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Weight annealing

Author

Listed:
  • Ninio, Matan
  • Schneider, Johannes J.

Abstract

Simulated Annealing has become a standard optimization technique for a wide variety of problems: starting at a random configuration and performing a sequence of moves, the system is optimized using a control parameter which partially allows for accepting a deterioration and therefore for climbing over barriers in the energy landscape. Our approach, Weight Annealing, changes the energy landscape by assigning variable weights to the single parts of the proposed problem. We describe the philosophies behind these algorithms and present results for the Traveling Salesman Problem and the Sherrington–Kirkpatrick-model for spin glasses.

Suggested Citation

  • Ninio, Matan & Schneider, Johannes J., 2005. "Weight annealing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 349(3), pages 649-666.
    • Handle: RePEc:eee:phsmap:v:349:y:2005:i:3:p:649-666
    DOI: 10.1016/j.physa.2004.10.022
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    References listed on IDEAS

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    1. Schneider, Johannes & Dankesreiter, Markus & Fettes, Werner & Morgenstern, Ingo & Schmid, Martin & Maria Singer, Johannes, 1997. "Search-space smoothing for combinatorial optimization problems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 243(1), pages 77-112.
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    6. Coy, Steven P. & Golden, Bruce L. & Wasil, Edward A., 2000. "A computational study of smoothing heuristics for the traveling salesman problem," European Journal of Operational Research, Elsevier, vol. 124(1), pages 15-27, July.
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    Cited by:

    1. Ransberger, Martin & Morgenstern, Ingo & Schneider, Johannes J., 2011. "Influence of rounding errors on the quality of heuristic optimization algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(13), pages 2571-2581.
    2. Burke, Edmund K. & Bykov, Yuri, 2017. "The late acceptance Hill-Climbing heuristic," European Journal of Operational Research, Elsevier, vol. 258(1), pages 70-78.

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