• DocumentCode
    183864
  • Title

    Paracontracting Multiagent Coordination Optimization: Convergence and parallelization

  • Author

    Qing Hui ; Haopeng Zhang

  • Author_Institution
    Dept. of Mech. Eng., Texas Tech Univ., Lubbock, TX, USA
  • fYear
    2014
  • fDate
    4-6 June 2014
  • Firstpage
    1698
  • Lastpage
    1703
  • Abstract
    In this paper, a contraction mapping based Multiagent Coordination Optimization (MCO) algorithm, called Paracontracting Multiagent Coordination Optimization (PMCO), is proposed and implemented in a parallel computing way by embedding MATLAB built-in function parfor into PMCO. Then we give a global convergence result of PMCO by means of paracontraction and semistability theory. The basic idea here is to convert the proposed derivative-free iterative algorithm for PMCO into a discrete-time switched linear system and then discuss its semistability property. Based on some matrix analysis results and semistability theory, we present two sufficient conditions for guaranteeing the global convergence of PMCO under different circumstances. Finally, numerical evaluation of the parallel PMCO algorithm is provided in the paper by running the proposed algorithm on multicore supercomputers.
  • Keywords
    convergence; discrete time systems; linear systems; matrix algebra; multi-agent systems; optimisation; parallel algorithms; stability; time-varying systems; MATLAB; contraction mapping; derivative-free iterative algorithm; discrete-time switched linear system; global convergence; matrix analysis; paracontracting multiagent coordination optimization; parallel PMCO algorithm; parallel computing; parallelization; parfor function; semistability property; semistability theory; sufficient conditions; Algorithm design and analysis; Convergence; Hypercubes; MATLAB; Optimization; Particle swarm optimization; Vectors; Agents-based systems; Evolutionary computing; Optimization algorithms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference (ACC), 2014
  • Conference_Location
    Portland, OR
  • ISSN
    0743-1619
  • Print_ISBN
    978-1-4799-3272-6
  • Type

    conf

  • DOI
    10.1109/ACC.2014.6858818
  • Filename
    6858818