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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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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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A levy chaotic horizontal vertical crossover based artificial hummingbird algorithm for precise PEMFC parameter

Pradeep Jangir1,2,3,4,5, Absalom E Ezugwu6, Kashif Saleem7

  • 1University Centre for Research and Development, Chandigarh University, Gharuan, Mohali, 140413, India.

Scientific Reports
|November 28, 2024
PubMed
Summary
This summary is machine-generated.

A new algorithm, the Lévy Chaotic Artificial Hummingbird Algorithm (LCAHA), accurately identifies parameters in Proton Exchange Membrane Fuel Cell (PEMFC) models. LCAHA outperforms traditional methods in accuracy and speed, ensuring reliable fuel cell performance.

Keywords:
Artificial hummingbird algorithmElectrical Engineering OptimizationLCAHAOptimal parameter estimationPEM fuel cell

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Area of Science:

  • Engineering
  • Computational Intelligence
  • Renewable Energy

Background:

  • Proton Exchange Membrane Fuel Cells (PEMFCs) are crucial for clean energy, but accurate parameter identification is challenging.
  • Existing optimization algorithms often struggle with the complexity and non-linearity of PEMFC models.

Purpose of the Study:

  • To develop an enhanced optimization algorithm for accurate PEMFC parameter identification.
  • To introduce the Lévy Chaotic Artificial Hummingbird Algorithm (LCAHA) for improved PEMFC modeling.

Main Methods:

  • Proposed the Lévy Chaotic Artificial Hummingbird Algorithm (LCAHA), integrating chaotic mapping, Lévy flights, and a novel foraging strategy.
  • Utilized LCAHA to identify unknown parameters in PEMFC models.
  • Compared LCAHA's performance against Particle Swarm Optimization (PSO), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Sparrow Search Algorithm (SSA).

Main Results:

  • LCAHA achieved a significantly lower Sum of Squared Errors (SSE) of 0.0254 compared to PSO (0.1924) and GWO (0.0364).
  • LCAHA demonstrated superior stability with a standard deviation of 4.59E-08.
  • LCAHA exhibited faster convergence, reducing runtime by approximately 47% compared to DE and SSA.
  • Simulated and actual I-V curves across six PEMFC stacks showed close alignment using LCAHA.

Conclusions:

  • LCAHA offers superior accuracy, stability, and efficiency for PEMFC parameter identification.
  • The proposed algorithm validates its robustness and reliability for real-world PEMFC applications.
  • LCAHA represents a significant advancement over existing optimization techniques for fuel cell modeling.