Non-GEO Satellite Constellation Design with Satellite Diversity Using Genetic Algorithm

Novel strategies for automatic satellite constellation design with satellite diversity and radio resource management are proposed. The automatic satellite constellation design means that some parameters of satellite constellation design can be determined simultaneously. The total number of satellites, the altitude of a satellite, the angle between planes, the angle shift between satellites and the inclination angle are considered in the design. Satellite constellation design is modelled using a multiobjective genetic algorithm. This method is applied to low Earth orbit (LEO), medium Earth orbit (MEO) and hybrid constellations. The use of a genetic algorithm allows automatic satellite constellation design while achieving dual satellite diversity statistics. Furthermore, a strategy for dynamic channel allocation is proposed that uses a genetic algorithm for use in mobile satellite systems (MSS) networks. The main idea behind this algorithm is to use the minimum cost as a metric to provide optimum channel solutions for specified interference constraints. The simulation is designed for a MEO satellite constellation. Using this algorithm, the proposed model outperforms conventional dynamic channel assignment (DCA) schemes in terms of call blocking and call dropping probability. Generally, genetic algorithms are robust to dynamic variations in satellite constellation design and provide resource allocation improvements in DCA in MSS networks.

As a fundamental and significant stage of satellite networks which play an important role in global communications, a design of satellite constellations with inter-satellite links(ISLs) becomes necessary. An optimization method is proposed to determine both satellite elements and constellation elements using the multi-objective genetic algorithm(GA) in this paper. The objectives to be optimized, which are determined by the elements to be designed, are divided into performance objectives and cost objectives. The former consist of the coverage rate, the ISL range and the minimum elevation while the latter contain the total satellite count, the orbital altitude and the orbital inclination. With the aim of maximizing the performance minimizing the cost, the proposed method tries to achieve a balance among multi objectives and design specific satellite constellations considered in different conditions. Then the constellations designed in different schemes are compared with each other to demonstrate the correctness and effectiveness of the method.

The way to solve the problem of satellite constellation design was outlined in the 1960s, recognizing the importance of satellite coverage (continuous or periodic) function and allowing interpretation of the operation of different types of space systems. Due to the fact that Earth periodic coverage optimization is extremely complex, for many years, the solutions of this problem have been searched for among a priori fixed constellation types successfully implemented before for continuous coverage, with continuous coverage seeming to be much easier than periodic coverage. In this study, it is shown that the technological advance in satellite constellation design for periodic coverage could be achieved by considering it as a unique and separate problem. The introduction in the route theory for satellite constellation design for Earth periodic coverage that aims at creating methods for optimization of arbitrary constellations, which is an alternative to the traditional approach that considers narrow classes of constellations to be analyzed, is described. The so-called route constellation is presented as a mathematical abstraction for approximation of arbitrary satellite constellation. The theory elements of the optimization procedure in the infinite domain of route constellations are introduced. Previously unknown regularities in Earth periodic coverage and in localization of optimal low-Earth-orbit satellite constellation parameters are presented and illustrated.

As a huge space system, satellite constellation is developing rapidly. Satellite constellation design is a basic problem in the design of multi spacecraft space system. It is the premise of building constellation of earth observation, satellite navigation, satellite communication and various scientific exploration satellites. According to the characteristics of modern satellite constellation design, this paper investigates the design and application of particle swarm optimization algorithm in satellite constellation, and the latest research progress of various research institutions in satellite constellation maintenance and control, autonomous navigation and space real-time monitoring, and discusses the latest strategies and technical methods of satellite constellation operation and management.KeywordsSatellite constellationParticle Swarm OptimizationLocalizationMappingPosition precision

The wireless satellite communication system is considered to be the most promising new communication method to achieve the coverage of heaven, earth and sea. Satellite constellation design is the most important stage before the actual deployment of satellites. It will determine the number of satellites and orbit related parameters. We aim to study the constellation design of 5G low-orbit satellites. At present, some advanced research results have been proposed, providing important theory and experience for in-depth exploration and improvement of satellite constellation structure. We proposed a method based on the multi-objective particle swarm optimization (PSO) algorithm for designing low-orbit 5G satellite constellation. The method takes the number of satellites and orbit related parameters as the limit, and take the coverage of a specific area as the objective function. The results showed that the PSO algorithm can meet the full-time and full-area coverage low-orbit 5G constellation design for a specific area, and verified the effectiveness of the PSO algorithm for the coverage low-orbit 5G constellation design for the target area. We provided satellite constellation coverage guarantee for link-level simulation of 5g LEO satellite communication system in the next step.

BackgroundProtein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task.ResultsIn order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods.ConclusionsThe hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively.

In recent years, the low-orbit satellites constellation has developed rapidly at home and abroad, involving navigation, remote sensing, communications, the internet and other fields. The performance of constellations will be influenced by multi-fold coverage heavily. For instance, remote sensing satellite constellations can improve the resolution of ground imaging through double coverage that two satellites can take ground photos get together at adjacent positions. There are more scholars studying continuous global coverage constellation, but less two-fold coverage or more. This article proposes the two-fold coverage width based on this hot demand about remote sensing satellite constellations, and proposes a constellation optimization design process. Then initial constellation parameters will be obtained from the single continue global coverage satellite constellation designed by the SOC method. The constellation will be optimized by the method proposed multiple iterations in this paper. Finally, the optimized constellations satisfied the function of two-fold coverage width and has better performance of the digital transmission opportunities to ground. The designed constellation satisfies the demand of two-fold coverage and has certain reference significance for engineering practice.

Satellite constellation designers take into consideration the entire telecommunication network and their design choices are influenced by many factors including: number of satellites, orbital characteristics, coverage area, network interconnections, system cost, and complexity. When the satellite constellation design method does not include a large number of possible configurations, then the final result of the design process is a suboptimal solution. Flower Constellation (FC) design provides a relatively new design approach which can overcome this problem. The time evolution of the FC theory is here summarized and the fundamental mathematics allowing the constellation design is provided. In particular, the theory is applied to the design and optimization of constellations maximizing the global coverage and the network connectivity via intersatellite links. Performance results are compared to the classical type of satellite constellations, i.e., the Walker Constellations. The performance improvement provided by FC design with respect to Walker Constellation design is shown. Finally, considerations regarding the cost for deployment and orbital control are also provided.

With the development of science and technology, there has been growing the grid size, the growing demand for electricity and the extent of increasing the electricity market.In this paper, a mathematical morphology and genetic algorithm based dynamic reactive power optimization method for distribution network is proposed.By means of constructing mathematical morphology filter, the binary image composed by allelic genes of chromosome is filtered and the problem that deals with the action times of voltage regulating devices for compensation is turned into filtering problem of discrete binary image.With further automate scheduling, distribution automation and unmanned substation reactive power optimization of distribution line running made an urgent request.Reactive power optimization can improve the voltage passing rate, increasing the reliability of the system, the power quality, and the system runs the security and economy of the perfect combination, very broad application prospects.

For the long time lag and nonlinear double-tank experimental system, research has been carried on by PID control strategy with more setting parameters of genetic algorithm. During the process of tank level control, a kind of adaptive setting method combined with genetic algorithm crossover probability and mutation probability is applied. By using the method to optimize parameters of the PID controller and writing simulation control program in the MATALB language environment to do the simulation study with the double-tank. The results of simulation and application shows that: the method can be used to realize PID parameters of global optimization and compared with conventional PID control, the tank level control with GA genetic and PID algorithm works with faster response and faster to achieve stability, which improve the performance of a control system.

Frequent and reliable observation of ground targets is critical for an Earth observation satellite constellation. Limited satellite numbers often result in long revisit intervals. This study aims to minimize the worst-case revisit time in satellite constellation design. The paper proposes an integer linear programming (ILP)-based framework under common ground track constraints. The framework optimizes seed orbital elements and discretizes possible satellite phases for inclusion in the ILP. In a low-Earth orbit case study, the approach reduces the worst-case revisit time by over 60% compared to a symmetric constellation. The ILP-based method provides a systematic, computationally efficient solution for minimizing worst-case revisit time in constellation pattern design. Future work can extend the framework to account for multiple ground targets and incorporate additional measures of effectiveness.

Optimization and design of an aircraft’s morphing wing-tip demonstrator for drag reduction at low speed, Part I – Aerodynamic optimization using genetic, bee colony and gradient descent algorithms

In order to study the prediction and optimization of borehole stability parameters in deepwater drilling based on genetic algorithm. First, a genetic hybrid algorithm based on pattern search is proposed. Then, based on the adaptive genetic algorithm, the evolutionary population is searched for patterns, which makes the hybrid algorithm not only has a strong global search ability but also improves the local optimization accuracy. Finally, the unit footage cost in the drilling process is taken as the objective function, and the algorithm is verified by taking the drilling in Karamay area as an example. The calculation results show that if the bit wear reaches 0.8-0.9 and then the bit is pulled out, the utilization rate of the bit can be increased, the design efficiency and accuracy can be improved, and the drilling cost can be reduced. The wear amount of the optimized bit is higher than that of the actual bit. Increasing the utilization rate of the bit can reduce the cost of drilling meters to a certain extent and improve the economic benefits of drilling. The objective function and constraint conditions for the optimization of drilling parameters are determined, and the algorithm is verified with the drilling data of Karamay Oilfield. The results show that the algorithm improves the stability and speed of iterative convergence and improves the reliability of data analysis results. Based on the regional three-dimensional formation rock parameter data volume, the optimization method can be used to optimize the drilling parameters before drilling and provide a basis for formulating the drilling design scheme.

The article develops an effective method for solving optimization problems based on the synthesis of evolution ary mechanisms of nature’s development, which are based on the principles of genetic search for the best solu tions. It analyzes evolutionary concepts of biological species development by such well-known scientists as Ch. Darwin, J. Lamarck, Hugo de Vries, K. Popper, and M. Kimura. The key mechanisms for the emergence of new individuals with better adaptation (the best solutions) are identified. Based on the known relevant concepts, the main provisions of the theory of evolution are developed. Corresponding models are constructed, which in turn become computational analogies for the development of evolutionary methods for solving optimization prob lems. An optimization method has been developed based on a genetic algorithm, which is based on the basic operators of evolution: reproduction (selection), crossover, and mutation. A distinctive feature of the proposed approach is the hybridization of the classical genetic algorithm with adaptive mechanisms for parameter tuning and local improvement of solutions. The genetic algorithm used reproduction operators (tournament and roulette wheel selection), single- and double-point crossover, and mutation. This allows for an increase in the efficiency of global search in terms of convergence (number of computational iterations) and solution accuracy (average absolute error of the solution at 100 runs), as well as avoiding the stopping of the computational process at local extrema. Based on the developed optimization method, a genetic algorithm has been created that incorporates all the mechanisms of evolutionary computation. A genetic algorithm has been developed that contains all the mech anisms of evolutionary computation. Based on the genetic algorithm, model problems of multi-criteria optimi sation were calculated in Python in binary coding of the optimal solution (OneMax, LeadingOnes) and in coding of the solution using real numbers (two-extreme function). In the corresponding test problems, the stable achieve ment of the global extremum of the objective function and the stability of the algorithm were recorded. This allows us to conclude that the proposed method of optimizing multi-criteria functions based on genetic algo rithms is effective.

Aiming at the disadvantage of premature convergence of basic genetic algorithm, an adaptive simulated annealing genetic tabu search algorithm is proposed. This algorithm fully combines the global convergence and adaptability of simulated annealing algorithm and the strong climbing ability and high efficiency of tabu search strategy. It has strong convergence and adaptability. The simulation results of the adaptive simulated annealing genetic tabu search algorithm are given and compared with the basic genetic algorithm and simulated annealing algorithm. The simulation results show that the algorithm has better convergence and optimization performance, and can better solve the combinatorial optimization problem.