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MECOA: A Multi-Strategy Enhanced Coati Optimization Algorithm for Global Optimization and Photovoltaic Models

Hang Chen1,2, Maomao Luo3,4

  • 1General Education School, Xi'an Eurasia University, Xi'an 710065, China.

Biomimetics (Basel, Switzerland)
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

The Multi-strategy Enhanced Coati Optimization Algorithm (MECOA) improves global optimization and photovoltaic (PV) model parameter identification. MECOA demonstrates superior performance and efficiency over traditional methods in complex engineering tasks.

Keywords:
coati optimization algorithmexploration-exploitationglobal optimizationparameter estimationphotovoltaic models

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Renewable Energy Systems

Background:

  • Traditional Coati Optimization Algorithm (COA) faces limitations in global exploration, population cooperation, and convergence efficiency.
  • Accurate parameter identification for photovoltaic (PV) models is crucial for system efficiency and reliability.

Purpose of the Study:

  • To propose a Multi-strategy Enhanced Coati Optimization Algorithm (MECOA) to overcome the limitations of the traditional COA.
  • To enhance the performance of COA for global optimization and PV model parameter identification.

Main Methods:

  • MECOA incorporates elite-guided search with Lévy flights for balanced exploration-exploitation.
  • Horizontal crossover is implemented to improve information sharing and cooperative search efficiency.
  • Precise elimination strategy removes low-fitness individuals and generates new ones around the best solution to enhance population quality.

Main Results:

  • MECOA achieved superior performance on CEC2017 and CEC2022 benchmark suites, outperforming COA and other leading algorithms.
  • Statistical analysis confirmed MECOA's significant superiority over COA.
  • Applied to PV models, MECOA significantly reduced RMSE for single-diode models and achieved excellent accuracy for double-diode models.

Conclusions:

  • MECOA effectively addresses the limitations of the traditional COA.
  • The proposed algorithm demonstrates robust and efficient performance in complex engineering optimization problems.
  • MECOA provides a reliable solution for accurate modeling and optimization of PV systems.