2025.3-Present School of Mathematical Sciences, Beijing University of Posts and Telecommunications 2014.1-2014.8 School of Computer Science, University of Birmingham 2013.8-2014.2 School of Computer Science and Electronic Engineering, University of Essex 2005.7-2025.3 School of Science, Beijing University of Posts and Telecommunications

Professor, School of Mathematical Sciences, Beijing University of Posts and Telecommunications Reference: AI-Driven Portfolio Optimization with Swarm Intelligence This study explores the integration of artificial intelligence (AI) and swarm intelligence (SI) techniques to enhance portfolio optimization in dynamic financial markets. Traditional portfolio management strategies often struggle to adapt to non-linear market behaviors and high-dimensional data, prompting the need for advanced computational approaches. Leveraging SI algorithms such as Particle Swarm Optimization (PSO) and Difference Evolution (DE), combined with AI-driven predictive models, the proposed framework dynamically balances risk and return by simulating collective decision-making behaviors observed in natural systems. The methodology emphasizes real-time adaptability, incorporating market sentiment analysis, volatility forecasting, and multi-objective optimization to construct diversified portfolios.