of both algorithms were compared separately on some mathematical benchmark test problems. Furthermore, to exhibit and compare their performance on solving more complex problems, size and topology optimization of the structural systems are also examined. Achieved results demonstrate the superiority of iPSO in comparison with TLBO in both search capability and convergence rate

Comparison of Two Metaheuristic Algorithms on Sizing and Topology Optimization of Trusses and Mathematical Functions

WOS: 000434277000008Optimal solution of a desired optimization problem is mostly obtained via minimizing or maximizing a real function considering several predefined limitations. Selection of proper optimization algorithm as an optimizer tool plays a key role on the solution process. In this respect, current study intends to compare the performances of two different prevalent metaheuristic optimization algorithms. These are integrated particle swarm optimizer (iPSO) and teaching and learning based optimizer (TLBO). The former method is a single-phase algorithm while the latter one is the double-phase algorithm. Capabilities of both algorithms were compared separately on some mathematical benchmark test problems. Furthermore, to exhibit and compare their performance on solving more complex problems, size and topology optimization of the structural systems are also examined. Achieved results demonstrate the superiority of iPSO in comparison with TLBO in both search capability and convergence rate

Optimum Design of Braced Steel Space Frames including Soil-Structure Interaction via Teaching-Learning-Based Optimization and Harmony Search Algorithms

Optimum design of braced steel space frames including soil-structure interaction is studied by using harmony search (HS) and teaching-learning-based optimization (TLBO) algorithms. A three-parameter elastic foundation model is used to incorporate the soil-structure interaction effect. A 10-storey braced steel space frame example taken from literature is investigated according to four different bracing types for the cases with/without soil-structure interaction. X, V, Z, and eccentric V-shaped bracing types are considered in the study. Optimum solutions of examples are carried out by a computer program coded in MATLAB interacting with SAP2000-OAPI for two-way data exchange. The stress constraints according to AISC-ASD (American Institute of Steel Construction-Allowable Stress Design), maximum lateral displacement constraints, interstorey drift constraints, and beam-to-column connection constraints are taken into consideration in the optimum design process. The parameters of the foundation model are calculated depending on soil surface displacements by using an iterative approach. The results obtained in the study show that bracing types and soil-structure interaction play very important roles in the optimum design of steel space frames. Finally, the techniques used in the optimum design seem to be quite suitable for practical applications