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.
Structural topology optimization methods that are driven by the geometric features of components, such as the Moving Morphable Component (MMC), are widely explored due to their convenient interaction with design softw...
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Structural topology optimization methods that are driven by the geometric features of components, such as the Moving Morphable Component (MMC), are widely explored due to their convenient interaction with design software. However, these methods exhibit significant sensitivity to the initial positioning of components, limiting their suitability for applications involving complex geometric boundaries. This study addresses these challenges by introducing a novel dual-layer optimization framework that employs a quantum computing-based intelligent optimizationalgorithm, quantum-behavedparticleswarmoptimization (QPSO). The potential drawbacks of the MMC method in engineering applications, particularly its sensitivity to initial conditions, are critically examined, leading to the proposal of a global-gradient hybrid framework for geometry feature-driven topology optimization. This proposed method demonstrates superior capability in optimizing material arrangements compared to the original MMC framework and enhances engineering applicability, making it more suitable for real-world applications. Through three representative examples, the limitations of the original MMC method are illustrated, and the advantages of the proposed dual-layer framework are highlighted. The results indicate that this method not only overcomes sensitivity issues but also stably identifies superior configurations, particularly for structures with complex geometric boundaries, providing models that facilitate interaction with CAD systems. This method offers a robust and precise approach for optimizing designs in various engineering fields.
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.
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.
Aiming at the shortcomings of quantum-behaved particle swarm optimization algorithm (QPSO), an improved quantum-behaved particle swarm optimization algorithm (IQPSO) is put forward, and the improved algorithm is appli...
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Aiming at the shortcomings of quantum-behaved particle swarm optimization algorithm (QPSO), an improved quantum-behaved particle swarm optimization algorithm (IQPSO) is put forward, and the improved algorithm is applied in solving a kind of fuzzy portfolio selection problems. Firstly, a kind of portfolio models with fuzzy return rates and background risk is established followed by some necessary preparations of fuzzy theory. Then, in the improved algorithm, hybrid probability distribution strategy and contraction-expansion coefficient with nonlinear structure are chosen to enhance particle's exploration ability, and premature prevention mechanism is used to maintain population diversity. Furthermore, the experimental results on 16 benchmark functions show that IQPSO has better convergence and robustness than PSO with inertia weight, QPSO and QPSO with a hybrid probability distribution in most cases. Finally, when solving a fuzzy portfolio model, IQPSO provides comparable and superior results compared with the other metaheuristics.
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 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 solve constrained portfolio selection model effectively, an improved quantum-behaved particle swarm optimization algorithm(LQPSO) is presented. Firstly, considering its practicality in real dealing process, a class...
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To solve constrained portfolio selection model effectively, an improved quantum-behaved particle swarm optimization algorithm(LQPSO) is presented. Firstly, considering its practicality in real dealing process, a class of fuzzy portfolio models with transaction costs and background risk is established. Then in the design of improved algorithm, Levy flight strategy and contraction-expansion coefficient with nonlinear structure are taken into account for enhancing particle's exploration ability, and premature prevention mechanism is used to increase population diversity. According to the following performance test, LQPSO demonstrates better convergence and robustness than PSO with inertia weight, QPSO and QPSO with a hybrid probability distribution in 12 benchmark functions. Furthermore, experimental results indicate that LQPSO outperforms several metaheuristics when seeking optimal solution for the fuzzy portfolio model with constraints. (C) 2020 Elsevier B.V. All rights reserved.
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