Enhancing convergence efficiency of self-propelled agents using direction preference

Chen, Yurong; Zhang, Xianxia; Yu, Yinsheng; Ma, Shuang; Yang, Banghua

doi:10.1016/j.physa.2021.126415

Cited by 1 publication

(2 citation statements)

References 34 publications

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“…Providing more information Hierarchical society [24], Neighbors' degree [25] Weight difference [26], Direction preference [27] Adaptive velocity [28] Providing less information Evolutionary game theory [19], Line of sight [30] RVFVM [29], RNSVM [31] The proposed new neighbor strategy…”

Section: Category Methodsmentioning

confidence: 99%

“…Methods to accomplish improvements in the convergence performance of the original VM are another popular research branch, and this paper divides them into two categories. The first category realizes improvement by providing particles in VM with more information when updating their directions [24][25][26][27][28], and the second category is ultimately the opposite, providing less information [19,[29][30][31]. Examples of the first category include the adaptive velocity model proposed by Li Wei et al, where particles adjust their speed constantly according to additional consensus information provided [28].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Vicsek Model of Swarms Based on a New Neighbor Strategy Considering View and Distance

Wang,

Zhao,

2023

Applied Sciences

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Collective behaviors in nature and human societies have been intensively studied in recent decades. The Vicsek model is one of the typical models that explain self-ordered particle systems well. In the original Vicsek model, the neighbor strategy takes all its neighbors’ mean directions into account when updating particles’ directions, which leads to a longer convergence time and higher computation cost due to the excess number of neighbors. In this paper, we introduce a new neighbor strategy to the Vicsek model. It defines that each particle will only select a certain number of particles with the farthest distance that fall into its vision sector as its neighbors. In addition, we classify the Vicsek model as the static model and the dynamic model according to whether the features of particles in the model are constant or not. Moreover, we design a new rule to apply the new neighbor strategy to dynamic Vicsek models. The simulation results indicate that our new neighbor strategy can significantly decrease the average number of particles’ neighbors but still be able to further enhance the Vicsek model’s convergence performance. The comparative results found that the static and dynamic model applied with the new neighbor strategy outperforms the models that only apply view restriction or remote neighbor strategy in noiseless and noisy conditions.

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