Particle swarm optimization for mobile network design

El‐Saleh, Ayman A.; Ismail, Mahamod; Viknesh, R.; Mark, Ching Chuen Teck; Chan, Mun Leong

doi:10.1587/elex.6.1219

Cited by 11 publications

(11 citation statements)

References 6 publications

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“…In 1995 Eberhart and Kennedy developed a technique that uses population-based on the stochastic optimization and they get inspired by the bird flocking behavior or the behavior of fish schooling. According to the next equations of the motion can manipulate the particles [24,25]:…”

Section: Tuning Of Controller Parametersmentioning

confidence: 99%

Optimal Quantitative Controller Design for Twin Rotor MIMO System

Khreabet

Ali²

2020

ETJ

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In this paper, the control approach is used for achieving the desired performance and stability of the twin-rotor MIMO system. This system is considered one of the complex multiple inputs of multiple-output systems. The complexity because of the high nonlinearity, significant cross-coupling and parameter uncertainty makes the control of such systems is a very challenging task. The dynamic of the Twin Rotor MIMO System (TRMS) is the same as that in helicopters in many aspects. The Quantitative Feedback Theory (QFT) controller is added to the control to enhance the control algorithm and to satisfy a more desirable performance. QFT is one of the frequency domain techniques that is used to achieve a desirable robust control in presence of system parameters variation. Therefore, a combination between control and QFT is presented in this paper to give a new efficient control algorithm. On the other hand, to obtain the optimal values of the controller parameters, Particle Swarm Optimization (PSO) which is one of the powerful optimization methods is used. The results show that the proposed quantitative control can achieve more desirable performance in comparison to control especially in attenuating the cross-coupling and eliminating the steady-state error.

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Section: Tuning Of Controller Parametersmentioning

confidence: 99%

Optimal Quantitative Controller Design for Twin Rotor MIMO System

Khreabet

Ali²

2020

ETJ

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“…In multi-core 3D IC, the temperature of one core is not only determined by the program bound, but also influenced by the neighbour cores and programs, including horizontal and vertical direction [12]. This section will make an analysis of the power consumption of different benchmarks, which influence the distribution of temperature in multi-core 3D IC.…”

Section: Analysis Of Benchmarksmentioning

confidence: 99%

Thermal optimal task allocation algorithm for multi-core 3D IC with interlayer cooling system

Guo

Zhang

Yao

et al. 2016

IEICE Electron. Express

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Multi-core 3D IC is a new solution for greater quantity of cores, but with critical challenge of internal thermal. The interlayer cooling system is introduced for this problem, and expends the design space of task allocation. This work proposes a thermal optimal task allocation algorithm for multi-core 3D IC with interlayer cooling system, integrated with simulated annealing method. The results show that the maximal temperature and temperature gradient reduced by 19.4°C and 5.1°C respectively, compared to the randomized policy, and obtain better balance between normal and extreme cases than traditional ranking policy, in a design of 3 active device layers.

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“…It improves a candidate solution by iteratively trying according to measure standard [11]. The units move in the search space with 4 candidate trends:…”

Section: Optimizing Floorplanmentioning

confidence: 99%

Floorplanner for multi-core micro-processors in 3D ICs with interlayer cooling system

Guo

Zhang

Yao

2015

IEICE Electron. Express

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3D ICs is a solution for multi-core processors, with critical challenge of internal thermal problem. A new solution for this problem is interlayer cooling system, which expands the floorplan design space of micro-processors in 3D ICs. This work proposes a floorplanner for multicore processor in 3D ICs with interlayer cooling system, integrated with greedy and particle swarm optimization method. The results show that the maximal temperature and temperature gradient reduced by 10.3°C and 9.2°C respectively, compared with the baseline design in 3 active device layers.

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Particle swarm optimization for mobile network design

Cited by 11 publications

References 6 publications

Optimal Quantitative Controller Design for Twin Rotor MIMO System

Optimal Quantitative Controller Design for Twin Rotor MIMO System

Thermal optimal task allocation algorithm for multi-core 3D IC with interlayer cooling system

Floorplanner for multi-core micro-processors in 3D ICs with interlayer cooling system

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