Frequency Dependence on Image Reconstruction for a Buried Conducting Cylinder

2013 ◽  
Vol 284-287 ◽  
pp. 473-477
Author(s):  
Wei Chien ◽  
Hsien Wei Tseng ◽  
Yung Wen Lee ◽  
Liang Yu Yen ◽  
Chien Ching Chiu
Keyword(s):  
Genetic Algorithm ◽  
Steady State ◽  
Image Reconstruction ◽  
Resonant Frequency ◽  
Frequency Dependence ◽  
Incident Wave ◽  
Optimization Problem ◽  
Quantitative Information ◽  
Frequency Range ◽  
Conducting Cylinder

Frequency dependence on image reconstruction for a buried conducting cylinder is investigated. A conducting cylinder of unknown shape scatters the incident wave in free space and measured the scattered field. By using measured fields, the imaging problem is reformulated into an optimization problem and solved by the steady-state genetic algorithm (SSGA). Numerical results show that the reconstruction is quite well in the resonant frequency range. This work provides both comparative and quantitative information

Image reconstruction for a partially immersed perfectly conducting cylinder using the steady state genetic algorithm

Radio Science ◽  
2004 ◽  
Vol 39 (2) ◽  
pp. n/a-n/a ◽  
Author(s):  
Ching-Lieh Li ◽  
Shao-Hon Chen ◽  
Chih-Ming Yang ◽  
Chien-Ching Chiu
Keyword(s):  
Genetic Algorithm ◽  
Steady State ◽  
Image Reconstruction ◽  
Conducting Cylinder

scholarly journals Modeling and Optimizing the Multi-Objective Portfolio Optimization Problem with Trapezoidal Fuzzy Parameters

2021 ◽  
Vol 26 (2) ◽  
pp. 36
Author(s):  
Alejandro Estrada-Padilla ◽  
Daniela Lopez-Garcia ◽  
Claudia Gómez-Santillán ◽  
Héctor Joaquín Fraire-Huacuja ◽  
Laura Cruz-Reyes ◽  
...  
Keyword(s):  
Steady State ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Solution Process ◽  
Density Estimator ◽  
Nsga Ii ◽  
Spatial Spread ◽  
Multi Objective ◽  
Significant Difference ◽  
Portfolio Optimization Problem

A common issue in the Multi-Objective Portfolio Optimization Problem (MOPOP) is the presence of uncertainty that affects individual decisions, e.g., variations on resources or benefits of projects. Fuzzy numbers are successful in dealing with imprecise numerical quantities, and they found numerous applications in optimization. However, so far, they have not been used to tackle uncertainty in MOPOP. Hence, this work proposes to tackle MOPOP’s uncertainty with a new optimization model based on fuzzy trapezoidal parameters. Additionally, it proposes three novel steady-state algorithms as the model’s solution process. One approach integrates the Fuzzy Adaptive Multi-objective Evolutionary (FAME) methodology; the other two apply the Non-Dominated Genetic Algorithm (NSGA-II) methodology. One steady-state algorithm uses the Spatial Spread Deviation as a density estimator to improve the Pareto fronts’ distribution. This research work’s final contribution is developing a new defuzzification mapping that allows measuring algorithms’ performance using widely known metrics. The results show a significant difference in performance favoring the proposed steady-state algorithm based on the FAME methodology.

scholarly journals EXTENSION OF THE FREQUENCY-RANGE OF INTERFEROMETERS FOR THE "MAGNETIC" COMPONENTS OF GRAVITATIONAL WAVES?

2007 ◽  
Vol 22 (13) ◽  
pp. 2361-2381 ◽  
Author(s):  
CHRISTIAN CORDA
Keyword(s):  
Data Analysis ◽  
Frequency Dependence ◽  
Gravitational Waves ◽  
Response Functions ◽  
Frequency Range ◽  
Total Response ◽  
High Frequencies ◽  
Magnetic Components ◽  
Linear Dimensions ◽  
Dominant Part

Recently, with an enlightening treatment, Baskaran and Grishchuk have shown the presence and importance of the so-called "magnetic" components of gravitational waves (GW's), which have to be taken into account in the context of the total response functions of interferometers for GW's propagating from arbitrary directions. In this paper the analysis of the response functions for the magnetic components is generalized in its full frequency dependence, while in the work of Baskaran and Grishchuk the response functions were computed only in the approximation of wavelength much larger than the linear dimensions of the interferometer. It is also shown that the response functions to the magnetic components grow at high frequencies, differently from the values of the response functions to the well-known ordinary components that decrease at high frequencies. Thus the magnetic components could in principle become the dominant part of the signal at high frequencies. This is important for a potential detection of the signal at high frequencies and confirms that the magnetic contributions must be taken into account in the data analysis. More, the fact that the response functions of the magnetic components grow at high frequencies shows that, in principle, the frequency-range of Earth-based interferometers could extend to frequencies over 10000 Hz.

scholarly journals The Use of a Simulation Model for High-Runner Strategy Implementation in Warehouse Logistics

2020 ◽  
Vol 12 (23) ◽  
pp. 9818
Author(s):  
Gabriel Fedorko ◽  
Vieroslav Molnár ◽  
Nikoleta Mikušová
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Optimization Problem ◽  
Software Tool ◽  
Time Saving ◽  
General Validity ◽  
Resistance Function ◽  
Genetic Algorithm Method ◽  
Working Day ◽  
Enterprise Logistics

This paper examines the use of computer simulation methods to streamline the process of picking materials within warehouse logistics. The article describes the use of a genetic algorithm to optimize the storage of materials in shelving positions, in accordance with the method of High-Runner Strategy. The goal is to minimize the time needed for picking. The presented procedure enables the creation of a software tool in the form of an optimization model that can be used for the needs of the optimization of warehouse logistics processes within various types of production processes. There is a defined optimization problem in the form of a resistance function, which is of general validity. The optimization is represented using the example of 400 types of material items in 34 categories, stored in six rack rows. Using a simulation model, a comparison of a normal and an optimized state is realized, while a time saving of 48 min 36 s is achieved. The mentioned saving was achieved within one working day. However, the application of an approach based on the use of optimization using a genetic algorithm is not limited by the number of material items or the number of categories and shelves. The acquired knowledge demonstrates the application possibilities of the genetic algorithm method, even for the lowest levels of enterprise logistics, where the application of this approach is not yet a matter of course but, rather, a rarity.

scholarly journals An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jing Xiao ◽  
Jing-Jing Li ◽  
Xi-Xi Hong ◽  
Min-Mei Huang ◽  
Xiao-Min Hu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Experimental Comparison ◽  
Software Project ◽  
Project Portfolio ◽  
Multiobjective Evolutionary Algorithms ◽  
Reference Distance ◽  
Nondominated Sorting

As it is becoming extremely competitive in software industry, large software companies have to select their project portfolio to gain maximum return with limited resources under many constraints. Project portfolio optimization using multiobjective evolutionary algorithms is promising because they can provide solutions on the Pareto-optimal front that are difficult to be obtained by manual approaches. In this paper, we propose an improved MOEA/D (multiobjective evolutionary algorithm based on decomposition) based on reference distance (MOEA/D_RD) to solve the software project portfolio optimization problems with optimizing 2, 3, and 4 objectives. MOEA/D_RD replaces solutions based on reference distance during evolution process. Experimental comparison and analysis are performed among MOEA/D_RD and several state-of-the-art multiobjective evolutionary algorithms, that is, MOEA/D, nondominated sorting genetic algorithm II (NSGA2), and nondominated sorting genetic algorithm III (NSGA3). The results show that MOEA/D_RD and NSGA2 can solve the software project portfolio optimization problem more effectively. For 4-objective optimization problem, MOEA/D_RD is the most efficient algorithm compared with MOEA/D, NSGA2, and NSGA3 in terms of coverage, distribution, and stability of solutions.

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