Ant colony optimization based model for network zero-configuration

Ring, Steven; Munirajan, V.K.; Cole, Eric

doi:10.1109/spcom.2004.1458494

Cited by 3 publications

(4 citation statements)

References 4 publications

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“…An important feature of the swarm based model is concerned with partitions which do not need to be considered as special cases. On the contrary, when partitions merge, there is a sudden increase in the number of IP address conflicts and the system has to make a large effort to respond to the new environmental change, [11], [23].…”

Section: B the Antconf Protocolmentioning

confidence: 99%

“…Section 2 briefly reviews references on the swarm paradigm, specifically based on ant and bee behaviors, with a glance at their use for the solution of the MANET routing problem. Section 3 first defines the IP address auto-configuration problem for ad-hoc networks, then describes two well kwon protocols, such as the Buddy protocol and the AntConf protocol, developed with a stateful approach based on the binary split idea of [10], and with a swarm intelligence based model targeted at network administration [11], respectively. Section 4 contains the description of the AutoBeeConf protocol, our proposal for the IP address auto-configuration for MANET that integrates the advantages deriving from the classical approaches with the benefits arising from the most typical activities of a bee swarm.…”

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confidence: 99%

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AutoBeeConf : A swarm intelligence algorithm for MANET administration

Caputo¹,

Davino²,

Santis³

et al. 2013

IJARAI

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Section: B the Antconf Protocolmentioning

confidence: 99%

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confidence: 99%

AutoBeeConf : A swarm intelligence algorithm for MANET administration

Caputo¹,

Davino²,

Santis³

et al. 2013

IJARAI

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“…This technique guarantees reliable data transmission and is commonly used in routing problems. While previous studies have discussed ACO optimization for routing in TCP networks [17][18][19], this paper focuses on optimizing the flow of packets in a congested router by tuning the PI controller using the MOACO technique. Finally, the authors present a MATLAB simulation to demonstrate the performance of the proposed method (MOACO) for the PI-AQM control system.…”

Section: Introductionmentioning

confidence: 99%

Perspective Chapter: Enhancing Network Efficiency Using Ant Colony Optimization and Pareto Optimality for Tuning PI Controller in Congested Routers

Chebli

2023

Disturbance Rejection Control

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The objective of this manuscript is to stabilize the queue of the router congestion window by designing an active queue management (AQM). The problem is dealt with under the theory of the command by using the regulator PI for that purpose. The tuning of this controller is based on a new approach of stabilization that relies on an extension of Hermite-Biehler theorem applied to quasi-polynomials. This stabilization method turns out relevant to seek the optimized results achieved within this stability region. For that, the optimization is performed using an improved multi-objective ant colony optimization (ACO) algorithm. The performance of the proposed control scheme is evaluated via a series of numerical simulations in MATLAB and Simulink.

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“…The ACO optimization topic for the routing in TCP networks has already been discussed (Khanpara, Valiveti, & Kotecha, 2010;Rathore & Khan, 2017;Ring, Munirajan, & Cole, 2004), but without involving the problem of congestion in this algorithm. The contribution made via this paper is indeed to optimize the flow of packets in a congested router by PID controller tuned by the MOACO technique.…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Optimization based on Pareto optimality: application to a congested router controlled by PID regulation

Chebli

Elakkary

Sefiani

2018

Systems Science & Control Engineering

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The subject of this research work is to stabilize the network TCP (transmission control protocol) as well as the queue of the router congestion by designing an Active Queue Management scheme able to ensure this role. The problem is dealt with under the theory of the command by using a tuned PID (proportional-integral-derivative) controller based on an extension of the Hermite-Biehler theorem applied to quasi-polynomials. This tuning approach uses Hurwitz stability concept, that is to say, a sufficient and necessary condition must be given so the roots of the quasi-polynomial lie in the left half plane. Since this stabilization method gives rise to a set of values for parameters 'P', 'I' and 'D', it turns out relevant to optimize the results achieved within this stability region. To achieve this purpose, a multi-criterion Ant Colony Optimization based on Pareto optimality is used, given the conflictual character of the closed-loop system performance parameters. The set of optimal solutions of the problem is given by determining the Pareto front of objective functions. The effectiveness of the proposed control scheme is evaluated via a series of numerical simulations in MATLAB and SIMULINK. The results are compared with those of the genetic algorithm and Ziegler-Nichols methods.

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Ant colony optimization based model for network zero-configuration

Cited by 3 publications

References 4 publications

AutoBeeConf : A swarm intelligence algorithm for MANET administration

AutoBeeConf : A swarm intelligence algorithm for MANET administration

Perspective Chapter: Enhancing Network Efficiency Using Ant Colony Optimization and Pareto Optimality for Tuning PI Controller in Congested Routers

Ant Colony Optimization based on Pareto optimality: application to a congested router controlled by PID regulation

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