scholarly journals Satellite Constellation Orbit Design Optimization with Combined Genetic Algorithm and Semianalytical Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Tania Savitri ◽  
Youngjoo Kim ◽  
Sujang Jo ◽  
Hyochoong Bang
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Semimajor Axis ◽  
Low Earth Orbit ◽  
Computational Time ◽  
Target Point ◽  
Target Area ◽  
Orbital Elements ◽  
Satellite Constellation ◽  
Fitness Value

This paper focuses on maximizing the percent coverage and minimizing the revisit time for a small satellite constellation with limited coverage. A target area represented by a polygon defined by grid points is chosen instead of using a target point only. The constellation consists of nonsymmetric and circular Low Earth Orbit (LEO) satellites. A global optimization method, Genetic Algorithm (GA), is chosen due to its ability to locate a global optimum solution for nonlinear multiobjective problems. From six orbital elements, five elements (semimajor axis, inclination, argument of perigee, longitude of ascending node, and mean anomaly) are varied as optimization design variables. A multiobjective optimization study is conducted in this study with percent coverage and revisit time as the two main parameters to analyze the performance of the constellation. Some efforts are made to improve the objective function and to minimize the computational load. A semianalytical approach is implemented to speed up the guessing of initial orbital elements. To determine the best parametric operator combinations, the fitness value and the computational time from each study cases are compared.

scholarly journals Hybrid Real-Coded Genetic Algorithm and Variable Neighborhood Search for Optimization of Product Storage

2019 ◽  
Vol 4 (2) ◽  
pp. 166
Author(s):  
Nindynar Rikatsih ◽  
Wayan Firdaus Mahmudy ◽  
Syafrial Syafrial
Keyword(s):  
Genetic Algorithm ◽  
Variable Neighborhood Search ◽  
Computational Time ◽  
Neighborhood Search ◽  
Agricultural Product ◽  
Local Optimum ◽  
Optimum Result ◽  
Fitness Value ◽  
Real Coded Genetic Algorithm ◽  
Product Storage

Agricultural product storage has a problem that need to be noticedbecause it has an impact in gaining the profit according to the number ofproducts and the capacity of storage. Inappropriate combination of productcauses high expenses and low profit. To solve the problem, we propose geneticalgorithm (GA) as the optimization method. Although GA is good enough tosolve the problem, GA not always gives an optimum result in complex searchspaces because it is easy to be trapped in local optimum. Therefore, we presenta hybrid real-coded genetic algorithm and Variable Neighborhood Search(HRCGA-VNS) to solve the problem. VNS is applied after reproductionprocess of GA to repair the offspring and improve GA exploitation capabilitiesin local area to get better result. The test results show that the optimal popsizeof GA is 180, number of generations is 80, combination of cr and mr is 0.7 and0.3 while optimum Kmax of VNS is 40 with number of iterations 50. Eventhough HRCGA-VNS need longer computational time, HRCGA-VNS hasproven to provide a better result based on higher fitness value compared withclassical GA and VNS.

scholarly journals LEO Hybrid Satellite Constellation Design Based on Multi-purpose Genetic Algorithm to Optimize Cost and Reliability of Global Coverage

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Genetic Algorithm ◽  
System Reliability ◽  
Optimization Design ◽  
Low Cost ◽  
Satellite Constellation ◽  
Constellation Design ◽  
Effective Role ◽  
Global Coverage ◽  
Reliability Performance ◽  
Cost Of Construction

Assessing the reliability of systems plays an effective role in the constellation design. Genetic Algorithm can be applied for the optimization design of satellite constellation, which are imperative in various fields like communication, surveillance and navigation. Opposite goals, such as optimizing performance and reducing the number of satellites in constellations along with low cost of construction and launch, have been analyzed in this paper. In the design of constellations, launching and replacing unhealthy satellites to avoid breakdown, and the time costing has a major impact on the level of system reliability performance. A method to design hybrid constellation for communication and navigation is proposed in this paper, it takes coverage capability and precession into consideration. According to LEO constellation, The issue of optimizing the number of satellites and other effective panels in constellation design has been discussed. The genetic algorithm is designed to the hybrid LEO constellations design by using a methodology of coverage constellation. It provides the optimal solutions for enhancing capability of communication and navigation. The simulation results confirm the performance of the proposed algorithm and indicates that it is feasible and effective Accordingly, in this paper, after designing the constellations using the genetic algorithm, we draw the final constellation diagram block and evaluating the conspicuous performance and reliability at the time of request.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

Cazacu and Barlat Criterion Identification Using the Cylindrical Cup Deep Drawing Test and the Coupled Artificial Neural Networks – Genetic Algorithm Method

2012 ◽  
Vol 504-506 ◽  
pp. 637-642 ◽  
Author(s):  
Hamdi Aguir ◽  
J.L. Alves ◽  
M.C. Oliveira ◽  
L.F. Menezes ◽  
Hedi BelHadjSalah
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Deep Drawing ◽  
Inverse Analysis ◽  
Computational Time ◽  
Ann Model ◽  
Drawing Test ◽  
Artificial Neural ◽  
Deep Drawing Test ◽  
Cylindrical Cup

This paper deals with the identification of the anisotropic parameters using an inverse strategy. In the classical inverse methods, the inverse analysis is generally coupled with a finite element code, which leads to a long computational time. In this work an inverse analysis strategy coupled with an artificial neural network (ANN) model is proposed. This method has the advantage of being faster than the classical one. To test and validate the proposed approach an experimental cylindrical cup deep drawing test is used in order to identify the orthotropic material behaviour. The ANN model is trained by finite element simulations of this experimental test. To reduce the gap between the experimental responses and the numerical ones, the proposed method is coupled with an optimization procedure based on the genetic algorithm (GA) to identify the Cazacu and Barlat’2001 material parameters of a standard mild steel DC06.

Performance Study of Harmony Search Algorithm for Multilevel Thresholding

2016 ◽  
Vol 25 (4) ◽  
pp. 473-513 ◽  
Author(s):  
Salima Ouadfel ◽  
Abdelmalik Taleb-Ahmed
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Computational Time ◽  
Multilevel Thresholding ◽  
Performance Study ◽  
Bacterial Foraging Optimization ◽  
Bee Colony ◽  
Fitness Value ◽  
Multiple Thresholds

AbstractThresholding is the easiest method for image segmentation. Bi-level thresholding is used to create binary images, while multilevel thresholding determines multiple thresholds, which divide the pixels into multiple regions. Most of the bi-level thresholding methods are easily extendable to multilevel thresholding. However, the computational time will increase with the increase in the number of thresholds. To solve this problem, many researchers have used different bio-inspired metaheuristics to handle the multilevel thresholding problem. In this paper, optimal thresholds for multilevel thresholding in an image are selected by maximizing three criteria: Between-class variance, Kapur and Tsallis entropy using harmony search (HS) algorithm. The HS algorithm is an evolutionary algorithm inspired from the individual improvisation process of the musicians in order to get a better harmony in jazz music. The proposed algorithm has been tested on a standard set of images from the Berkeley Segmentation Dataset. The results are then compared with that of genetic algorithm (GA), particle swarm optimization (PSO), bacterial foraging optimization (BFO), and artificial bee colony algorithm (ABC). Results have been analyzed both qualitatively and quantitatively using the fitness value and the two popular performance measures: SSIM and FSIM indices. Experimental results have validated the efficiency of the HS algorithm and its robustness against GA, PSO, and BFO algorithms. Comparison with the well-known metaheuristic ABC algorithm indicates the equal performance for all images when the number of thresholds M is equal to two, three, four, and five. Furthermore, ABC has shown to be the most stable when the dimension of the problem is too high.

Sign in / Sign up

Export Citation Format

Share Document