Collective motion patterns of self-propelled agents with both velocity alignment and aggregation interactions

Area of Science:

• Physics
• Complex Systems
• Biophysics

Background:

• Collective motion is observed in various biological systems.
• Existing models like the Vicsek model focus on velocity alignment.
• The role of aggregation in noisy environments is less understood.

Purpose of the Study:

• To investigate the combined effects of velocity alignment and aggregation on active agent collective motion.
• To analyze how varying proportions of these interactions and external noise influence system dynamics.
• To understand the impact of aggregation on phase transitions and order in collective behavior.

Main Methods:

• Development of a model combining velocity alignment (k) and aggregation (1-k) interactions.
• Extensive numerical simulations on a 2D square lattice.
• Finite-size scaling analysis to determine critical noise levels and transition types.

Main Results:

• Diverse dynamic patterns emerge, mirroring biological systems.
• Aggregation interactions alter critical noise levels and phase transition types.
• A first-order phase transition is observed under weak noise as aggregation decreases.
• An optimal aggregation proportion exists for maximizing order in moderate noise.

Conclusions:

• Aggregation interactions play a significant role in collective motion dynamics.
• The findings offer insights into self-propelled particle systems and biological swarming.
• This research provides a framework for understanding complex emergent behaviors in multi-agent systems.