This paper presents a comprehensive review of the current research on spatial sensor networks and the node deployment methods employed for mobile target tracking. The study introduces particleswarmoptimization (PSO)...
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This paper presents a comprehensive review of the current research on spatial sensor networks and the node deployment methods employed for mobile target tracking. The study introduces particleswarmoptimization (PSO) and its quantum behavior extension, detailing concepts such as the quantum state wave function and particle position representation. Subsequently, an improved quantumparticleswarmoptimization (QPSO) algorithm is proposed. This enhanced algorithm increases population diversity by incorporating quantum rotation gates and quantum mutation mechanisms, expands the search space through the superposition state and interference principles of quantum mechanics, and dynamically adjusts algorithm parameters to balance global exploration and local search. These modifications aim to improve both the convergence speed and accuracy of the algorithm. Simulation results demonstrate that the improved QPSO algorithm surpasses traditional mobile tracking deployment algorithms and the standard quantum behavior particleswarmoptimizationalgorithm in terms of target tracking deployment within spatial sensor networks. Notably, it significantly enhances the tracking success rate and reduces tracking errors.
quantumparticleswarmalgorithm integrated the quantum behavior with particleswarmoptimizationalgorithm, is used to settle the majorization question of calculating available transmission capability. And by using t...
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ISBN:
(数字)9781728197241
ISBN:
(纸本)9781728197241
quantumparticleswarmalgorithm integrated the quantum behavior with particleswarmoptimizationalgorithm, is used to settle the majorization question of calculating available transmission capability. And by using the software of Matlab to IEEE-30 bus system as an example of the simulation, after comparing the simulation results with the traditional particleswarmoptimizationalgorithm results, we dissected the optimization performance and convergence speed of the above two algorithms, and verify the effectiveness of quantumparticleswarmalgorithm to settle the majorization question of the available transmission capability.
A assembled method based on quantumparticleswarmoptimization (QPSO) algorithm combined with Bayesian networks (BN) is proposed to solve complex multi-objective nutritional diet decision making problem. To realize n...
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ISBN:
(纸本)9781424445189
A assembled method based on quantumparticleswarmoptimization (QPSO) algorithm combined with Bayesian networks (BN) is proposed to solve complex multi-objective nutritional diet decision making problem. To realize nutritional diet decision optimization for patients, BN model for dealing with associative relationship between diseases and diets is set up to compute and update the edibility of every food in database. QPSO algorithm is selected as the core optimizationalgorithm to avoid being trapped in a local optimum. Actual experimental results show that such combined method is a feasible and effective approach for actual nutritional diet decision making problem.
Through the analysis of the typical CCHP structure and the consideration of the daily operation cost and environmental cost of the micro energy network, the wind-natural-storage combined synchronous generation model i...
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ISBN:
(纸本)9781728113128
Through the analysis of the typical CCHP structure and the consideration of the daily operation cost and environmental cost of the micro energy network, the wind-natural-storage combined synchronous generation model including electric, flue gas, air and hot water as the basic busbar is established. The optimization results of cold, heat and electric load scheduling of micro energy network in different scenarios are analyzed. By using the quantum particle swarm optimization algorithm (QPSO) to chaotic search, neighborhood mutation and weighted updating of the optimal position center of the population, the particles are effectively prevented from falling into the local optimal solution too early when searching, thus improving the population quality and convergence speed. The analysis results show that the proposed algorithm can effectively solve the CCHP model, reduce the operating cost and environmental cost under the reliable operation of the load demand and micro-energy network, improve the utilization rate of renew able energy and realize the economic dispatch of the micro energy network.
In clustering, in order to find a better data clustering center, make the algorithm convergence faster and clustering results more accurate, a k-means clustering algorithm based on improved quantumparticleswarm opti...
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ISBN:
(纸本)9781665412544
In clustering, in order to find a better data clustering center, make the algorithm convergence faster and clustering results more accurate, a k-means clustering algorithm based on improved quantum particle swarm optimization algorithm is proposed. In this algorithm, the cluster center is simulated as a particle. Cloning and mutation operations are used to increase the diversity and improve the global search ability of QPSO. A suitable and stable cluster center is obtained. Finally, an effective clustering result is obtained. The algorithm is tested with UCI data set. The results show that the improved algorithm not only ensures the global convergence of the algorithm, but also obtains more accurate clustering results.
Non-intrusive load monitoring (NILM) can provide rich power consumption data for home users and power supply companies, which is helpful to use the electricity in an efficient way. This paper proposes a novel NILM met...
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ISBN:
(纸本)9781728121659
Non-intrusive load monitoring (NILM) can provide rich power consumption data for home users and power supply companies, which is helpful to use the electricity in an efficient way. This paper proposes a novel NILM method based on quantumparticleswarmoptimization (QPSO) algorithm. First, the parameters which can represent the load characteristics of household appliances are presented. Then, the detailed NILM method based on QPSO algorithm is described. At last, experiments are carried out on a house with 8 kinds of appliances. Results show that the proposed NILM method is effective, and QPSO algorithm performs better than that of basic PSO algorithm.
Structural damage recognition is always the concerned focus in many fields like aerospace, petroleum and petrochemical industry, industrial production and civil life. For damage recognition in complex structure or str...
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Structural damage recognition is always the concerned focus in many fields like aerospace, petroleum and petrochemical industry, industrial production and civil life. For damage recognition in complex structure or structural interior, especially somewhere sensors can't go, minor damage is often hard identified by not only traditional nondestructive testing methods like ultrasonic testing, radiographic testing, magnetic particle testing, penetrant testing, eddy current testing, but also the current popular ultrasonic guided wave based on the piezoelectric wafer, electromagnetic acoustic transducer or magnetostrictive sensor, which is mainly because the response signals are always affected by many structural features. In this article, the advanced global search algorithm, quantum particle swarm optimization algorithm is first combined with the finite element method to accurately recognize the structural damage based on the conductance-frequency spectrum resulted from electromechanical impedance method. Meanwhile, the objective function is designed to compare the difference of peak frequency variations in the experiment and finite element calculation respectively. By adopting the stiffness reduction method of the elements near the structural damage, the identification efficiency is largely improved for no need to repeatedly partition the model grid. And after multiple iteration optimization of the artificial intelligence algorithm - quantum particle swarm optimization algorithm, the identification error of damage parameters including location and degree can be reduced to below 4 percent. Therefore, the combination of finite element method and quantum particle swarm optimization algorithm is quite effective for guaranteeing high accuracy and efficiency for damage parameters' recognition in complex structures.
The fine interpretation and inversion of transient electromagnetic method measurement data have the problems of nonlinearity, multi-solution, and ill condition. However, the conventional particleswarmoptimization (P...
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The fine interpretation and inversion of transient electromagnetic method measurement data have the problems of nonlinearity, multi-solution, and ill condition. However, the conventional particleswarmoptimization (PSO) nonlinear inversion methods suffer from prematurity, slow convergence, and low calculation accuracy. To solve these problems, a quantum PSO (QPSO) algorithm based on the elite opposition-based learning (EOL) strategy is proposed. Firstly, three performances tests of the EOL-QPSO algorithm are carried out with Peaks, Schaffer and Rastrigin functions. The results show that the EOL-QPSO algorithm has excellent solution accuracy, efficient calculation speed and balanced exploitation and exploration capability. Secondly, the conventional PSO algo-rithm and the EOL-QPSO algorithm are used to compare the inversion of the theoretical model and the synthetic data with noise, and combined with Bayesian method, the posterior model probability statistics of the synthetic data are carried out. The research shows that the EOL-QPSO inversion algorithm is improved in terms of calculation accuracy, calculation efficiency, anti-noise performance and exploitation and exploration capability, and it can accurately obtain the posterior estimates of the real model. Finally, the inversion of field-measured data demonstrates that the EOL-PSO inversion method accurately reflects the position of the water -accumulated goaf.
A assembled method based on quantumparticleswarmoptimization(QPSO) algorithm combined with Bayesian networks(BN) is proposed to solve complex multiobjective nutritional diet decision making *** realize nutritional ...
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A assembled method based on quantumparticleswarmoptimization(QPSO) algorithm combined with Bayesian networks(BN) is proposed to solve complex multiobjective nutritional diet decision making *** realize nutritional diet decision optimization for patients,BN model for dealing with associative relationship between diseases and diets is set up to compute and update the edibility of every food in database,QPSO algorithm is selected as the core optimizationalgorithm to avoid being trapped in a local optimum,Actual experimental results show that such combined method is a feasible and effective approach for actual nutritional diet decision making problem.
A assembled method based on quantumparticleswarmoptimization (QPSO) algorithm combined with Bayesian networks (BN) is proposed to solve complex multiobjective nutritional diet decision making problem. To realize nu...
详细信息
A assembled method based on quantumparticleswarmoptimization (QPSO) algorithm combined with Bayesian networks (BN) is proposed to solve complex multiobjective nutritional diet decision making problem. To realize nutritional diet decision optimization for patients, BN model for dealing with associative relationship between diseases and diets is set up to compute and update the edibility of every food in database. QPSO algorithm is selected as the core optimizationalgorithm to avoid being trapped in a local optimum. Actual experimental results show that such combined method is a feasible and effective approach for actual nutritional diet decision making problem.
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