Implementing Quantum-Behaved Particle Swarm Optimization Algorithm in FPGA for Embedded Real-Time Applications

Chai, Zhilei; Sun, Jun; Cai, Rui; Xu, Wenbo

doi:10.1109/iccit.2009.21

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…QPSO is inspired by the theory of particle swarm optimization (PSO) and quantum mechanics and is a probabilistic optimization algorithm. In QPSO, the particle is rendered by Schrodinger wave equation [20] i.e. | ψ (x, t) | 2 , in lieu of position and velocity in PSO.…”

Section: Qpso -Quantum Behaved Particle Swarm Optimizationmentioning

confidence: 99%

Resource Allocation Strategy for D2D-Assisted Edge Computing System Using Weighted Genetic and Ant Colony Optimization Algorithms

Teja¹,

Nikhileswar²,

Nandakumar³

2021

Preprint

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Due to the constrained battery ability and computing functionality of cellularcustomers, the Resource allocation approach in D2D-assisted edge computingsystem with hybrid electricityharvesting is investigated on thisdocument.By using magnetic induction-primarily based on wireless reverse charging technology, cellular consumer can complement more electricity from nearby users whilst the electricity gathered from the surrounding radio frequency is set to be exhausted. Due to the constrained computing resource of MEC server the MEC server reaches the limit of its computing capability the adjacent Base station’s user can act as a relay node and by setting up the D2D relay links the computing responsibilities of the users which are left under previous base station can now be transferred to new base station’s MEC server which has enough resources. The goal of the resource allocation approach is to improvise the energy efficiency under computation delayconstraint and energy harvestingconstraints.An optimal answer is produced by adopting the Quantum behaved Particle Swarm Optimization (QPSO) algorithm, weighted genetic algorithm (WGA/GA) and Ant-colony optimization (ACO) algorithm, Simulation outcomes display that overall performance of theapproach that is proposed is superior than different benchmark strategies, and weighted genetic algorithm (WGA), Ant-colony optimization (ACO) algorithmcan attain better energy efficiency than the quantum behaved particle swarm optimization and standard particle swarm optimization algorithm also Simulation studiesshows that the self-learning nature of these methods(i.e WGA and ACO) gives better results even for higher complexity problems.

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Section: Qpso -Quantum Behaved Particle Swarm Optimizationmentioning

confidence: 99%

Resource Allocation Strategy for D2D-Assisted Edge Computing System Using Weighted Genetic and Ant Colony Optimization Algorithms

Teja¹,

Nikhileswar²,

Nandakumar³

2021

Preprint

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“…Formula (4), ( 8) and (10) make up the iterative equations of Quantum Behaved Particle Swarm Optimization. Quantum Particle Swarm function show its superiority in test (Clerc, 2004), filter design (Chen, Sun & Ding, 2008), a multi-stage financial planning (Chai, Sun, Cai et al, 2009), neural network optimization (Omkar, Khandelwal, Ananth et al, 2009) and H ∞ control (Goh, Tan, Liu et al, 2010) and other applications.…”

Section: Quantum Particle Swarm Optimization (Qpso)mentioning

confidence: 99%

“…Sun et al had studied the intelligent population evolution in-depth based on the analysis of particle swarm optimization algorithm, quantum theory was introduced into a PSO algorithm, quantum-behaved particle swarm optimization algorithm was proposed with a global search capability (Quantum-behaved Particle Swarm Optimization, QPSO) . Since the proposed PSO algorithm, it has the simple calculation program, and it is easy to implement, and there are less control parameters, etc., it caused research and attention of many scholars in related fields at home and abroad (Goh, Tan, Liu et al, 2010;Omranpour, Ebadzadeh, Shirt et al, 2012), but also it has been applied to some practical problems (Chen, Sun & Ding, 2008;Chai, Sun, Cai et al, 2009;Omkar, Khandelwal, Ananth et al, 2009). QPSO algorithm has only parameter (contraction expansion factor), Sun et al, used a fixed parameter control strategy , later Fang proposed increases evolutionary numbers, the linear or nonlinear decreasing parameter control method were used, the simulation results showed that good improvement effect are achieved in most of the test functions (Fang, 2008).…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization Research Base on Quantum Q-Learning Behavior

2017

Journal of Information Technology Research

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Quantum-behaved Particle Swarm Optimization algorithm is analyzed, contraction-expansion coefficient and its control method are studied. To the different performance characteristics with different coefficients control strategies, a control method of coefficient with Q-learning is proposed. The proposed method can tune the coefficient adaptively, and the whole optimization performance is increased. The comparison and analysis of results with the proposed method, constant coefficient control method, linear decreased coefficient control method and non-linear decreased coefficient control method is given based on CEC 2005 benchmark function.

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