Hybrid Filtered Beam Search Algorithm for the Optimization of Monitoring Patrols

被引:0
|
作者
Marwa Gam
Achraf Jabeur Telmoudi
Dimitri Lefebvre
机构
[1] University le Havre Normandie,
[2] Laboratoire d’Ingenierie des Systèmes Industriels et des Energies Renouvelables,undefined
[3] The National Higher Engineering School of Tunis (ENSIT),undefined
[4] University of Tunis,undefined
来源
关键词
Security; Monitoring patrol; Optimization; Tasks allocation; Trajectory planning; Automated guided vehicles;
D O I
暂无
中图分类号
学科分类号
摘要
This paper offers an operational and methodological response for managing industrial risks by improving the monitoring of industrial areas. The objective is the optimization of monitoring patrols with automated mobile agents that are responsible for the surveillance. Such agents are formed by automated guided vehicles or unmanned aerial vehicles that carry various sensors. Apart from the specificities of each class of agents, the proposed approach is motivated by the need to inspect sites that may be dangerous or difficult to access. The optimization of the missions is carried out in compliance with functional (e.g., precedence of the operations) and operational (e.g., the travel time reserve of the agents) constraints in the double perspective of patrol configuration and trajectory planning as far as these aspects are strongly correlated. The questions that should be answered are as follows. How many mobile agents are required to perform a given set of measurements? How many sensors and what types of sensors must each of these agents equip? How to define the mission and trajectory of each agent? Such questions are studied as a multi-robots / multi-tasks problem, and an approach based on the hybrid filtered beam search is proposed for that purpose.
引用
收藏
相关论文
共 50 条
  • [41] Binary optimization using hybrid particle swarm optimization and gravitational search algorithm
    Mirjalili, Seyedali
    Wang, Gai-Ge
    Coelho, Leandro dos S.
    NEURAL COMPUTING & APPLICATIONS, 2014, 25 (06): : 1423 - 1435
  • [42] Hybrid of firefly algorithm and pattern search for solving optimization problems
    Wahid, Fazli
    Ghazali, Rozaida
    EVOLUTIONARY INTELLIGENCE, 2019, 12 (01) : 1 - 10
  • [43] A hybrid optimization method for image classification with gravitational search algorithm
    Wang, Shengsheng
    Dickson, Bolou Bolou
    Wang, Weilie
    Liu, Dong
    Feng, Long
    Journal of Information and Computational Science, 2014, 11 (17): : 6393 - 6400
  • [44] Hybrid of firefly algorithm and pattern search for solving optimization problems
    Fazli Wahid
    Rozaida Ghazali
    Evolutionary Intelligence, 2019, 12 : 1 - 10
  • [45] A hybrid optimization technique coupling an evolutionary and a local search algorithm
    Kelner, Vincent
    Capitanescu, Florin
    Uonard, Olivier
    Wehenkel, Louis
    JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2008, 215 (02) : 448 - 456
  • [46] A Hybrid Harmony search and Simulated Annealing algorithm for continuous optimization
    Assad, Assif
    Deep, Kusum
    INFORMATION SCIENCES, 2018, 450 : 246 - 266
  • [47] Optimization of cardinality constrained portfolios with a hybrid local search algorithm
    Maringer, D
    Kellerer, H
    OR SPECTRUM, 2003, 25 (04) : 481 - 495
  • [48] Optimization of cardinality constrained portfolios with a hybrid local search algorithm
    Dietmar Maringer
    Hans Kellerer
    OR Spectrum, 2003, 25 : 481 - 495
  • [49] Hybrid Hierarchical Backtracking Search Optimization Algorithm and Its Application
    Feng Zou
    Debao Chen
    Renquan Lu
    Arabian Journal for Science and Engineering, 2018, 43 : 993 - 1014
  • [50] An effective hybrid cuckoo search algorithm for constrained global optimization
    Wen Long
    Ximing Liang
    Yafei Huang
    Yixiong Chen
    Neural Computing and Applications, 2014, 25 : 911 - 926