Improved Genetic Algorithm with Local Search for Satellite Range Scheduling System and its Application in Environmental monitoring

被引:18
|
作者
Song, Yan-Jie [1 ]
Zhang, Zhong-Shan [1 ]
Song, Bing-Yu [1 ]
Chen, Ying-Wu [1 ]
机构
[1] Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Hunan, Peoples R China
基金
中国国家自然科学基金;
关键词
Improved Genetic Algorithm; Local Search; Satellite Range Scheduling Problem; Heuristic; Optimization; Environmental monitoring;
D O I
10.1016/j.suscom.2018.11.009
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Satellites play an important role in such areas as environmental monitoring and disaster prediction that are related to human survival and development. Satellite monitoring and control is the key to the satellite's management and control of the ground to ensure its smooth implementation. Because of the imbalance of demand and available resources, satellite range scheduling has become particularly important. This paper analyzes the satellite range scheduling problem and sets up mathematical models and constraints. Afterwards, this paper proposes an efficient algorithm that combines improved genetic algorithm and local search method. The improved genetic algorithm is used to rapidly improve the quality of the planning scheme, and the neighborhood search is used for the subsequent small-scale optimization. In order to improve the speed of search, our algorithm uses a reorganization operation and a mutation operation adjusted with the number of iterations. In order to test the effectiveness of the algorithm, we conducted experimental verifications of the calculations of various types of satellites at different mission scales and compared them with other algorithms. (C) 2018 Elsevier Inc. All rights reserved.
引用
收藏
页码:19 / 27
页数:9
相关论文
共 50 条
  • [1] Multi-objective genetic local search algorithm and its application to flowshop scheduling
    Ishibuchi, Hisao
    Murata, Tadahiko
    IEEE Transactions on Systems, Man & Cybernetics Part C: Applications and Reviews, 1998, 28 (03): : 392 - 403
  • [2] A multi-objective genetic local search algorithm and its application to flowshop scheduling
    Ishibuchi, H
    Murata, T
    IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS, 1998, 28 (03): : 392 - 403
  • [3] An Improved Adaptive Genetic Algorithm and Its Application in Intelligent Course Scheduling System
    Wang, Peiping
    Xu, Xiaoping
    Liu, Chuhong
    2019 6TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND CONTROL ENGINEERING (ICISCE 2019), 2019, : 121 - 125
  • [4] Genetic algorithm and its application in scheduling system
    Ni, Jian
    Yang, Ning-Ning
    Telkomnika - Indonesian Journal of Electrical Engineering, 2013, 11 (04): : 1934 - 1939
  • [5] An improved genetic algorithm with local search for order acceptance and scheduling problems
    Cheng, Chen
    Yang, Zhenyu
    Xing, Lining
    Tan, Yuejin
    PROCEEDINGS OF THE 2013 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN PRODUCTION AND LOGISTICS SYSTEMS (CIPLS), 2013, : 115 - 122
  • [6] An Improved Local Search Algorithm with Pruning for Satellite Data Transmission Scheduling Problem
    Zhao, Man
    He, Qianzhou
    Li, Shenglong
    Ren, Min
    2021 21ST INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY AND SECURITY COMPANION (QRS-C 2021), 2021, : 561 - 568
  • [7] An Improved Genetic Algorithm with Local Search for Dynamic Job Shop Scheduling Problem
    Wang, Ming
    Zhang, Peng
    Zheng, Peng
    He, Junjie
    Zhang, Jie
    Bao, Jinsong
    2020 IEEE 16TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE), 2020, : 766 - 771
  • [8] Local Search and Genetic Algorithms for Satellite Scheduling Problems
    Kolici, Vladi
    Herrero, Xavier
    Xhafa, Fatos
    Barolli, Leonard
    2013 EIGHTH INTERNATIONAL CONFERENCE ON BROADBAND, WIRELESS COMPUTING, COMMUNICATION AND APPLICATIONS (BWCCA 2013), 2013, : 328 - 335
  • [9] Design and Application of an Improved Genetic Algorithm to a Class Scheduling System
    Chen, Xiangliu
    Yue, Xiao-Guang
    Li, Rita Yi Man
    Zhumadillayeva, Ainur
    Liu, Ruru
    INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING, 2021, 16 (01): : 44 - 59
  • [10] Design and Application of an Improved Genetic Algorithm to a Class Scheduling System
    Chen X.
    Yue X.-G.
    Man Li R.Y.
    Zhumadillayeva A.
    Liu R.
    Liu, Ruru (lru255@sina.cn), 1600, Kassel University Press GmbH (16): : 44 - 59