A simulated annealing algorithm for joint stratification and sample allocation
Section 8. Conclusions

Table of contents

We compared the SAA with the GGA in the case of atomic strata and the TGA in the case of continuous strata (Ballin and Barcaroli, 2020). The k-means algorithm provided good starting points in all cases. When the hyperparameters have been fine-tuned all algorithms attain results of similar quality.

However, the execution times for the recommended hyperparameters are lower for the SAA than for the GGA with respect to atomic strata and traditional genetic algorithm with respect to continuous strata. Delta evaluation also has advantages in reducing the training times needed to find the suitable hyperparameters for the SAA.

The GGA might benefit from being extended into a memetic algorithm by using local search to quickly improve a chromosome before adding it to the GGA chromosome population.

The SAA, by using local search (along with a probabilistic acceptance of inferior solutions), is well suited to navigation out of local minima and the implementation of delta evaluation enables a more complete search of the local neighbourhood than would otherwise be possible in the same computation time.

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Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. Background information
Section 3. The joint stratification and sample allocation problem
Section 4. Outline of the simulated annealing algorithm
Section 5. Improving the performance of the simulated annealing algorithm using delta evaluation
Section 6. Comparing the performance of the two algorithms
Section 7. Comparison with the continuous method in SamplingStrata
Section 8. Conclusions
Section 9. Further work
Acknowledgements
Appendix
References

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