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Visiting near-optimal solutions using local search algorithms

In: Compstat 2006 - Proceedings in Computational Statistics

Author

Listed:
  • Sheldon H. Jacobson

    (University of Illinois, Simulation and Optimization Laboratory)

  • Shane N. Hall

    (University of Illinois, Simulation and Optimization Laboratory)

  • Laura A. McLay

    (University of Illinois, Simulation and Optimization Laboratory)

Abstract

This paper presents results on the analysis of local search algorithms to visit near-optimal solutions. The β-acceptable solution probability is used to capture how effectively an algorithm has performed to date and how effectively an algorithm can be expected to perform in the future. An estimator for the expected number of iterations for local search algorithm to visit a β-acceptable solution is obtained. Computational experiments are reported with a modified simulated annealing algorithm applied to four small travelling salesman problem instances with known optimal solutions.

Suggested Citation

  • Sheldon H. Jacobson & Shane N. Hall & Laura A. McLay, 2006. "Visiting near-optimal solutions using local search algorithms," Springer Books, in: Alfredo Rizzi & Maurizio Vichi (ed.), Compstat 2006 - Proceedings in Computational Statistics, pages 471-481, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-1709-6_38
    DOI: 10.1007/978-3-7908-1709-6_38
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