Numerical optimization plays a key role in improving the efficiency of solar photovoltaic (PV) systems and solving complex engineering problems. Traditional optimization methods often struggle with finding optimal solutions within a reasonable timeframe due to high-dimensional and non-linear problem landscapes. This study aims to introduce a novel swarm intelligence algorithm, the Beaver Behavior Optimizer (BBO), inspired by the cooperative behaviors of beavers during dam construction. The goal is to validate BBO's performance on both benchmark test functions and real-world engineering problems, particularly in solar PV parameter optimization. The BBO was modeled based on beaver behaviors of material gathering and dam maintenance, integrating exploration and exploitation phases. To assess its performance, experiments were conducted using CEC 2017 and CEC 2022 benchmark functions with varying dimensions (10, 20, 30, 50, 100). Statistical significance was verified using Wilcoxon signed-rank and Friedman mean rank tests. Furthermore, BBO was applied to solve three solar PV parameter identification problems and four real-world engineering problems, comparing its performance with 11 other algorithms. BBO demonstrated superior performance across all benchmark functions and ranked first in tackling solar PV and engineering design problems. It outperformed other state-of-the-art algorithms in most test scenarios, showcasing robust convergence, quick optimization, and minimal variance in results. The results validate BBO as a powerful optimization tool, particularly for solar PV parameter identification and engineering challenges. Its bio-inspired approach effectively balances exploration and exploitation, making it a competitive algorithm for solving complex optimization tasks.
Ouyang et al. (Mon,) studied this question.