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

Ran Etgar () and Yuval Cohen ()
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Ran Etgar: Faculty of Engineering
Yuval Cohen: Afeka College for Engineering

OR Spectrum: Quantitative Approaches in Management, 2022, vol. 44, issue 1, No 9, 249-271

Abstract: 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.

Keywords: Meta-heuristics; Genetic algorithms; Global optimization; Stopping point; Search algorithms (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00291-021-00650-z

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