תקציר
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.
שפה מקורית | אנגלית |
---|---|
עמודים (מ-עד) | 249-271 |
מספר עמודים | 23 |
כתב עת | OR Spectrum |
כרך | 44 |
מספר גיליון | 1 |
מזהי עצם דיגיטלי (DOIs) | |
סטטוס פרסום | פורסם - מרץ 2022 |
פורסם באופן חיצוני | כן |
הערה ביבליוגרפית
Publisher Copyright:© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.