Collective behaviors of two-component swarms

You, Sang Koo; Kwon, Dae‐Hyuk; Park, Yong-ik; Kim, Sun Myong; Chung, Minhwa; Kim, Chul Koo

doi:10.1016/j.jtbi.2009.08.025

Cited by 22 publications

(9 citation statements)

References 24 publications

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“…It is worth mentioning that we are able to determine that the rate of consensus is exponentially fast (using a robustness result of Mao (1996)), which has not been addressed for group consensus problems so far. Recent agent-based simulations on multiple-component swarms reveal that (Vab & Skaret, 2008;You et al, 2009) the motion of a swarm may lead to respective flocking behaviours from group to group within a swarm due to different behavioural parameters involved in the model. In these applications, the rate of consensus plays an important role.…”

Section: Introductionmentioning

confidence: 99%

Group consensus of multi-agent systems in directed networks with noises and time delays

Shang¹

2013

International Journal of Systems Science

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In this paper, group consensus problems in fixed directed networks of dynamic agents are investigated. Group consensus means that the agents in each group share a consistent value while there is no agreement between any two groups. Based on algebraic graph theory, sufficient conditions guaranteeing group consensus under the proposed control protocol in the presence of random noises and communication delays are derived. The analysis uses a stability result of Mao for stochastic differential delay equations, which ensures the consensus can be achieved almost surely and exponentially fast. Numerical examples are provided to demonstrate the availability of the obtained results as well as the effect of time delay/noise intensity.

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Section: Introductionmentioning

confidence: 99%

Group consensus of multi-agent systems in directed networks with noises and time delays

Shang¹

2013

International Journal of Systems Science

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“…In this section, we will introduce a new CI algorithm which inspired by the collective decision-making mechanisms among the animal groups (Sulis 1997;Tollefsen 2006;Nicolis et al 2003;Schutter et al 2001;You et al 2009;Couzin 2009;Aleksiev et al 2008;Stradner et al 2013;Zhang et al 2012b, Niizato andGunji 2011;Oca et al 2011;Eckstein et al 2012;Petit and Bon 2010).…”

Section: Collective Animal Behaviour Algorithmmentioning

confidence: 99%

Emerging Biology-based CI Algorithms

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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In this chapter, a group of (more specifically 56 in total) emerging biology-based computational intelligence (CI) algorithms are introduced. We first, in Sect. 17.1, describe the organizational structure of this chapter. Then, from Sects. 17.2 to 17.57, each section is dedicated to a specific algorithm which falls within this category, respectively. The fundamentals of each algorithm and their corresponding performances compared with other CI algorithms can be found in each associated section. Finally, the conclusions drawn in Sect. 17.58 closes this chapter.

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“…The importance of multi-consensus is not limited to engineering applications; it is, for instance, momentous in brain science where, thanks to the connectivity and the structure of the brain, each area could perform specific task [11], as well in other natural systems, e.g., bird flocks or schools of fish splitting into different subgroups for avoiding predation or for foraging. Examples of multi-consensus are also found in social systems, e.g., the dynamics of different L. V. Gambuzza coexisting opinions or pattern formation in bacteria colonies [12], [13].…”

Section: Introductionmentioning

confidence: 98%

“…E-mail: [lucia.gambuzza,mattia.frasca]@dieei.unict.it. coexisting opinions or pattern formation in bacteria colonies [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Distributed Control of Multiconsensus

Gambuzza

Frasca

2021

IEEE Trans. Automat. Contr.

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We consider the problem of steering a multi-agent system to multi-consensus, namely a regime where groups of agents agree on a given value which may be different from group to group. We first address the problem by using distributed proportional controllers that implement additional links in the network modeling the communication protocol among agents and introduce a procedure for the optimal selection of them. Both the cases of single integrators and of second-order dynamics are taken into account and the stability for the multi-consensus state is studied, ultimately providing conditions for the gain of the controllers. We then extend the approach to controllers that either add or remove links in the original structure, by preserving eventually the weak connectedness of the resulting graph.

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Collective behaviors of two-component swarms

Cited by 22 publications

References 24 publications

Group consensus of multi-agent systems in directed networks with noises and time delays

Group consensus of multi-agent systems in directed networks with noises and time delays

Emerging Biology-based CI Algorithms

Distributed Control of Multiconsensus

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