Research

Game and Optimization for Swarm Intelligence and Cyber-physical Systems

The deep integration of swarm intelligence and autonomous decision-making technologies is fundamentally transforming the architecture of complex decision systems. These systems are increasingly adopting a distributed paradigm, where the dynamics of the system and the local decision-making processes of autonomous agents are tightly coupled. This shift gives rise to collective intelligent decision-making systems, driven by individual autonomy yet coordinated to achieve system-level goals.

In this context, game-theoretic and optimization-based methods for swarm intelligence have become key technologies for enabling cooperation and coordination in autonomous multi-agent systems. They are widely applied in areas such as:

• Resource allocation in smart cities

• Cooperative control of autonomous swarms

• Route planning in intelligent transportation

• Strategy design in sharing economies

Despite their promise, these systems face fundamental challenges. Due to heterogeneous objectives and self-interested behaviors among agents under networked coupling, there is often a conflict between individual rationality and collective welfare. This tension can lead to suboptimal group outcomes and even Pareto inefficiency. Traditional game-theoretic and optimization algorithms that lack incentive-aware design are often insufficient to guarantee efficient and stable system performance in such environments. Furthermore, factors such as individual autonomy constraints, budget limitations, and the decentralized nature of the system introduce significant information limitations and decision complexity. 

My research tackles these challenges by developing theoretical foundations and distributed algorithms for game and optimization in complex, decentralized cyber-physical systems. From a mathematical perspective, I aim to understand and design the infrastructure systems that will support future society.