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 knapsack bicriteria multiple heuristics cooperation asynchronous teams

Copyright information

© Kluwer Academic Publishers 2002