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A Comparative Study on Evolutionary Multi-objective Optimization Algorithms Estimating Surface Duct.

Qixiang Liao1, Zheng Sheng2, Hanqing Shi3

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Summary
This summary is machine-generated.

This study introduces a multi-objective method for atmospheric duct inversion using Global Positioning System (GPS) data. Performance metrics varied, and higher parameter dimensions led to unstable inversion results, highlighting the need for sufficient data.

Keywords:
GPS, hypervolumeatmospheric ductinverted generational distancemulti-objective optimization algorithm

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Area of Science:

  • Atmospheric science
  • Geophysics
  • Radio propagation

Background:

  • Atmospheric duct inversion is typically a single-objective optimization problem.
  • Existing methods may not fully capture the complexities of atmospheric ducting phenomena.
  • Ground-based Global Positioning System (GPS) data offers a valuable resource for atmospheric studies.

Purpose of the Study:

  • To develop and evaluate a multi-objective optimization method for atmospheric duct inversion.
  • To incorporate source frequency and receiving antenna height into the inversion process.
  • To assess the performance of various multi-objective evolutionary algorithms (MOEAs) for this problem.

Main Methods:

  • Utilized ground-based Global Positioning System (GPS) phase delay and propagation loss data.
  • Developed a multi-objective approach considering source frequency and receiving antenna height.
  • Evaluated seven MOEAs using Hypervolume (HV), Inverted Generational Distance (IGD), and averaged Hausdorff distance (Δ₂).
  • Compared inversion results with simulation and experimental data.

Main Results:

  • Algorithm performance rankings differed based on the chosen performance metrics.
  • High performance in metrics did not guarantee closer inversion results to the true value.
  • Increased retrieved parameter dimensions led to more unstable inversion outcomes.
  • Sufficient observed data appeared to improve inversion accuracy.

Conclusions:

  • The choice of performance metrics significantly impacts the evaluation of MOEAs for atmospheric duct inversion.
  • There is a trade-off between metric-based performance and the accuracy of inversion results.
  • Parameter dimension and data availability are critical factors influencing inversion stability and accuracy.