Abstract
Since genetic algorithm-based optimization methods are computationally expensive for practical use in the field of structural optimization, a resizing technique-based hybrid genetic algorithm for the drift design of multistory steel frame buildings is proposed to increase the convergence speed of genetic algorithms. To reduce the number of structural analyses required for the convergence, a genetic algorithm is combined with a resizing technique that is an efficient optimal technique to control the drift of buildings without the repetitive structural analysis. The resizing technique-based hybrid genetic algorithm proposed in this paper is applied to the minimum weight design of three steel frame buildings. To evaluate the performance of the algorithm, optimum weights, computational times, and generation numbers from the proposed algorithm are compared with those from a genetic algorithm. Based on the comparisons, it is concluded that the hybrid genetic algorithm shows clear improvements in convergence properties.
Highlights
Genetic algorithms (GAs) are a type of evolutionary computation algorithm that exhibits excellent capability in finding the global optimal solution [1,2,3,4] and have been applied to various research fields [5,6,7,8,9,10,11]
It was shown that quality of optimum solutions and convergence speed of Hybrid GA (HGA) were improved by the applications of resizing technique
In the HGA, to increase the convergence speed of genetic algorithms by reducing the number of structural analyses required for the convergence, a genetic algorithm is combined with a resizing technique that is an efficient optimal technique to control the drift of buildings without the repetitiveness structural analysis
Summary
Genetic algorithms (GAs) are a type of evolutionary computation algorithm that exhibits excellent capability in finding the global optimal solution [1,2,3,4] and have been applied to various research fields [5,6,7,8,9,10,11]. GA [16], the Tabu search technique-based GA [17], the optimal criteria technique-based GA [18, 19], and the Taguchi technique-based GA [20] These HGA techniques can be applied to a wide variety of problems; they cannot provide the fundamental solutions to the improvement of the convergence speed because they require complicated sensitivity analysis of structures or reiterated computations in addition to the structural analysis. In this study, a resizing technique-based HGA for the optimal drift design of multistory steel frame buildings is proposed to increase the convergence speed of GAs and reduce the number of structural analyses required for convergence of GAs. To improve the convergence speed of the optimal drift design procedure based on the GA, the GA is combined with a resizing technique that does not require repeated or complicated structural analysis, such as sensitivity analysis, and allows for effective control of lateral displacement [21]. To evaluate the performance of the resizing-based HGA, optimum weights, computational times, and generation numbers from the proposed algorithm are compared with those from the GA
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