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.
Bibliographical notePublisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
- Genetic algorithms
- Global optimization
- Search algorithms
- Stopping point