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

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.