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Combining Lagrangian Decomposition with an Evolutionary Algorithm for the Knapsack Constrained Maximum Spanning Tree Problem

  • Sandro Pirkwieser
  • Günther R. Raidl
  • Jakob Puchinger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4446)

Abstract

We present a Lagrangian decomposition approach for the Knapsack Constrained Maximum Spanning Tree problem yielding upper bounds as well as heuristic solutions. This method is further combined with an evolutionary algorithm to a sequential hybrid approach. Experimental investigations, including a comparison to a previously suggested simpler Lagrangian relaxation based method, document the advantages of the new approach. Most of the upper bounds derived by Lagrangian decomposition are optimal, and together with the evolutionary algorithm, large instances with up to 12000 nodes can be either solved to provable optimality or with a very small remaining gap in reasonable time.

Keywords

Local Search Planar Graph Knapsack Problem Lagrangian Relaxation Steiner Tree Problem 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Sandro Pirkwieser
    • 1
  • Günther R. Raidl
    • 1
  • Jakob Puchinger
    • 2
  1. 1.Institute of Computer Graphics and Algorithms, Vienna University of Technology, ViennaAustria
  2. 2.National ICT Australia (NICTA) Victoria Laboratory, Dep. of Comp. Sci. & Softw. Eng., The University of MelbourneAustralia

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