Journal of Heuristics

, Volume 8, Issue 2, pp 215–239

Cooperative Strategies for Solving the Bicriteria Sparse Multiple Knapsack Problem

Authors

  • F. Sibel Salman
    • GSIA, Carnegie Mellon University
  • Jayant R. Kalagnanam
    • IBM T. J. Watson Research Center
  • Sesh Murthy
    • IBM T. J. Watson Research Center
  • Andrew Davenport
    • IBM T. J. Watson Research Center
Article

DOI: 10.1023/A:1017964608086

Cite this article as:
Salman, F.S., Kalagnanam, J.R., Murthy, S. et al. Journal of Heuristics (2002) 8: 215. doi:10.1023/A:1017964608086

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.

multiple knapsackbicriteriamultiple heuristicscooperationasynchronous teams

Copyright information

© Kluwer Academic Publishers 2002