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

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.
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Communication dans un congrès
Carlos Cotta; Jano van Hemert. Evolutionary Computation in Combinatorial Optimization, 7th European Conference, EvoCOP 2007, Apr 2007, Valencia, Spain. Springer Berlin Heidelberg, Lecture Notes in Computer Science, 4446, pp.Pages 176-187, 2007, Evolutionary Computation in Combinatorial Optimization. 〈10.1007/978-3-540-71615-0_16〉
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Sandro Pirkwieser, Günther Raidl, Jakob Puchinger. Combining Lagrangian Decomposition with an Evolutionary Algorithm for the Knapsack Constrained Maximum Spanning Tree Problem. Carlos Cotta; Jano van Hemert. Evolutionary Computation in Combinatorial Optimization, 7th European Conference, EvoCOP 2007, Apr 2007, Valencia, Spain. Springer Berlin Heidelberg, Lecture Notes in Computer Science, 4446, pp.Pages 176-187, 2007, Evolutionary Computation in Combinatorial Optimization. 〈10.1007/978-3-540-71615-0_16〉. 〈hal-01299751〉

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