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- MakaleYalnız künye2025Optimal design of inerter-assisted series tuned mass dampers for seismic vibration control of multi-story buildings
Araz, Onur
This work introduces a framework for the optimal design of inerter-assisted series tuned mass dampers (STMDI) placed at the top story of a multi-story building under ground motion and performs comparative research to assess its effectiveness against conventional TMDI. The identical inertance ratio and total mass ratio are taken into consideration for the optimization of both control systems. In the frequency-domain analysis, the five design variables of the STMDI are optimized based on the minimization of the peak amplitude of the displacement transfer function at the top story of the building using the genetic algorithm (GA). The optimized variables are damping ratios (ξ1and ξ2), tuning frequency ratios (f1and f2), and mass ratio of the first to second TMD mass (µh). The proposed control system is applied to a 4-story, an 8-story, and a 16-story case-studies, and the optimal parameters are verified using eighty-five historical benchmark earthquakes in time domain analysis. These records are divided into three different groups according to their frequency content, i.e. PGA/PGV ratio: high, intermediate, and low. The results demonstrate that the STMDI performs better than the TMDI at the same mass and inertance ratio, especially at excitation frequencies close to resonance. The most important advantage of STMDI compared to TMDI is that it provides this performance in a wider frequency range and in an almost similar way. Finally, the seismic performances of STMDI and TMDI are compared considering ground motions with different frequency content.
- MakaleAçık Erişim2021Finding the optimum parameter values of the round robin CPU scheduling algorithm with genetic algorithm; [Döner çekirdek CPU tarifeleme algoritmasinin optimum parametre deǧerlerinin genetik algoritma ile bulunmasi]
Ökdem, Selçuk; Koşmaz, Betül
With the use of computers in every field, the usage intensity of processors is increasing day by day. In order to cope with this intensity, processors need to be constantly updated and improved. Various scheduling algorithms are being developed every day in order to continuously improve and accelerate the processors and to cope with the processing intensity. The main purpose of the scheduling algorithms is to increase the efficiency of the processor. For this, the processor needs to make a planning between the pending processes. Planning can be done by giving different priorities or using different methods. In order to increase the efficiency of the processor, in this paper, existing algorithms were examined. Round Robin Algorithm was improved using Genetic Algorithm. It is aimed to find the optimum values by selecting the quantum value according to the pending processes and their parameters. When the developed new method compared with the existing standard algorithms, it is observed that the waiting time and the turnaround time were shorter than the other algorithms.
- MakaleYalnız künye2018Modeling of Co(II) adsorption by artificial bee colony and genetic algorithm
Öztürk, Nurcan; Sentürk, Hasan Basri; Gündogdu, Ali; Duran, Celal
In this work, it was investigated the usability of artificial bee colony (ABC) and genetic algorithm (GA) in modeling adsorption of Co(II) onto drinking water treatment sludge (DWTS). DWTS, obtained as inevitable byproduct at the end of drinking water treatment stages, was used as an adsorbent without any physical or chemical pre-treatment in the adsorption experiments. Firstly, DWTS was characterized employing various analytical procedures such as elemental, FT-IR, SEM-EDS, XRD, XRF and TGA/DTA analysis. Then, adsorption experiments were carried out in a batch system and DWTS's Co(II) removal potential was modelled via ABC and GA methods considering the effects of certain experimental parameters (initial pH, contact time, initial Co(II) concentration, DWTS dosage) called as the input parameters. The accuracy of ABC and GA method was determined and these methods were applied to four different functions: quadratic, exponential, linear and power. Some statistical indices (sum square error, root mean square error, mean absolute error, average relative error, and determination coefficient) were used to evaluate the performance of these models. The ABC and GA method with quadratic forms obtained better prediction. As a result, it was shown ABC and GA can be used optimization of the regression function coefficients in modeling adsorption experiments.
- MakaleYalnız künye2011Weight minimization of trusses with genetic algorithm
Dede, Tayfun; Bekiroglu, Serkan; Ayvaz, Yusuf
This study is concerned with encoding types such as value encoding and binary encoding for continuous and discrete optimization with a GA, which is coded in FORTRAN and considers stress and displacement constraints, in view of weight minimization of truss structures. Moreover, when continuous optimization is performed, the challenge of huge search space due to the effort of considering continuous set of design variables is overcame by a mechanism introduced as restricted range approach (RRA) in this study. In comparison with the literature, it is concluded that the program developed in this study can be effectively used in the weight minimization of truss structures. It is also came to the conclusion that value encoding overcomes the adverse effects of Hamming-cliff, and that value encoding requires less computer memory and time, never destroys the fit chromosome. (C) 2010 Elsevier B.V. All rights reserved.
- MakaleYalnız künye2009Implementation of different encoding types on structural optimization based on adaptive genetic algorithm
Bekiroǧlu, Serkan; Dede, Tayfun; Ayvaz, Yusuf
In this study, the use of new alternatives of encoding types such as quaternary encoding and octal encoding is reviewed along with that of binary and value encodings and how they provide contribution to efficiency and robustness of genetic algorithm is discussed in structural problems. In addition to new alternatives of encoding types, a genetic algorithm with adaptive manner is presented so that an adaptive approach including adaptive mutation and adaptive crossover operators is employed. It is concluded that it is not possible to tell that one of the encoding types is exactly dominant over the others in all aspects such as convergence, finding the optimum solution, and iteration number. However, it is worth to say that to run genetic process with different encoding types should be considered since one of the runs can give more appropriate solution than that of the others.