Showing 5 results for Genetic Algorithm
R. Keypour, H. Seifi, A. Yazdian,
Volume 21, Issue 1 (7-2002)
Abstract
In this paper, two algorithms have been developed for allocation and size determination of Active Power Filters (APF) in power systems. In the first algorithm, the objective is to minimize harmonic voltage distortion. The objective in the second algorithm is to minimize the new APF injection currents while satisfying harmonic standards. Genetic algorithm is proposed for these two optimization problems. The simulation results for an 18-bus system show the effectiveness of the genetic algorithm for these two optimization problems.
Keywords: Genetic Algorithm, Active Power Filter, Harmonics, Allocation, Optimization
H. Deldari, T. Ghafarian,
Volume 22, Issue 2 (1-2004)
Abstract
Algorithmic skeleton has received attention as an efficient method of parallel programming in recent years. Using the method, the programmer can implement parallel programs easily. In this study, a set of efficient algorithmic skeletons is introduced for use in implementing parallel genetic algorithm (PGA).A performance modelis derived for each skeleton that makes the comparison of skeletons possible in order to select the best one for the application. The performance of the selected skeleton can be increased by specifying the virtual topology required by the appliation.This is a novel approach with no precedent. Nesting of skeletons used hereis another novelty of the study which has been employed only in few previous studies.
M. H. Bagheripour, E. Shasavandi, and S. M. Marandi,
Volume 25, Issue 2 (1-2007)
Abstract
This paper introduces an accurate, fast, and applicable method for optimization of slip surfaces in earth slopes. Using Genetic Algorithm (GA), which is one of the modern and non-classic optimization methods, in conjunction with the
well -known Bishop applied method, the optimum slip surface in an earth slope is investigated and its corresponding lowest safety factor is determined. Investigations have shown that selection of appropriate variables to define and to solve the problem and determination of a good range for these variables have a profound effect on the speed of convergence in the problem. In the present study, appropriate variables have been defined for solving the problem in a way that the number of repetitions required to reach convergence are considerably reduced by up to 50% compared with other approaches. This has led to a drastic reduction in time and the memory required. The accuracy of the method is shown first by solving examples related to search for optimum failure surfaces of some homogenous, non-homogenous, and earth dam slopes and then by comparison of the results with those of other optimization techniques. In order to show the application of the present method in modern geotechnical engineering, a reinforced earth slope is studied and its failure surface is finally optimized
M.r. Dashtbayazi, R. Esmaeili,
Volume 34, Issue 2 (7-2015)
Abstract
Based on molecular dynamics simulation results, a model was developed for determining elastic properties of aluminum nanocomposites reinforced with silicon carbide particles. Also, two models for prediction of density and price of nanocomposites were suggested. Then, optimal volume fraction of reinforcement was obtained by genetic algorithm method for the least density and price, and the highest elastic properties. Based on optimization results, the optimum volume fraction of reinforcement was obtained equal to 0.44. For this optimum volume fraction, optimum Young’s modulus, shear modulus, the price and the density of the nanocomposite were obtained 165.89 GPa, 111.37 GPa, 8.75 $/lb and 2.92 gr/cm3, respectively.
M. Rezazadeh, R. Emadi, A. Saatchi, A. Ghasemi, M. Rezaeinia,
Volume 35, Issue 3 (12-2016)
Abstract
Simultaneous application of mechanical pressure and electrical charge on powder samples in spark plasma sintering process, has resulted in a sample with a density close to the theory. In the present study, a thermal-electrical-mechanical coupled finite element model of spark plasma sintering system using multi-objective optimization algorithm is proposed to optimize the mold variable. The simulation performed for Si3N4-SiO2 (1:1 mol) specimen has good agreement with the experimental results. Multi-objective genetic algorithms was used for optimization of mold design in order to maximize the temperature of sample core and minimize the mises stress in the mold. The results show that the optimized dimensions cause 8% increase in sample temperature and about 18% decrease in temperature difference between mold surface and sample core. This leads to better uniformity in the porosity distribution of final sample.
