Beaver behavior optimizer: A novel metaheuristic algorithm for solar PV parameter identification and engineering problems.

Introduction: 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.

Objectives: 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.

A comprehensive analysis of digital inclusive finance's influence on high quality enterprise development through fixed effects and deep learning frameworks.

In the context of global economic transformation, high-quality enterprise development (HQED) is crucial for driving economic growth, particularly through enhancing Total Factor Productivity (TFPLP). Digital Inclusive Finance (DIF), as a classical financial model, plays an important role in promoting high-quality enterprise development. To explore the relationship between TFP and DIF, we first applied traditional double fixed-effects models, along with robustness and heterogeneity tests, for modeling experiments.

Multiple objectives escaping bird search optimization and its application in stock market prediction based on transformer model.

Stock market prediction has long attracted the attention of academia and industry due to its potential for substantial financial returns. Despite the availability of various forecasting methods, such as CNN, LSTM, BiLSTM, GRU, and Transformer, the hyperparameter optimization of these models often faces limitations, particularly in single-objective optimization, where they can easily fall into local optima. To address this issue, this paper proposes an innovative multi-objective optimization algorithm-the Multi-Objective Escape Bird Algorithm (MOEBS)-and introduces the MOEBS-Transformer architecture to enhance the efficiency and effectiveness of hyper-parameter optimization for Transformer models.