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Enhancing the convergence efficiency of a self-propelled agent system via a weighted model
Jianxi Gao1, Zhuo Chen, Yunze Cai
1Department of Automation, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, People's Republic of China. jianxi.gao@gmail.com
Summary
This study explores weighted self-propelled agents, finding that increased influence from neighbors with more connections enhances consensus. A specific scaling exponent improves convergence efficiency in agent systems.
Area of Science:
- Complex Systems
- Agent-Based Modeling
- Statistical Physics
Background:
- Self-propelled agents are fundamental in modeling collective behaviors.
- Understanding how local interactions influence global consensus is crucial.
- Existing models often simplify neighbor influence.
Purpose of the Study:
- To investigate a weighted self-propelled agent system.
- To introduce a scaling exponent to model varying neighbor influence.
- To analyze the impact of this exponent on consensus and convergence.
Main Methods:
- Developed a weighted self-propelled agent model.
- Incorporated exponential weights based on neighbor count.
- Introduced a scaling exponent (0 to infinity) for neighbor number.
- Performed simulations to observe system dynamics.
Main Results:
- Enhanced convergence efficiency was observed when the scaling exponent was 1.
- Increasing the exponent strengthened the effect of neighbor weights.
- Higher exponents facilitated easier achievement of direction consensus within the agent network.
Conclusions:
- Neighbor influence, modulated by a scaling exponent, significantly impacts agent system consensus.
- The proposed weighting mechanism offers a more nuanced approach to agent interactions.
- Optimal consensus is achievable by tuning the neighbor influence exponent.
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