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

Chebli, Samira; Elakkary, Ahmed; Sefiani, Nacer

doi:10.1080/21642583.2018.1509395

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…The impact of pheromone density on a certain route refers to the direction that the ant adopts, denoted by α, which is a positive value in Equation ( 4). It is noteworthy that, if α = 0, the closest node will be selected every time, whereas, if β = 0, only the chemical factor (pheromone) tracks recreate on the selection Prainetr et al (2019); Chebli et al (2018).…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

Reduction of Large Scale Linear Dynamic MIMO Systems Using ACO-PID Controller

Alkhasraji,

Shneen,

Sulttan

2024

Ing. Inv.

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The MIMO technique is an essential element in the standards of communication systems (IEEE 802.11n, IEEE 802.11ac, WiMAX, and LTE) because it helps to increase their capacity. This paper employs a model order reduction (MOR) technique with a PID controller, an ACO algorithm, and the ITAE fitness function to reduce the large-scale linearity of the MIMO technique. The numerator and denominator parameters are set by minimizing the ITAE fitness function between the transient responses of the original and the reduced model. The objectives are achieved with the PID controller and the ACO algorithm for the unit step input. The simulation results show a good system performance. The controller performance is presented with regard to the dynamic response in terms of rising time, settling time, and overshoot/undershoot. Moreover, the results of the proposed method are compared with four literature reports for validation purposes. Evaluating the parameters within the time frame and the error values with and without the PID controller and ACO algorithm allowed validating the functioning of the proposed method. Furthermore, the simulation results revealed that the proposed scheme exhibited sufficient robustness and demonstrated a reduction in the time-domain response and error values.

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Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

Reduction of Large Scale Linear Dynamic MIMO Systems Using ACO-PID Controller

Alkhasraji,

Shneen,

Sulttan

2024

Ing. Inv.

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“…The components of the fuzzy system are Bio-inspired optimization algorithms have been used for the parameter optimization problem. Accordingly, ant colony optimization is used for tuning the proportional integral derivative (PID) method [11]. Particle swarm optimization is used to optimize the fuzzy version of the PID method [19].…”

Section: Proposed Workmentioning

confidence: 99%

Fuzzy Comprehensive Random Early Detection of Router Congestion

Abu-Shareha

Al-Kasasbeh²,

Shambour³

et al. 2022

ITC

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The queue length and the load rate should be monitored to overcome the problem of router congestion due to the increase in network utilization and achieve a high-speed transmission. Previous active queue management methods manage the queued packets in the router buffer to maintain high network performance. However, these methods depend on monitoring indicators that do not cover all the congestion signs, leading to packet loss and delay. Accordingly, all the congestion signs should be wrapped into these indicators and managed by an algorithm that randomly drops packets to avoid global synchronization, loss, and delay. In this paper, a fuzzy comprehensive random early detection (FCRED) is proposed to deal with the gap in network monitoring and congestion control at the router buffer. FCRED is built by using three indicators, which monitor the router's arrival, departure, and queue length. Accordingly, a fuzzy inference process is developed to manage these indicators and calculate the dropping probability (Dp). Simulation results show that FCRED improves loss and packet dropping under various network statuses compared with RED, BLUE, and ERED. In terms of loss, FCRED achieves zero loss at high congested status. For dropping, FCRED achieves an optimal rate of 0.47 with an arrival rate of 0.95. For the throughput and delay, FCRED achieves the best results. Accordingly, the proposed FCRED method achieves zero loss and reduces packet dropping from 0.28 to 0.21, a 25% reduction compared with the best performance of these methods. Compared with recent fuzzy-based methods, the proposed FCRED achieves comparable results and outperforms them by dropping more packets to avoid loss, which in such case is necessary dropping.

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“…Parameters optimization often requires an objective function that gives quantitative description of system performance index [30]. Generally, the dynamic performance of a closed loop control system is evaluated in terms of transient response, steady state error and stability [31]. In this study, to trace the output of the system alongside a path, an objective (fitness) function is adopted to reduce the error.…”

Section: B Objective Functionsmentioning

confidence: 99%

Controller Parameters Optimization for Multi-Terminal DC Power System Using Ant Colony Optimization

et al. 2021

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Voltage source converter based multi -terminal DC grids (VSC -MTDC) are widely used for integration of renewable resources. Control of these grids requires vector control method, which includes inner and outer current controllers, tuning of these controllers is very important. Conventionally used tuning methods consider approximated linear model to tune the proportional -integral (PI) parameters of voltage source converter (VSC) which fails to produce optimum disturbance rejection results. In this research an Ant Colony Optimization (ACO) technique is used to get the optimum parameters of inner and outer power control layers for multi -terminal DC system. ACO is applied simultaneously on inner and outer control layers and results are compared with classical tuning method and well established meta -herustic technique, Particle Swarm Optimization (PSO) using MATLAB software. Dynamic models of VSC -MTDC are developed in PSCAD/EMTDC to exalt the qualities of the presented tuning method. In this paper, a four terminal VSC -MTDC has been used under different sequence of events of disturbances e.g. load change on AC side, active power reference change for inverter and rectifier mode and disconnection of one terminal to evaluate the robustness of ACO algorithm with respect to classical method. The proposed tuning method gives superior results under different disturbance compared to conventional method.

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

Cited by 4 publications

References 31 publications

Reduction of Large Scale Linear Dynamic MIMO Systems Using ACO-PID Controller

Reduction of Large Scale Linear Dynamic MIMO Systems Using ACO-PID Controller

Fuzzy Comprehensive Random Early Detection of Router Congestion

Controller Parameters Optimization for Multi-Terminal DC Power System Using Ant Colony Optimization

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