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A Modified Artificial Protozoa Optimizer for Robust Parameter Identification in Nonlinear Dynamic Systems.

Davut Izci1,2, Serdar Ekinci3, Gökhan Yüksek4

  • 1Department of Electrical and Electronic Engineering, Bursa Uludag University, Bursa 16059, Turkey.

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Summary
This summary is machine-generated.

A new modified artificial protozoa optimizer (mAPO) enhances parameter identification in complex dynamic systems. It balances global search and local refinement for improved accuracy and robustness in nonlinear and chaotic systems.

Keywords:
Nelder–Mead simplex methodartificial protozoa optimizernonlinear systemsparameter identificationrandom learning mechanism

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Nonlinear dynamics

Background:

  • Accurate parameter identification in nonlinear and chaotic systems is crucial for modeling and control.
  • Existing optimization algorithms struggle to balance global exploration and local refinement in complex, multimodal landscapes.
  • Challenges include high dimensionality and the need for robust solutions in dynamic environments.

Purpose of the Study:

  • To develop a modified artificial protozoa optimizer (mAPO) for enhanced parameter identification.
  • To improve global search capability and local refinement in optimization.
  • To validate the performance of mAPO on benchmark functions and in real-world nonlinear system identification tasks.

Main Methods:

  • Developed mAPO by integrating a probabilistic random learning strategy and a Nelder-Mead simplex-based local refinement stage into the original artificial protozoa optimizer (APO).
  • Evaluated mAPO using the CEC2017 benchmark suite, including shifted/rotated, hybrid, and composition functions.
  • Applied mAPO to parameter identification of the Rössler chaotic system and a permanent magnet synchronous motor (PMSM) under static and dynamic conditions.

Main Results:

  • mAPO demonstrated improved mean performance and reduced variability on the CEC2017 benchmark suite, indicating enhanced robustness.
  • In nonlinear system identification, mAPO achieved smaller objective function values, more accurate parameter estimates, and superior statistical stability compared to APO and other state-of-the-art optimizers.
  • Exact parameter reconstruction with zero error was achieved for the PMSM, with rapid and smooth convergence observed.

Conclusions:

  • The proposed mAPO effectively balances global exploration and local exploitation for accurate parameter identification in complex nonlinear and chaotic systems.
  • mAPO offers enhanced robustness and scalability, outperforming existing methods on benchmark functions and practical dynamic system identification.
  • The algorithm shows significant potential for applications requiring precise parameter estimation in dynamic and time-varying environments.