Simulated Annealing and Genetic Algorithm Approaches for Discontinuous Coverage Satellite Constellation Design

Crossley, William A.; Williams, Edwin A.

doi:10.1080/03052150008941304

Cited by 38 publications

(18 citation statements)

References 2 publications

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“…The genetic algorithm approach for discontinuous satellite constellations was introduced in [34] and [40]. Herein [34] and [40], satellite constellation numbers are examined over a range of the altitudes for various discontinuous coverages.…”

Section: Ga For Discontinuous Global Coveragementioning

confidence: 99%

“…Herein [34] and [40], satellite constellation numbers are examined over a range of the altitudes for various discontinuous coverages. The objective function is proposed for the maximum revisit time of the constellation.…”

Section: Ga For Discontinuous Global Coveragementioning

confidence: 99%

“…Recently, genetic algorithms have been proposed to achieve the optimal discontinuous coverage in satellite constellation geometry analysis [32], [33] and [34]. Multiobjective genetic algorithms for Walker satellite constellations and for zonal coverage ai'ea have also been introduced in [39] and [40].…”

Section: Introductionmentioning

confidence: 99%

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Satellite constellation design and radio resource management using genetic algorithm

2004

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Section: Ga For Discontinuous Global Coveragementioning

confidence: 99%

Section: Ga For Discontinuous Global Coveragementioning

confidence: 99%

See 1 more Smart Citation

Satellite constellation design and radio resource management using genetic algorithm

2004

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“…This approach demonstrates the performance of hybrid LEO/MEO, LEO/GEO, and MEO/GEO constellations. A study on Simulated Annealing and GA approaches to satellite constellation design for coverage of a limited latitude region was conducted by Crossley and Williams [13], where both methods outperformed the conventional Walker approach at low Earth central angles.…”

Section: Introductionmentioning

confidence: 99%

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

Savitri

Kim

et al. 2017

International Journal of Aerospace Engineering

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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.

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“…However, some missions requiring specific coverage constraints need to be resolved by means of optimization methods 8 . Evolutionary algorithms like simulated annealing 9 , Ant colony 10 and Genetic Algorithms (GA) 11 have been used to propose new satellite constellation geometries and to help for the optimization of existing constellation designs. Most of the applications have studied the global coverage general case.…”

Section: Introductionmentioning

confidence: 99%

Optimization of small satellite constellation design for continuous mutual regional coverage with multi-objective genetic algorithm

Meziane-Tani¹,

Métris²,

Lion³

et al. 2016

IJCIS

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This paper describes the application of an evolutionary optimization method to design satellite constellation for continuous regional coverage without intersatellite links. This configuration, called mutual coverage, is related to some technical limitations that exist on small satellite technology. The coverage of the north Algerian seismological network is taken as an example of application. A Multi Objective Genetic Algorithm (MOGA) is used to make a trade-off between the improvement of the coverage rate, the minimization of the total number of satellites and the reduction of the satellites' altitude. First, some experiments have been performed to find the weight distribution of the fitness function that shows the most significant improvement of the average fitness function. Then, some optimized constellation designs are given for different ranges of altitude and it is shown that the size of the MOGA constellation design is significantly reduced compared to the traditional geometrical design.

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Simulated Annealing and Genetic Algorithm Approaches for Discontinuous Coverage Satellite Constellation Design

Cited by 38 publications

References 2 publications

Satellite constellation design and radio resource management using genetic algorithm

Satellite constellation design and radio resource management using genetic algorithm

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

Optimization of small satellite constellation design for continuous mutual regional coverage with multi-objective genetic algorithm

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