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Cooperative Strategies for Solving the Bicriteria Sparse Multiple Knapsack Problem

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Abstract

For hard optimization problems, it is difficult to design heuristic algorithms which exhibit uniformly superior performance for all problem instances. As a result it becomes necessary to tailor the algorithms based on the problem instance. In this paper, we introduce the use of a cooperative problem solving team of heuristics that evolves algorithms for a given problem instance. The efficacy of this method is examined by solving six difficult instances of a bicriteria sparse multiple knapsack problem. Results indicate that such tailored algorithms uniformly improve solutions as compared to using predesigned heuristic algorithms.

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Authors and Affiliations

  1. GSIA, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    F. Sibel Salman

  2. IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598, USA

    Jayant R. Kalagnanam, Sesh Murthy & Andrew Davenport

Authors
  1. F. Sibel Salman
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  2. Jayant R. Kalagnanam
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  3. Sesh Murthy
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  4. Andrew Davenport
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Salman, F.S., Kalagnanam, J.R., Murthy, S. et al. Cooperative Strategies for Solving the Bicriteria Sparse Multiple Knapsack Problem. Journal of Heuristics 8, 215–239 (2002). https://doi.org/10.1023/A:1017964608086

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