Promoting collective motion of self-propelled agents by distance-based influence

Yang, Han-Xin; Zhou, Tao; Huang, Liang

doi:10.1103/physreve.89.032813

Cited by 16 publications

(4 citation statements)

References 52 publications

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“…(19) and (20) are tangentially touching at the critical fraction for the first order phase transition,…”

Section: No-feedback Conditionmentioning

confidence: 98%

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Robustness of network of networks with interdependent and interconnected links

Dong

et al. 2015

Physica A: Statistical Mechanics and its Applications

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h i g h l i g h t s• Understanding the robustness of NON with interdependent and interconnected links is helpful to design resilient infrastructures.• The framework is developed to study analytically and numerically the robustness of this system with no-feedback and feedback conditions.• The results suggest that feedback condition of dependency links, in contrast to no-feedback condition, makes the system extremely vulnerable.• The theory can be applied to networks with given degree distribution and other basic topological structure of network. a b s t r a c tRobustness of network of networks (NON) has been studied only for dependency coupling and only for connectivity coupling (Leicht and Souza, arxiv:0907.0894). The case of network of networks with both interdependent and interconnected links is more complicated, and also more close to real-life coupled network systems. Understanding the robustness of NON with interdependent and interconnected links is helpful to design resilient infrastructures. Here we develop a framework to study analytically and numerically the robustness of this system with no-feedback and feedback conditions for the case of starlike NON. When assumed that all degree distributions of the connectivity intra-and inter-links are Poissonian, we find that the system undergoes from second order through hybrid order to first order phase transition as coupling strength q increases. Additionally, for both conditions, the results suggest that increasing density of connectivity links (intra-connectivity links or inter-connectivity links) can increase the robustness of the system, while the interdependency links decrease its robustness. Furthermore, by comparing critical attacking strength under same parameters for both conditions, we also find that feedback condition of dependency links, in contrast to no-feedback condition, makes the system extremely vulnerable. Although our detailed analysis is for Poisson degree distribution, the theory can be applied to any degree distribution and other basic topological structure of network such like tree-like and loop-like NON.

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“…(19) and (20) are tangentially touching at the critical fraction for the first order phase transition,…”

Section: No-feedback Conditionmentioning

confidence: 98%

“…Most of the research have focused on isolated networks that do not connect with or depend on other networks [14][15][16][17][18][19][20]. However, most real-world infrastructures are not isolated, are often interconnected, or interdependent, or both [21][22][23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Robustness of network of networks with interdependent and interconnected links

Dong

et al. 2015

Physica A: Statistical Mechanics and its Applications

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“…Inspired by the above results, we construct a new index named information coupling degree (ICD) to denote the mutual interaction intensity between individuals. ICD is a motion dependent variable that is relevant to many factors; for example, individuals are usually more influenced by the close neighbors (distance) [26], they tend to be more sensitive to fast moving neighbors (velocity) [19,27], and individual with more neighbors are usually more dominated (number of neighbors) [28]. In particular, we choose the two most dominating factors, the relative position and relative velocity between individuals, to design ICD in the following form:…”

Section: Information Coupling Degree Based Fission Rulementioning

confidence: 99%

Self-Organized Fission Control for Flocking System

Liu

Yang

Lei

et al. 2015

Journal of Robotics

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This paper studies the self-organized fission control problem for flocking system. Motivated by the fission behavior of biological flocks, information coupling degree (ICD) is firstly designed to represent the interaction intensity between individuals. Then, from the information transfer perspective, a “maximum-ICD” based pairwise interaction rule is proposed to realize the directional information propagation within the flock. Together with the “separation/alignment/cohesion” rules, a self-organized fission control algorithm is established that achieves the spontaneous splitting of flocking system under conflict external stimuli. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed algorithm.

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“…This approach avoids the sequential search of canonical clustering algorithms and permits a scalable implementation. Cetin et al [25] implemented the fuzzy logic controller into UAV swarm control model for the control of the altitude, speed, and heading. Yang et al [26] proposed an SR control model with tunable parameter for agent aggregation behaviors.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Rule System Based Control Model with Tunable Parameters for Swarm Robotic Control in Confined Environment

Wang,

Xing,

Wang

et al. 2024

Complex Syst. Model. Simul.

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Enhancing the adaptability of Unmanned Aerial Vehicle (UAV) swarm control models to cope with different complex working scenarios is an important issue in this research field. To achieve this goal, control model with tunable parameters is a widely adopted approach. In this article, an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking (MO O-Flocking) is proposed. The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters. To achieve multi-objective parameter tuning, a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2 (ISPEA2) is designed. Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives. Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.

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Promoting collective motion of self-propelled agents by distance-based influence

Cited by 16 publications

References 52 publications

Robustness of network of networks with interdependent and interconnected links

Robustness of network of networks with interdependent and interconnected links

Self-Organized Fission Control for Flocking System

Multi-Objective Rule System Based Control Model with Tunable Parameters for Swarm Robotic Control in Confined Environment

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