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Optimizing termination decision for meta-heuristic search techniques that converge to a static objective-value distribution

Author

Listed:
  • Ran Etgar

    (Faculty of Engineering)

  • Yuval Cohen

    (Afeka College for Engineering)

Abstract

This paper proposes a new technique for assisting search technique optimizers (most evolutionary, swarm, and bio-mimicry algorithms) to get an informed decision about terminating the heuristic search process. Current termination/stopping criteria are based on pre-determined thresholds that cannot guarantee the quality of the achieved solution or its proximity to the optimum. So, deciding when to stop is more an art than a science. This paper provides a statistical-based methodology to balance the risk of omitting a better solution and the expected computing effort. This methodology not only provides the strong science-based decision making but could also serve as a general tool to be embedded in various single-solution and population-based meta-heuristic studies and provide a cornerstone for further research aiming to provide better search terminating point criteria.

Suggested Citation

  • Ran Etgar & Yuval Cohen, 2022. "Optimizing termination decision for meta-heuristic search techniques that converge to a static objective-value distribution," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 249-271, March.
    • Handle: RePEc:spr:orspec:v:44:y:2022:i:1:d:10.1007_s00291-021-00650-z
    DOI: 10.1007/s00291-021-00650-z
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    References listed on IDEAS

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    3. Andreas C. Nearchou, 2018. "Multicriteria scheduling optimization using an elitist multiobjective population heuristic: the h-NSDE algorithm," Journal of Heuristics, Springer, vol. 24(6), pages 817-851, December.
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    Cited by:

    1. Corominas, Albert, 2023. "On deciding when to stop metaheuristics: Properties, rules and termination conditions," Operations Research Perspectives, Elsevier, vol. 10(C).

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