Multilevel Refinement for Combinatorial Optimisation Problems

被引:4
|
作者
Chris Walshaw
机构
[1] University of Greenwich,Computing and Mathematical Sciences
[2] Old Royal Naval College,undefined
来源
关键词
multilevel refinement; combinatorial optimisation; metaheuristic; graph partitioning; travelling salesman; graph colouring;
D O I
暂无
中图分类号
学科分类号
摘要
We consider the multilevel paradigm and its potential to aid the solution of combinatorial optimisation problems. The multilevel paradigm is a simple one, which involves recursive coarsening to create a hierarchy of approximations to the original problem. An initial solution is found (sometimes for the original problem, sometimes the coarsest) and then iteratively refined at each level. As a general solution strategy, the multilevel paradigm has been in use for many years and has been applied to many problem areas (most notably in the form of multigrid techniques). However, with the exception of the graph partitioning problem, multilevel techniques have not been widely applied to combinatorial optimisation problems. In this paper we address the issue of multilevel refinement for such problems and, with the aid of examples and results in graph partitioning, graph colouring and the travelling salesman problem, make a case for its use as a metaheuristic. The results provide compelling evidence that, although the multilevel framework cannot be considered as a panacea for combinatorial problems, it can provide an extremely useful addition to the combinatorial optimisation toolkit. We also give a possible explanation for the underlying process and extract some generic guidelines for its future use on other combinatorial problems.
引用
收藏
页码:325 / 372
页数:47
相关论文
共 50 条
  • [1] Multilevel refinement for combinatorial optimisation problems
    Walshaw, C
    [J]. ANNALS OF OPERATIONS RESEARCH, 2004, 131 (1-4) : 325 - 372
  • [2] A review on learning to solve combinatorial optimisation problems in manufacturing
    Zhang, Cong
    Wu, Yaoxin
    Ma, Yining
    Song, Wen
    Le, Zhang
    Cao, Zhiguang
    Zhang, Jie
    [J]. IET COLLABORATIVE INTELLIGENT MANUFACTURING, 2023, 5 (01)
  • [3] Schemata Bandits for Binary Encoded Combinatorial Optimisation Problems
    Drugan, Madalina M.
    Isasi, Pedro
    Manderick, Bernard
    [J]. SIMULATED EVOLUTION AND LEARNING (SEAL 2014), 2014, 8886 : 299 - 310
  • [4] A Global Parametric Programming Optimisation Strategy for Multilevel Problems
    Faisca, N. P.
    Dua, V.
    Saraiva, P. M.
    Rustem, B.
    Pistikopoulos, E. N.
    [J]. 16TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING AND 9TH INTERNATIONAL SYMPOSIUM ON PROCESS SYSTEMS ENGINEERING, 2006, 21 : 215 - 220
  • [5] A distributed evolutionary simulated annealing algorithm for combinatorial optimisation problems
    Aydin, ME
    Fogarty, TC
    [J]. JOURNAL OF HEURISTICS, 2004, 10 (03) : 269 - 292
  • [6] A Distributed Evolutionary Simulated Annealing Algorithm for Combinatorial Optimisation Problems
    M. Emin Aydin
    Terence C. Fogarty
    [J]. Journal of Heuristics, 2004, 10 : 269 - 292
  • [7] Dynamic combinatorial optimisation problems: an analysis of the subset sum problem
    Rohlfshagen, Philipp
    Yao, Xin
    [J]. SOFT COMPUTING, 2011, 15 (09) : 1723 - 1734
  • [8] Dynamic combinatorial optimisation problems: an analysis of the subset sum problem
    Philipp Rohlfshagen
    Xin Yao
    [J]. Soft Computing, 2011, 15 : 1723 - 1734
  • [9] Hyper-heuristic local search for combinatorial optimisation problems
    Turky, Ayad
    Sabar, Nasser R.
    Dunstall, Simon
    Song, Andy
    [J]. KNOWLEDGE-BASED SYSTEMS, 2020, 205
  • [10] A combinatorial particle swarm optimisation for solving permutation flowshop problems
    Jarboui, Bassem
    Ibrahim, Saber
    Siarry, Patrick
    Rebai, Abdelwaheb
    [J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2008, 54 (03) : 526 - 538