Lining Xing

Evolutionary Algorithms for Many-Objective Ground Station Scheduling Problem

The task planning of satellite-ground time synchronization (SGTSTP) is a complex many-objective ground station scheduling problem. In this paper, we first provide a mathematical formulation of SGTSTP. To solve this problem, we propose a decomposition-and-integration (DI) based method. In DI method, the plan horizon is evenly divided into many disjoint plan periods and all time windows are distributed to each period, based on which the task planning problem turns into a multi-period 0-1 programming problem. Then we embed DI method into evolutionary algorithm framework and propose DI based evolutionary many-objective algorithm (DI-EMOA). At last, the computational results show that the DI-EMOAs have obvious performance promotion compared with heuristic algorithm.

Bio-inspired Algorithms Applied in Multi-objective Vehicle Routing Problem: Frameworks and Applications

Multi-objective vehicle routing problem (MOVRP) is developed from vehicle routing problem (VRP). MOVRP is a classic multi-objective optimization problem. During the recent years, the MOVRPs had a progress in problem scales and complex level. As a result, to get better solutions of MOVRPs, Bio-inspired algorithms were introduced into this area. This article first analyses the MOVRP framework, and then reviews the bio-inspired algorithm frameworks that designed to solve MOVRPs. This analysis leads to the identification of bio-inspired algorithms which can get better solutions for MOVPRs and can be applied to real-life cases successfully.

A Hybrid Dynamic Population Genetic Algorithm for Multi-satellite and Multi-station Mission Planning System

Satellite is an important space platform today. Achieving reasonable satellite management control greatly affects the development of the aerospace field. Multi-satellite and multi-station mission planning system was proposed to achieve control of satellite resources. In the system, planning algorithms are particularly important, so we proposed a mathematical model for the mission planning of multi-satellite and multi-ground station. Then, we proposed a hybrid dynamic population genetic algorithm (HDPGA) for satellite mission planning. In this algorithm, the large-population size is used for global optimization and the small-population size is used for local improvements. Additionally, the mission planning algorithm (MPA) is used to arrange the mission sequence on the ground station time window. We designed multiple sets of experiments to verify the effect of HDPGA. The results show that our proposed algorithm can meet the needs of the planning system. At the same time, HDPGA is better than the other four algorithms.

CNP Based Satellite Constellation Online Coordination Under Communication Constraints

This paper focuses on online managing Earth observation satellite constellation under dynamic environment, like detecting, observing, and tracking forest fires or volcanic eruptions without ground interval. In reality, the inter-satellite communication is limited by practical reasons. The objective is to maximize the total profit of the satellite constellation by increasing the efficiency of onboard resources and coordinating the different satellite to provide a greater level of responsiveness and adaptability, subject to communication time window constraints and observation time window constraints. Firstly, the online scheduling algorithm for a single satellite is proposed on basis of revision techniques and progressive techniques. Then, we propose the novel online coordination mechanism and the core algorithm based on Contract Net Protocol.

An improved ant colony algorithm for the dual time windows constraining Job Shop Scheduling Problem

In this paper, we carry out research on a new Job Shop Scheduling problem in production scheduling applications, namely the Dual Time Windows Constraining Job Shop Scheduling problem (DTWJSP), which involves job machining time window constraints and equipment working time window constrains. We regard maximizing the job completion rate as the scheduling objective of DTWJSP and establish a dual time windows constraining job shop scheduling mathematical model, then make detail analysis on scheduling objective and solving complexity. Based on the characteristic and complexity of problem we propose an improved ant colony algorithm, in which we add new solutions using heuristic and stochastic methods except a conventional construction approach in each generation solutions produced by ant colony algorithm. Besides, a neighborhood search method is used to obtain local optimal solution and bi-directional convergence is used in the pheromone update step, this combination of methods effectively avoids local optima and improves the search efficiency. We compare the improved algorithm with classical ant colony algorithm using a variety of instances, the experimental results demonstrated that the improvement is effective and the improved algorithm is feasible and efficient.

Recent Advances in Evolutionary Programming

In this paper, we provide an overview of some recent advances in evolutionary programming. We mainly discuss the principle and technical method of design for classical evolutionary programming and improving evolutionary programming (IEP). IEP has included many types of improving methods to solve realistic problems: fast evolutionary programming, self-adaptive Cauchy evolutionary programming, mixed mutation strategy in evolutionary programming, parallel evolutionary programming, Quality of Transmission (QoT) aware evolutionary programming algorithm, shifting classical evolutionary programming, and surrogate-assisted evolutionary programming. The above methods and some issues related to the future development of evolutionary programming are discussed in this paper.

Auto mission planning system design for imaging satellites and its applications in environmental field

Abstract Satellite hardware has reached a level of development that enables imaging satellites to realize applications in the area of meteorology and environmental monitoring. As the requirements in terms of feasibility and the actual profit achieved by satellite applications increase, we need to comprehensively consider the actual status, constraints, unpredictable information, and complicated requirements. The management of this complex information and the allocation of satellite resources to realize image acquisition have become essential for enhancing the efficiency of satellite instrumentation. In view of this, we designed a satellite auto mission planning system, which includes two sub-systems: the imaging satellite itself and the ground base, and these systems would then collaborate to process complicated missions: the satellite mainly focuses on mission planning and functions according to actual parameters, whereas the ground base provides auxiliary information, management, and control. Based on the requirements analysis, we have devised the application scenarios, main module, and key techniques. Comparison of the simulation results of the system, confirmed the feasibility and optimization efficiency of the system framework, which also stimulates new thinking for the method of monitoring environment and design of mission planning systems.

Cloud Avoidance Scheduling Algorithm for Agile Optical Satellites

The existence of cloud seriously influences the imaging quality and efficiency of traditional optical satellites, which can be overcome thanks to the enhancement of the mobility of the new generation of agile satellites. The problem of agile optical satellite scheduling considering real-time cloud information is therefore investigated. A two-phased scheduling framework, containing off-line scheduling on the ground and on-line rescheduling onboard, is proposed. An algorithm based on the ant colony algorithm is designed to solve this problem. An on-line re-scheduling algorithm based on confliction sliding strategy is proposed. A series of experiments are carried out to testify the effectiveness of the algorithm. The results show that almost 50 % of the satellite observation capacity is saved with consideration of real-time cloud information.