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ACO Algorithm for MKP Using Various Heuristic Information

  • Stefka Fidanova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2542)

Abstract

The ant colony optimization (ACO) algorithms are being applied successfully to diverse heavily constrained problems: traveling salesman problem, quadratic assignment problem. Early applications of ACO algorithms have been mainly concerned with solving ordering problems. In this paper, the principles of the ACO algorithm are applied to the multiple knapsack problem (MKP). In the first part of the paper we explain the basic principles of ACO algorithm. In the second part of the paper we propose different types of heuristic information and we compare the obtained results.

Keywords

Travel Salesman Problem Solution Component Quadratic Assignment Problem Pheromone Trail Heuristic Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Stefka Fidanova
    • 1
  1. 1.IRIDIAUniversité Libre de BruxellesBruxellesBelgium

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