• DocumentCode
    3161783
  • Title

    A memetic algorithm for uncertain Capacitated Arc Routing Problems

  • Author

    Juan Wang ; Ke Tang ; Xin Yao

  • Author_Institution
    Sch. of Comput. Sci. & Technol., Univ. of Sci. & Technol. of China, Hefei, China
  • fYear
    2013
  • fDate
    16-19 April 2013
  • Firstpage
    72
  • Lastpage
    79
  • Abstract
    The Capacitated Arc Routing Problem (CARP) is a widely investigated classic combinatorial optimization problem. Being a deterministic model, it is far away from the real world. A more practical problem model of CARP is the Uncertain CARP (UCARP), with the objective of finding a robust solution which performs well in all possible environments. There exist few algorithms for UCARP in previous work. In this paper, a Memetic Algorithm (MA) and its modified version in time consumption for UCARP are proposed. Experimental results on two benchmark test sets show that with an integrated fitness function and a large step-size local search operator, the new MAs show excellent ability to find robust solutions for UCARP. We also present a less time-consuming version of our MA which shows significant advantages in time consumption.
  • Keywords
    deterministic algorithms; genetic algorithms; network routing; MA; UCARP; benchmark test sets; classic combinatorial optimization problem; deterministic model; integrated fitness function; memetic algorithm; step-size local search operator; time consumption; uncertain CARP; uncertain capacitated arc routing problems; Maintenance engineering; Memetics; Optimization; Robustness; Sociology; Statistics; Vehicles; UCARP; evolutionary algorithm; robust optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Memetic Computing (MC), 2013 IEEE Workshop on
  • Conference_Location
    Singapore
  • Type

    conf

  • DOI
    10.1109/MC.2013.6608210
  • Filename
    6608210