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Optimizing human activity patterns using global sensitivity analysis.

Geoffrey Fairchild1, Kyle S Hickmann2, Susan M Mniszewski3

  • 1Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, NM, USA.

Computational and Mathematical Organization Theory
|January 13, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a method to create realistic population activity schedules using the Dynamic Activity Simulation Engine (DASim). It efficiently tunes schedule regularity using Harmony Search and global sensitivity analysis for better simulations.

Keywords:
Agent-based modelingBayesian Gaussian process regressionGlobal optimizationGlobal sensitivity analysisHarmony searchSample entropy

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Area of Science:

  • Computational modeling
  • Simulation science
  • Operations research

Background:

  • Realistic activity patterns are vital for population modeling in areas like disease spread and disaster response.
  • Current simulation methods require efficient ways to generate complex, dynamic activity schedules.

Purpose of the Study:

  • To develop and demonstrate an efficient method for tuning activity regularity in population schedules.
  • To reduce the computational complexity of optimizing high-dimensional simulation parameters.

Main Methods:

  • Utilized the Dynamic Activity Simulation Engine (DASim) to generate population activity schedules.
  • Employed Sample Entropy (SampEn) to quantify schedule regularity.
  • Applied Bayesian Gaussian process regression for global sensitivity analysis.
  • Used Harmony Search (HS) for global optimization of SampEn.

Main Results:

  • Harmony Search combined with global sensitivity analysis efficiently tunes SampEn with minimal iterations.
  • Identified key parameters influencing SampEn variance through sensitivity analysis.
  • Demonstrated effective tuning of activities for realistic pattern generation.

Conclusions:

  • The developed methods efficiently optimize high-dimensional computer simulations, particularly for dynamic activity schedule generation.
  • Global sensitivity analysis effectively guides optimization and emulation processes.
  • Represents a significant advancement towards automated tuning of complex simulations.