Showing 3 results for Shayanfar
M. A. Shayanfar, A. Kaveh, O. Eghlidos , B. Mirzaei,
Volume 6, Issue 2 (6-2016)
Abstract
In this paper, a method is presented for damage detection of bridges using the Enhanced Colliding Bodies Optimization (ECBO) utilizing time-domain responses. The finite element modeling of the structure is based on the equation of motion under the moving load, and the flexural stiffness of the structure is determined by the acceleration responses obtained via sensors placed in different places. Damage detection problem presented in this research is an inverse problem, which is optimized by the ECBO algorithm, and the damages in the structures are fully detected. Furthermore, for simulating the real situation, the effect of measured noises is considered on the structure, to obtain more accurate results.
M. A. Shayanfar, M. A. Barkhordari , M. A. Roudak,
Volume 7, Issue 1 (1-2017)
Abstract
Monte Carlo simulation (MCS) is a useful tool for computation of probability of failure in reliability analysis. However, the large number of samples, often required for acceptable accuracy, makes it time-consuming. Importance sampling is a method on the basis of MCS which has been proposed to reduce the computational time of MCS. In this paper, a new adaptive importance sampling-based algorithm applying the concepts of first-order reliability method (FORM) and using (1) a new simple technique to select an appropriate initial point as the location of design point, (2) a new criterion to update this design point in each iteration and (3) a new sampling density function, is proposed to reduce the number of deterministic analyses. Besides, although this algorithm works with the position of design point, it does not need any extra knowledge and updates this position based on previous generated results. Through illustrative examples, commonly used in the literature to test the performance of new algorithms, it will be shown that the proposed method needs fewer number of limit state function (LSF) evaluations.
M. A. Roudak, M. A. Shayanfar, M. Farahani, S. Badiezadeh, R. Ardalan,
Volume 14, Issue 2 (2-2024)
Abstract
Genetic algorithm is a robust meta-heuristic algorithm inspired by the theory of natural selection to solve various optimization problems. This study presents a method with the purpose of promoting the exploration and exploitation of genetic algorithm. Improvement in exploration ability is made by adjusting the initial population and adding a group of fixed stations. This modification increases the diversity among the solution population, which enables the algorithm to escape from local optimum and to converge to the global optimum even in fewer generations. On the other hand, to enhance the exploitation ability, increasing the number of selected parents is suggested and a corresponding crossover technique has been presented. In the proposed technique, the number of parents to generate offspring is variable during the process and it could be potentially more than two. The effectiveness of the modifications in the proposed method has been verified by examining several benchmark functions and engineering design problems.
