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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Magnetic targets positioning method based on multi-strategy improved Grey Wolf optimizer.

Binjie Lu1,2, Zongji Li1, Xiaobing Zhang3,4

  • 1Naval University of Engineering, Wuhan, 430033, Hubei, China.

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|May 2, 2025
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Summary
This summary is machine-generated.

A new Multi-Strategy Improved Grey Wolf Optimizer (MSIGWO) enhances magnetic target state estimation accuracy. This advanced algorithm outperforms existing methods on complex problems, improving reliability in practical applications.

Keywords:
Adaptive dimensional learningAdaptive levy flightDynamic weightsGrey Wolf optimizerMagnetic target state estimationMulti-population fusion evolutionNonlinear convergence factorTent chaos mapping

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Area of Science:

  • Engineering
  • Computer Science
  • Optimization Algorithms

Background:

  • Magnetic target state estimation is crucial but challenged by accuracy issues.
  • Existing Grey Wolf Optimizer (GWO) algorithms struggle with complex problems.
  • Enhanced swarm intelligence is needed for improved estimation accuracy.

Purpose of the Study:

  • To propose and evaluate a Multi-Strategy Improved Grey Wolf Optimizer (MSIGWO) for magnetic target state estimation.
  • To enhance the accuracy and robustness of magnetic target state estimation.
  • To address the limitations of conventional GWO in complex optimization scenarios.

Main Methods:

  • Introduced Tent chaos mapping for improved initialization and diversity.
  • Implemented multi-population fusion evolution strategies for enhanced search.
  • Utilized nonlinear convergence factors and dynamic weight strategies for balanced exploration/exploitation.
  • Incorporated adaptive dimensional learning and adaptive Levy flight for robust optimization.

Main Results:

  • MSIGWO demonstrated superior performance over GWO and its variants on the CEC2018 benchmark functions.
  • Statistical indicators and Friedman tests confirmed MSIGWO's enhanced accuracy and global search capabilities.
  • The algorithm proved effective and applicable in magnetic target state estimation problems.

Conclusions:

  • The proposed MSIGWO algorithm significantly improves magnetic target state estimation accuracy.
  • MSIGWO offers enhanced population diversity, convergence speed, and global search ability.
  • This optimized approach provides a more reliable solution for practical magnetic target state estimation challenges.