Data-Driven Collaborative Scheduling Method for Multi-Satellite Data-Transmission

With continuous expansion of satellite applications,the requirements for satellite communication services,such as communication delay,transmission bandwidth,transmission power consumption,and communication coverage,are becoming higher.This paper first presents an overview of the current development status of Low Earth Orbit(LEO)satellite constellations,and then conducts a demand analysis for multi-satellite data transmission based on LEO satellite constellations.The problem is described,and the challenges and difficulties of the problem are analyzed accordingly.On this basis,a multi-satellite data-transmission mathematical model is then constructed.Combining classical heuristic allocating strategies on the features of the proposed model,with the reinforcement learning algorithm Deep Q-Network(DQN),a two-stage optimization framework based on heuristic and DQN is proposed.Finally,by taking into account the spatial and temporal distribution characteristics of satellite and facility resources,a multi-satellite scheduling instance dataset is generated.Experimental results validate the rationality and correctness of the DQN algorithm in solving the collaborative scheduling problem of multi-satellite data transmission.

A Two-Stage Approach for Electric Vehicle Routing Problem with Time Windows and Heterogeneous Recharging Stations

An Electric Vehicle(EV)is an appropriate substitution for traditional transportation means for diminishing greenhouse gas emissions.However,decision-makers are beset by the limited driving range caused by the low battery capacity and the long recharging time.To resolve the former issue,several transportation companies increases the travel distance of the EV by establishing recharging stations in various locations.The proposed Electric Vehicle-Routing Problem with Time Windows(E-VRPTW)and recharging stations are constructed in this context;it augments the VRPTW by reinforcing battery capacity constraints.Meanwhile,super-recharging stations are gradually emerging in the surroundings.They can decrease the recharging time for an EV but consume more energy than regular stations.In this paper,we first extend the E-VRPRTW by adding the elements of super-recharging stations.We then apply a two-stage heuristic algorithm driven by a dynamic programming process to solve the new proposed problem to minimize the travel and total recharging costs.Subsequently,we compare the experimental results of this approach with other algorithms on several sets of benchmark instances.Furthermore,we analyze the impact of super-recharging stations on the total cost of the logistic plan.

Data-Driven Heuristic Assisted Memetic Algorithm for Efficient Inter-Satellite Link Scheduling in the BeiDou Navigation Satellite System

Inter-satellite link(ISL)scheduling is required by the BeiDou Navigation Satellite System(BDS)to guarantee the system ranging and communication performance.In the BDS,a great number of ISL scheduling instances must be addressed every day,which will certainly spend a lot of time via normal metaheuristics and hardly meet the quick-response requirements that often occur in real-world applications.To address the dual requirements of normal and quick-response ISL schedulings,a data-driven heuristic assisted memetic algorithm(DHMA)is proposed in this paper,which includes a high-performance memetic algorithm(MA)and a data-driven heuristic.In normal situations,the high-performance MA that hybridizes parallelism,competition,and evolution strategies is performed for high-quality ISL scheduling solutions over time.When in quick-response situations,the data-driven heuristic is performed to quickly schedule high-probability ISLs according to a prediction model,which is trained from the high-quality MA solutions.The main idea of the DHMA is to address normal and quick-response schedulings separately,while high-quality normal scheduling data are trained for quick-response use.In addition,this paper also presents an easy-to-understand ISL scheduling model and its NP-completeness.A seven-day experimental study with 10080 one-minute ISL scheduling instances shows the efficient performance of the DHMA in addressing the ISL scheduling in normal(in 84 hours)and quick-response(in 0.62 hour)situations,which can well meet the dual scheduling requirements in real-world BDS applications.

Multi-Objective Rule System Based Control Model with Tunable Parameters for Swarm Robotic Control in Confined Environment

Enhancing the adaptability of Unmanned Aerial Vehicle(UAV)swarm control models to cope with different complex working scenarios is an important issue in this research field.To achieve this goal,control model with tunable parameters is a widely adopted approach.In this article,an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking(MO O-Flocking)is proposed.The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters.To achieve multi-objective parameter tuning,a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2(ISPEA2)is designed.Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives.Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.

General modeling and optimization technique for real-world earth observation satellite scheduling

Over the last two decades,many modeling and optimization techniques have been developed for earth observation satellite(EOS)scheduling problems,but few of them show good generality to be engineered in realworld applications.This study proposes a general modeling and optimization technique for common and real-world EOS scheduling cases;it includes a decoupled framework,a general modeling method,and an easy-to-use algorithm library.In this technique,a framework that decouples the modeling,constraints,and optimization of EOS scheduling problems is built.With this framework,the EOS scheduling problems are appropriately modeled in a general manner,where the executable opportunity,another format of the well-known visible time window per EOS operation,is viewed as a selectable resource to be optimized.On this basis,10 types of optimization algorithms,such as Tabu search and genetic algorithm,and a parallel competitive memetic algorithm,are developed.For simplified EOS scheduling problems,the proposed technique shows better performance in applicability and effectiveness than the state-of-the-art algorithms.In addition,a complicatedly constrained real-world benchmark exampled by a four-EOS Chinese commercial constellation is provided,and the technique is qualified and outperforms the in-use scheduling system by more than 50%.

Satellite scheduling engine: The intelligent solver for future multi-satellite management

With the rapid development and popularization of worldwide aerospace industries over the recent decades,the optimization requirements of multi-satellite management have exploded significantly.The latest data show 4852 operational satellites orbiting the earth,of which the US,China,and Russia own 2944,499,and 169,respectively.Therefore,how to manage and schedule effectively hundreds of satellites to conform to the developing and popularizing aerospace tendency emerges as a worldwide problem.

Multicommodity Flow Modeling for the Data Transmission Scheduling Problem in Navigation Satellite Systems

Introducing InterSatellite Links(ISLs)is a major trend in new-generation Global Navigation Satellite Systems(GNSSs).Data transmission scheduling is a crucial problem in the study of ISL management.The existing research on intersatellite data transmission has not considered the capacities of ISL bandwidth.Thus,the current study is the first to describe the intersatellite data transmission scheduling problem with capacity restrictions in GNSSs.A model conversion strategy is designed to model the aforementioned problem as a length-bounded single-path multicommodity flow problem.An integer programming model is constructed to minimize the maximal sum of flows on each intersatellite edge;this minimization is equivalent to minimizing the maximal occupied ISL bandwidth.An iterated tree search algorithm is proposed to resolve the problem,and two ranking rules are designed to guide the search.Experiments based on the BeiDou satellite constellation are designed,and results demonstrate the effectiveness of the proposed model and algorithm.

Quantum-Inspired Distributed Memetic Algorithm

This paper proposed a novel distributed memetic evolutionary model,where four modules distributed exploration,intensified exploitation,knowledge transfer,and evolutionary restart are coevolved to maximize their strengths and achieve superior global optimality.Distributed exploration evolves three independent populations by heterogenous operators.Intensified exploitation evolves an external elite archive in parallel with exploration to balance global and local searches.Knowledge transfer is based on a point-ring communication topology to share successful experiences among distinct search agents.Evolutionary restart adopts an adaptive perturbation strategy to control search diversity reasonably.Quantum computation is a newly emerging technique,which has powerful computing power and parallelized ability.Therefore,this paper further fuses quantum mechanisms into the proposed evolutionary model to build a new evolutionary algorithm,referred to as quantum-inspired distributed memetic algorithm(QDMA).In QDMA,individuals are represented by the quantum characteristics and evolved by the quantum-inspired evolutionary optimizers in the quantum hyperspace.The QDMA integrates the superiorities of distributed,memetic,and quantum evolution.Computational experiments are carried out to evaluate the superior performance of QDMA.The results demonstrate the effectiveness of special designs and show that QDMA has greater superiority compared to the compared state-of-the-art algorithms based on Wilcoxon’s rank-sum test.The superiority is attributed not only to good cooperative coevolution of distributed memetic evolutionary model,but also to superior designs of each special component.