Ant Colony Optimization View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2006

AUTHORS

Krzysztof Socha , Christian Blum

ABSTRACT

Ant colony optimization (ACO) is a metaheuristic that was originally introduced for solving combinatorial optimization problems. In this chapter we present the general description of ACO, as well as its adaptation for the application to continuous optimization problems. We apply this adaptation of ACO to optimize the weights of feed-forward neural networks for the purpose of pattern classification. As test problems we choose three data sets from the well-known PROBEN1 medical database. The experimental results show that our algorithm is comparable to specialized algorithms for feed-forward neural network training. Furthermore, the results compare favourably to the results of other general-purpose methods such as genetic algorithms. More... »

PAGES

153-180

References to SciGraph publications

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  • 1997. Ants can colour graphs in JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
  • 2000-06. A Continuous Genetic Algorithm Designed for the Global Optimization of Multimodal Functions in JOURNAL OF HEURISTICS
  • 1985-01. Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm in JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
  • 2004-09. An Ant Colony Optimization Algorithm for Shop Scheduling Problems in JOURNAL OF MATHEMATICAL MODELLING AND ALGORITHMS
  • 2002-08. Comparing an ACO algorithm with other heuristics for the single machine scheduling problem with sequence-dependent setup times in JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
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  • 1997-12. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces in JOURNAL OF GLOBAL OPTIMIZATION
  • 2002-08-23. A New Ant Colony Algorithm Using the Heterarchical Concept Aimed at Optimization of Multiminima Continuous Functions in ANT ALGORITHMS
  • 2004. Ant Colony Optimization and Swarm Intelligence, 4th International Workshop, ANTS 2004, Brussels, Belgium, September 5-8, 2004. Proceedings in NONE
  • 2004-03. Learning probability distributions in continuous evolutionary algorithms – a comparative review in NATURAL COMPUTING
  • 2003-04-15. Ant Algorithms for the University Course Timetabling Problem with Regard to the State-of-the-Art in APPLICATIONS OF EVOLUTIONARY COMPUTING
  • 2004. Training Neural Networks with GA Hybrid Algorithms in GENETIC AND EVOLUTIONARY COMPUTATION – GECCO 2004
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  • 2004. ACO for Continuous and Mixed-Variable Optimization in ANT COLONY OPTIMIZATION AND SWARM INTELLIGENCE
  • 2000. Ant Colony Optimization for the Total Weighted Tardiness Problem in PARALLEL PROBLEM SOLVING FROM NATURE PPSN VI
  • 1996-04. The continuous reactive tabu search: Blending combinatorial optimization and stochastic search for global optimization in ANNALS OF OPERATIONS RESEARCH
  • 2003-06-18. The Influence of Run-Time Limits on Choosing Ant System Parameters in GENETIC AND EVOLUTIONARY COMPUTATION — GECCO 2003
  • 1986-10. Learning representations by back-propagating errors in NATURE
  • Book

    TITLE

    Metaheuristic Procedures for Training Neutral Networks

    ISBN

    978-0-387-33415-8

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/0-387-33416-5_8

    DOI

    http://dx.doi.org/10.1007/0-387-33416-5_8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1030305707


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