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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Visualizing basins of attraction for different minimization algorithms.

Daniel Asenjo¹, Jacob D Stevenson, David J Wales

¹Department of Chemistry, University of Cambridge , Lensfield Road, Cambridge, CB2 1WE, United Kingdom.

The Journal of Physical Chemistry. B

|May 11, 2013

Summary

This summary is machine-generated.

Different minimization algorithms create varied basins of attraction for atomic systems. Some algorithms yield fragmented basins, unlike steepest-descent pathways, but step size can influence this. FIRE offers a good balance of speed and basin quality.

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Area of Science:

Computational chemistry
Materials science
Statistical physics

Background:

Minimization algorithms are crucial for exploring potential energy surfaces in atomic systems.
Understanding basins of attraction is key to predicting system behavior and stability.

Purpose of the Study:

To investigate and compare the basins of attraction generated by different minimization algorithms for atomic systems.
To analyze how algorithm parameters, like step size, affect basin characteristics.

Main Methods:

Studied basins of attraction for potential energy minima using various minimization algorithms.
Analyzed basin complexity, including boundaries and connectivity.
Compared algorithm performance based on speed and basin fragmentation.

Main Results:

Different algorithms produce basins with varying complexity, from compact to fragmented with disconnected parts.
Basin deviations from steepest-descent pathways can be influenced by adjusting the maximum step size.
While L-BFGS is fastest, the FIRE algorithm demonstrates competitive speed and generates less fragmented basins.

Conclusions:

Algorithm choice significantly impacts the resulting basins of attraction in atomic systems.
The FIRE algorithm presents a favorable option, balancing computational efficiency with the generation of more coherent basins.
Careful selection and parameter tuning of minimization algorithms are essential for accurate potential energy surface exploration.