quantum-behaved particle swarm optimization algorithm is firstly used in economic load dispatch of power system in this paper. quantum-behaved particle swarm optimization algorithm is the integration of particleswarm...
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quantum-behaved particle swarm optimization algorithm is firstly used in economic load dispatch of power system in this paper. quantum-behaved particle swarm optimization algorithm is the integration of particleswarmoptimizationalgorithm and quantum computing theory The superposition characteristic and probability representation of quantum methodology are combined into particleswarmoptimizationalgorithm This can make a single particle be expressed by several certain probability states. And the quantum rotation gates are used to realize update operation of particles. The algorithm is simulated by two cases, which validates it can effectively solve economic load dispatch problem. Though performance comparison, it is obvious the solution is superior to that of improved particleswarmoptimizationalgorithm and other optimizationalgorithms. (C) 2009 Elsevier Ltd. All rights reserved.
In the paper, a new method for designing Cosine Modulated Filter Banks (CMFB) is proposed, the prototype filter is designed by optimization method. Firstly, the method appropriately relaxes the limit conditions fo...
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In the paper, a new method for designing Cosine Modulated Filter Banks (CMFB) is proposed, the prototype filter is designed by optimization method. Firstly, the method appropriately relaxes the limit conditions for perfect reconstruction, the design problem of CMFB is formulated as a nonlinear and unconstrained optimization of an objective function, which stopband cutoff frequency is fixed, passband cutoff frequency is adjusted to minimize the cost function which satisfies reconstruction condition, and directly designs the prototype filter. The simulation results illustrate the proposed method and its improvement over other methods in terms of amplitude distortion, aliasing distortion, signal to noise ratio and reconstruction performance.
In this paper, a Two Sub-swarms quantum-behaved particle swarm optimization algorithm Based on Exchange Strategy (TS-QPSO) is proposed. Two sub-swarms of particles with quantum Behavior are set up in TS-QPSO. Once the...
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ISBN:
(纸本)9780769540207
In this paper, a Two Sub-swarms quantum-behaved particle swarm optimization algorithm Based on Exchange Strategy (TS-QPSO) is proposed. Two sub-swarms of particles with quantum Behavior are set up in TS-QPSO. Once the whole swarm falls into local optima and the best value of the global swarm is not improved after the allowable iterations, the exchange strategy will be carried out. The amount of exchange particles is different in each searching phase. In this way, the population diversity can be improved greatly and the problem that falling into local optima can be avoided effectively. Experiment results show that the overall performance of TS-QPSO is superior to QPSO algorithm and TSPSO algorithm.
This paper proposed a method to fuse multimodal medical images using the adaptive pulse-coupled neural networks (PCNN), which was optimized by the quantum-behavedparticleswarmoptimization ( QPSO) algorithm. In this...
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This paper proposed a method to fuse multimodal medical images using the adaptive pulse-coupled neural networks (PCNN), which was optimized by the quantum-behavedparticleswarmoptimization ( QPSO) algorithm. In this fusion model, two source images, A and B, were first processed by the QPSO-PCNN model, respectively. Through the QPSO algorithm, the PCNN model could find the optimal parameters for the source images, A and B. To improve the efficiency and quality of QPSO, three evaluation criteria, image entropy (EN), average gradient (AG) and spatial frequency (SF) were selected as the hybrid fitness function. Then, the output of the fusion model was obtained by the judgment factor according to the firing maps of two source images, which maybe was the pixel value of the image A, or that of the image B, or the tradeoff value of them. Based on the output of the fusion model, the fused image was gained. Finally, we used five pairs of multimodal medical images as experimental data to test and verify the proposed method. Furthermore, the mutual information (MI), structural similarity (SSIM), image entropy (EN), etc. were used to judge the performances of different methods. The experimental results illustrated that the proposed method exhibited better performances. (C) 2016 Elsevier B.V. All rights reserved.
Susceptibility analysis is important in any study of debris flows. Unlike debris flows in Southwest China, debris flows in Northern China occur with different characteristics and much lower frequency. However, neglect...
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Susceptibility analysis is important in any study of debris flows. Unlike debris flows in Southwest China, debris flows in Northern China occur with different characteristics and much lower frequency. However, neglecting the danger of possible debris flows in this northern area may result in a devastating disaster. In this paper, 12 debris flow catchments located near Zhirui town, in Heshigten Banner, Inner Mongolia, China, were investigated. A geographic information system, a global positioning system, and remote sensing were used to determine geological, topographical, morphological, and vegetation factors of influence. Principal component analysis was carried out to convert this set of possibly correlated factors into a set of values of linearly uncorrelated principal components. The accumulative contribution rate of the first five principal components retained most of the information on these factors, accounting for 90.9 %. An improved fuzzy C-means clustering analysis was applied to determine the susceptibility of debris flows in this area. This method is based on a quantum-behaved particle swarm optimization algorithm, which is an evolutionary algorithm that can achieve global optimization, and is not sensitive to the initial cluster centers. Results showed that the susceptibility levels for four of the debris flow catchments were high, six were moderate, and two were low. Our quantitative assessments based on these nonlinear methods were consistent with field investigations.
In dealing with the problem of the quantum-behaved particle swarm optimization algorithm （QPSO） easy falling into the local optima,we proposed the diversity guided immune clonal *** this algorithm the swarm was defi...
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In dealing with the problem of the quantum-behaved particle swarm optimization algorithm （QPSO） easy falling into the local optima,we proposed the diversity guided immune clonal *** this algorithm the swarm was defined two states: attraction and *** the optimization process the swarm transferred between the two states repeatedly reference to the swarm *** in the attraction state if the diversity is less than the pre-established value,we will carry the immune clonal algorithm to do the local *** we used this algorithm with wavelet to forecast the foundation settlement,and also made a compare with standard quantum-behavedparticleswarmoptimization with *** experiment indicated that this improved method had a better ability of searching global and local optima and high forecasting precision.
In this paper, we use the distributed compressed sensing to deal with video coding. To reduce the orthogonal matching pursuit algorithm computational complexity, we use the quantum-behavedparticleswarmoptimization ...
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In this paper, we use the distributed compressed sensing to deal with video coding. To reduce the orthogonal matching pursuit algorithm computational complexity, we use the quantum-behaved particle swarm optimization algorithm to reconstruct video signal. In this paper, we analyze the computational complexity of two algorithms, simulation results demonstrate that quantum-behaved particle swarm optimization algorithm greatly reduce the amount of computation. And it has better stability and faster convergence to deal with original video signal. Simulation results demonstrate that we can obtain the better reconstructed video with low sample value, and it can guarantee safety performance. Copyright (c) 2013 John Wiley & Sons, Ltd.
To overcome the shortage of standard particleswarmoptimization(SPSO) on premature convergence, quantum-behavedparticleswarmoptimization (QPSO) is presented to solve engineering constrained optimization problem. Q...
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ISBN:
(纸本)9783037852958
To overcome the shortage of standard particleswarmoptimization(SPSO) on premature convergence, quantum-behavedparticleswarmoptimization (QPSO) is presented to solve engineering constrained optimization problem. QPSO algorithm is a novel PSO algorithm model in terms of quantum mechanics. The model is based on Delta potential, and we think the particle has the behavior of quanta. Because the particle doesn't have a certain trajectory, it has more randomicity than the particle which has fixed path in PSO, thus the QPSO more easily escapes from local optima, and has more capability to seek the global optimal solution. In the period of iterative optimization, outside point method is used to deal with those particles that violate the constraints. Furthermore, compared with other intelligent algorithms, the QPSO is verified by two instances of engineering constrained optimization, experimental results indicate that the algorithm performs better in terms of accuracy and robustness.
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