Large language model-driven dynamic communication strategy generation for multi-swarm particle swarm optimization

Multi-swarm particle swarm optimization (MSO) has emerged as a crucial approach to enhance the search efficiency and global performance of swarm intelligence, showing broad applicability in complex problems. However, existing MSO methods generally rely on predefined communication strategies, which are insufficient to adapt to dynamic changes across different optimization stages and problem characteristics, extremely limiting their flexibility and generalization. To address this issue, this paper proposes a large language model-driven dynamic communication strategy generation for multi-swarm particle swarm optimization framework (L2D-MSO). By constructing natural language prompts that encode the status information of each swarm, L2D-MSO enables real-time reasoning via large language models to dynamically generate communication strategy, guiding information exchange among multiple swarms to accelerate convergence and improve solution quality. In addition, an adaptive temperature mechanism is introduced to adjust the perturbation intensity according to the optimization progress, further enhancing the phase adaptability of strategy generation. Experimental results on the CEC2022 benchmark, robotic dog inspection path planning, and cloud-edge collaborative energy-aware scheduling tasks demonstrate the superior performance of L2D-MSO, validating its advantages in convergence accuracy, search stability, and high-dimensional adaptability across both physical and computational optimization scenarios.

Authors

• Tongbang Jiang
• Shu-Chuan Chu
• Jeng-Shyang Pan
• Václav Snášel
• Yicheng Wei

Keywords

• Multi-swarm particle swarm optimization
• Large language models
• Dynamic communication strategy
• Prompt
• Path planning
• Energy-aware scheduling