Estimation of photovoltaic parameters by dynamic updating and selecting a snake optimizer based on multi-directional optimization

Abstract

A multi-strategy synergistic learning snake optimizer (MSSO) is proposed to address the challenges of high computational cost, limited identification of key parameters, and inaccurate results that are still prevalent in swarm intelligence algorithms when managing the nonlinear dynamics of solar photovoltaic systems. This paper, for the first time, explores the intrinsic mechanisms between the four behavioral patterns generated by snakes, which are influenced by food quantity and temperature, and their impact on the diversity and convergence of snake optimization algorithms. It also discusses the limitations observed, offering a novel interpretation of snake optimization algorithms from a fresh perspective. The innovatively proposed superior point strategy, adaptive snake spiral foraging strategy, and dynamic update selection mechanism with multi-directional optimization enable the algorithm to learn from the global optimum and the neighborhood optimum, reducing individuals’ over-reliance on optimal positions and accelerating convergence. Extensive experiments are conducted using CEC2017 and CEC2011 benchmarks on 43 function problems and three application problems for photovoltaic parameter estimation to evaluate the performance of MSSO. A comparative analysis with 26 metaheuristic algorithms (MAs) indicates that MSSO converges more rapidly and ranks first in terms of mean, standard deviation, and Wilcoxon and Friedman tests. The results of the half violin plot combined with scatter plots further illustrate that MSSO exhibits a denser data cloud, a more concentrated distribution density of optimal values, fewer outliers, and enhanced stability. Additionally, a higher quality solution can be obtained with only 50% of the iterations required, without any additional computational time. Finally, on the three application problems of photovoltaic parameter estimation, compared to 26 MAs, the solutions provided by MSSO ranked first in terms of mean and Root Mean Square Error (RMSE), and the performance of the algorithms can be improved by up to 94.0% and 2.7%, which highlights the superiority, universality, and applicability of the algorithms.