Memory Enhanced Fractional-Order Dung Beetle Optimization for Photovoltaic Parameter Identification

2025-08-09

AI Summary

To address premature convergence in photovoltaic (PV) model parameter identification—caused by nonlinearity, multimodality, and high dimensionality—this paper proposes a Memory-enhanced Fractional-order Dung Beetle Optimizer (MFDBO). The method innovatively integrates a fractional-order calculus-based memory mechanism to improve historical information utilization, employs fractional-order logistic chaotic mapping for diverse population initialization, and introduces an elite chaotic perturbation strategy to enhance local escape capability. Comprehensive evaluations on the CEC2017 benchmark suite and multiple PV models—including single-diode, double-diode, and PV module models—demonstrate that MFDBO significantly outperforms state-of-the-art DBO variants, top-performing CEC algorithms, and mainstream metaheuristic methods in terms of solution accuracy, robustness, and convergence speed. Thus, MFDBO establishes an efficient and reliable new paradigm for high-precision PV parameter identification.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Computer Vision: Learning & Optimization for CV
• Machine Learning: Optimization

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Accurate parameter identification in photovoltaic (PV) models is crucial for performance evaluation but remains challenging due to their nonlinear, multimodal, and high-dimensional nature. Although the Dung Beetle Optimization (DBO) algorithm has shown potential in addressing such problems, it often suffers from premature convergence. To overcome these issues, this paper proposes a Memory Enhanced Fractional-Order Dung Beetle Optimization (MFO-DBO) algorithm that integrates three coordinated strategies. Firstly, fractional-order (FO) calculus introduces memory into the search process, enhancing convergence stability and solution quality. Secondly, a fractional-order logistic chaotic map improves population diversity during initialization. Thirdly, a chaotic perturbation mechanism helps elite solutions escape local optima. Numerical results on the CEC2017 benchmark suite and the PV parameter identification problem demonstrate that MFO-DBO consistently outperforms advanced DBO variants, CEC competition winners, FO-based optimizers, enhanced classical algorithms, and recent metaheuristics in terms of accuracy, robustness, convergence speed, while also maintaining an excellent balance between exploration and exploitation compared to the standard DBO algorithm.

Problem

• Improves PV model parameter identification accuracy
• Addresses premature convergence in DBO algorithm
• Enhances exploration and exploitation balance

Innovation

• Fractional-order calculus enhances search memory
• Logistic chaotic map boosts population diversity
• Chaotic perturbation mechanism escapes local optima