Structural advantages for ant colony optimisation inherent in permutation scheduling problems

被引:0
|
作者
Montgomery, J [1 ]
Randall, M
Hendtlass, T
机构
[1] Bond Univ, Fac Informat Technol, Southport, Qld 4229, Australia
[2] Swinburne Univ Technol, Sch Informat Technol, Hawthorn, Vic 3122, Australia
关键词
heuristic search; planning and scheduling;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
When using a constructive search algorithm, solutions to scheduling problems such as the job shop and open shop scheduling problems are typically represented as permutations of the operations to be scheduled. The combination of this representation and the use of a constructive algorithm introduces a bias typically favouring good solutions. When ant colony optimisation is applied to these problems, a number of alternative pheromone representations are available, each of which interacts with this underlying bias in different ways. This paper explores both the structural aspects of the problem that introduce this underlying bias and the ways two pheromone representations may either lead towards poorer or better solutions over time. Thus it is a synthesis of a number of recent studies in this area that deal with each of these aspects independently.
引用
收藏
页码:218 / 228
页数:11
相关论文
共 50 条
  • [1] On solving permutation scheduling problems with ant colony optimization
    Merkle, D
    Middendorf, M
    [J]. INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2005, 36 (05) : 255 - 266
  • [2] An Ant Colony Optimisation Inspired Crossover Operator for Permutation Type Problems
    Chitty, Darren M.
    [J]. 2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021), 2021, : 57 - 64
  • [3] A new approach to solve permutation scheduling problems with Ant Colony Optimization
    Merkle, D
    Middendorf, M
    [J]. APPLICATIONS OF EVOLUTIONARY COMPUTING, PROCEEDINGS, 2001, 2037 : 484 - 494
  • [4] A Greedy Approach to Ant Colony Optimisation Inspired Mutation for Permutation Type Problems
    Chitty, Darren M.
    [J]. 2021 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2021), 2021,
  • [5] Solution representation for job shop scheduling problems in ant colony optimisation
    Montgomery, James
    Fayad, Carole
    Petrovic, Sanja
    [J]. ANT COLONY OPTIMIZATION AND SWARM INTELLIGENCE, PROCEEDINGS, 2006, 4150 : 484 - 491
  • [6] Performance of an ant colony optimisation algorithm in dynamic job shop scheduling problems
    Zhou, R.
    Nee, A. Y. C.
    Lee, H. P.
    [J]. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2009, 47 (11) : 2903 - 2920
  • [7] Ant colony optimisation for task matching and scheduling
    Chiang, C-W.
    Lee, Y-C.
    Lee, C-N.
    Chou, T-Y.
    [J]. IEE PROCEEDINGS-COMPUTERS AND DIGITAL TECHNIQUES, 2006, 153 (06): : 373 - 380
  • [8] Ant colony optimisation for machine layout problems
    Corry, P
    Kozan, E
    [J]. COMPUTATIONAL OPTIMIZATION AND APPLICATIONS, 2004, 28 (03) : 287 - 310
  • [9] Ant Colony Optimisation for Machine Layout Problems
    Paul Corry
    Erhan Kozan
    [J]. Computational Optimization and Applications, 2004, 28 : 287 - 310
  • [10] An ant colony optimisation algorithm for scheduling in agile manufacturing
    Liao, C. -J.
    Liao, C. -C.
    [J]. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2008, 46 (07) : 1813 - 1824