Target tracking approach via quantum genetic algorithm

Jin, Zhou; Hou, Zhiqiang; Yu, Wei; Wang, Xin

doi:10.1049/iet-cvi.2017.0176

Cited by 15 publications

(9 citation statements)

References 51 publications

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“…, m is the quantum number, and n is the population size. In addition, the normalization condition (as shown in equation 3) needs to be satisfied [13].…”

Section: ) Quantum Bit Codingmentioning

confidence: 99%

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Application of Quantum Genetic Optimization of LVQ Neural Network in Smart City Traffic Network Prediction

Zhang

Wang

et al. 2020

IEEE Access

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Accurate prediction of traffic flow in urban networks is of great significance for smart city management. A short-term traffic flow prediction algorithm of Quantum Genetic Algorithm-Learning Vector Quantization (QGA-LVQ) neural network is proposed to forecast the changes of traffic flow. Different from BP neural network, Learning Vector Quantization (LVQ) neural network is of simple structure, easy implementation and better clustering effect. Utilizing the global optimization ability of Quantum Genetic Algorithm (QGA), it is combined with LVQ neural network to overcome some shortcomings of LVQ neural network, including sensitive to initial weights and prone to local minima. In order to test the convergence ability and the timeliness of QGA-LVQ neural network in short-term traffic flow, some contrast experiments are performed. Experimental simulation results show that, QGA-LVQ neural network obtains excellent prediction results in prediction accuracy and convergence speed. Besides, compared with GA-BP neural network and wavelet neural network, QGA-LVQ neural network performs better in short-term traffic flow prediction. INDEX TERMS QGA, LVQ neural network, short-term traffic flow prediction, global optimization.

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“…, m is the quantum number, and n is the population size. In addition, the normalization condition (as shown in equation 3) needs to be satisfied [13].…”

Section: ) Quantum Bit Codingmentioning

confidence: 99%

“…Then, the fitness of the individual is calculated as equation (12). Step3: The termination condition of the iterative calculation is shown in equation (13).…”

Section: A Qga-lvq Neural Networkmentioning

confidence: 99%

Application of Quantum Genetic Optimization of LVQ Neural Network in Smart City Traffic Network Prediction

Zhang

Wang

et al. 2020

IEEE Access

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“…The genetic algorithm (GA) [43] is a kind of method to deal with complex optimization problems by simulating the rules of survival of the fittest and the mechanism of chromosome information exchange within the population. The quantum genetic algorithm (QGA) [44][45][46][47] is based on the state vector representation of quantum. It refers the probability amplitude representation of quantum bits to the coding of chromosomes, so that a chromosome can express the superposition of multiple states, and uses quantum revolving gate and quantum non-gate to realize the finer operation of chromosomes, thus achieving the optimal solution of the goal.…”

Section: Quantum Evolutionary Algorithmmentioning

confidence: 99%

Optimal Allocation of Intermittent Distributed Generation under Active Management

et al. 2018

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In recent years, distributed generation (DG) has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. At present, most distributed generators follow the principle of “installation is forgetting” after they are connected to a distribution network. This principle limits the popularization and benefit of distributed generation to a great extent. In order to solve these problems, this paper presents a two-tier model for optimal allocation of distributed power sources in active distribution networks (ADN). The objective of upper level planning is to minimize the annual comprehensive cost of distribution networks, and the objective of lower level planning is to minimize the active power cut-off of distributed generation through active management mode. Taking into account the time series characteristics of load and distributed power output, the improved K-means clustering method is used to cluster wind power and the photovoltaic output in different scenarios to get the daily curves in typical scenarios, and a bilevel programming model of distributed generation based on multiscenario analysis is established under active management mode. The upper level programming model is solved by Quantum genetic algorithm (QGA), and the lower level programming model is solved by the primal dual interior point method (PDIPM). The rationality of the model and the effectiveness of the algorithm are verified by simulation and analysis of a 33-bus distribution network.

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“…The terminal resistance is the optimization parameter. There are series of optimization algorithms that have been put forward to solve this type of optimization problem, such as Genetic algorithm [9,10], Ant Colony Algorithm [11,12], Simulated Annealing Algorithm [13,14], Particle Swarm Optimization [15,16], etc. Particle Swarm Optimization is a spatial search algorithm that solves the optimization problem and is very suitable for solving multidimensional problems.…”

Section: Introductionmentioning

confidence: 99%

A Particle Swarm Optimization for Terminal Resistor Matching

Nan¹,

Jiang²,

Xu³

2019

dteees

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The CAN bus is widely used in the industrial field and its terminal resistance has an important influence on the bus signal. In this paper, a particle swarm optimization based on the joint simulation of Saber and MATLAB is proposed to address this problem. First, we transform the terminal resistor matching problem into a minimisation multi-objective optimization problem and formulate the objective function by considering three objectives: bus voltage, rise time and overshoot. Then, we establish a network topology model of CAN bus in Saber, and combine saber and MATLAB. Finally, virtual simulation and physical verification are conducted and the results demonstrate that the optimized terminating resistor has a significant improvement on the signal quality of the bus, and algorithm has engineering applicability.

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Target tracking approach via quantum genetic algorithm

Cited by 15 publications

References 51 publications

Application of Quantum Genetic Optimization of LVQ Neural Network in Smart City Traffic Network Prediction

Application of Quantum Genetic Optimization of LVQ Neural Network in Smart City Traffic Network Prediction

Optimal Allocation of Intermittent Distributed Generation under Active Management

A Particle Swarm Optimization for Terminal Resistor Matching

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