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Atmospheric refractivity estimation from AIS signal power using the quantum-behaved particle swarm optimization algorithm

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OPEN GEOSCIENCES 2019年第1期11卷 542-548页

作者： Tang, Wenlong Cha, Hao Wei, Min Tian, Bin Ren, Xichuang Naval Univ Engn Coll Elect Engn Wuhan 430033 Peoples R China PLA Univ Unit 31003 Beijing 100191 Peoples R China PLA Univ Unit 91469 Beijing 100841 Peoples R China

This paper proposes a new refractivity profile estimation method based on the use of AIS signal power and quantum-behaved particle swarm optimization (QPSO) algorithm to solve the inverse problem. Automatic identification system (AIS) is a maritime navigation safety communication system that operates in the very high frequency mobile band and was developed primarily for collision avoidance. Since AIS is a one-way communication system which does not need to consider the target echo signal, it can estimate the atmospheric refractivity profile more accurately. Estimating atmospheric refractivity profiles from AIS signal power is a complex nonlinear optimization problem, the QPSO algorithm is adopted to search for the optimal solution from various refractivity parameters, and the inversion results are compared with those of the particle swarm optimization algorithm to validate the superiority of the QPSO algorithm. In order to test the anti-noise ability of the QPSO algorithm, the synthetic AIS signal power with different Gaussian noise levels is utilized to invert the surface-based duct. Simulation results indicate that the QPSO algorithm can invert the surface-based duct using AIS signal power accurately, which verify the feasibility of the new atmospheric refractivity estimation method based on the automatic identification system.

关键词： refractivity estimation automatic identification system surface-based duct quantum-behaved particle swarm optimization algorithm

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Identification of Uncertain Incommensurate Fractional-Order Chaotic Systems Using an Improved quantum-behaved particle swarm optimization algorithm

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JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS 2018年第5期13卷 051004-051004页

作者： Wei, Jiamin Yu, Yongguang Cai, Di Beijing Jiaotong Univ Dept Math Beijing 100044 Peoples R China

This paper is concerned with a significant issue in the research of nonlinear science, i.e., parameter identification of uncertain incommensurate fractional-order chaotic systems, which can be essentially formulated as a multidimensional optimization problem. Motivated by the basic particle swarm optimization and quantum mechanics theories, an improved quantum-behaved particle swarm optimization (IQPSO) algorithm is proposed to tackle this complex optimization problem. In this work, both systematic parameters and fractional derivative orders are regarded as independent unknown parameters to be identified. Numerical simulations are conducted to identify two typical incommensurate fractional-order chaotic systems. Simulation results and comparisons analyses demonstrate that the proposed method is suitable for parameter identification with advantages of high effectiveness and efficiency. Moreover, we also, respectively, investigate the effect of systematic parameters, fractional derivative orders, and additional noise on the optimization performances. The corresponding results further validate the superior searching capabilities of the proposed algorithm.

关键词： parameter identification incommensurate fractional-order chaotic systems quantum-behaved particle swarm optimization algorithm nonlinear optimization

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3D Multi-Modality Medical Image Registration based on quantum-behaved particle swarm optimization algorithm 14

3D Multi-Modality Medical Image Registration based on Quantu...

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International Symposium Distributed Computing and Applications Business Engineering and Science

作者： Li Hui Zhu Zhijun Jiangnan Univ Network Informat & Operat Ctr Wuxi Peoples R China Jiangnan Univ Arch Wuxi Peoples R China

ISBN: (纸本)9781467365932

Image registration based on mutual information is of high accuracy and robustness. Unfortunately, the mutual information function is generally not a smooth function but one containing many local maxima, which has a large influence on optimization. This paper proposes a registration method based on quantum-behaved particle swarm optimization algorithm. Not only QPSO has less parameters to con-troll, but also does its sampling space at each iteration covers the whole solution space. Thus QPSO can find the best solution quickly and guarantee to be global convergent. Experiments shows that this registration method could efficiently restrain local maxima of mutual information function and it can improve accuracy. Compare with the gold standard, the subvoxel accuracy can be achieved.

关键词： component mage registration Mutual information quantum-behaved particle swarm optimization algorithm

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3D Multi-Modality Medical Image Registration based on quantum-behaved particle swarm optimization algorithm

3D Multi-Modality Medical Image Registration based on Quantu...

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第十四届分布式计算及其应用国际学术研讨会

作者： Lihui Zhu Zhijun Jiangnan university Network Information&Operation Center Jiangnan university Archives

Image registration based on mutual information is of high accuracy and ***, the mutual information function is generally not a smooth function but one containing many local maxima, which has a large influence on optimization. This paper proposes a registration method based on quantum-behaved particle swarm optimization algorithm. Not only QPSO has less parameters to con-troll, but also does its sampling space at each iteration covers the whole solution *** QPSO can find the best solution quickly and guarantee to be global convergent. Experiments shows that this registration method could efficiently restrain local maxima of mutual information function and it can improve *** with the gold standard, the subvoxel accuracy can be achieved.

关键词： component mage registration Mutual information quantum-behaved particle swarm optimization algorithm

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Simultaneous Synthesis Of Heat Exchanger Network With Stream Split Based On quantum-behaved particle swarm optimization algorithm 33

Simultaneous Synthesis Of Heat Exchanger Network With Stream...

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33rd Chinese Control Conference (CCC)

作者： Ye Zhen Cheng Qian Zhi Yuan Luo Na Zhao Liang E China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

ISBN: (纸本)9781479947249

Simultaneous synthesis problem of heat exchanger network is formulated as a mixed-integer nonlinear programming model. Limited by the Characteristic of the nonlinearity, the convexity and the discontinuity of the model, the classical optimization algorithms which is used to solving the model are easy to fall into local minima. Based on the superstructure model with non-isothermal mixing of split stream, a two-level approach combined with quantum-behaved particle swarm optimization (QPSO) algorithm is proposed to find the optimum structure with a minimum annual cost. In the upper level, QPSO is utilized to generate the structure of the network, while in the lower level, split-stream fractions and heat load of exchangers are optimized by QPSO. Benchmark problems are solved and results show that the two level quantum particle swarm algorithm effectively avoids falling into local minima and it is feasible and effective for heat exchanger network problems.

关键词： Heat Exchanger Network Synthesis quantum-behaved particle swarm optimization algorithm Mixed-integer Nonlinear Programming

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quantum-behaved particle swarm optimization algorithm with Adaptive Mutation Based on q-Gaussian Distribution

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CHINESE JOURNAL OF ELECTRONICS 2012年第3期21卷 449-452页

作者： Zhao Wei San Ye Harbin Inst Technol Control & Simulat Ctr Harbin 150001 Peoples R China

Aiming at the drawback of being easily trapped into the local optima and premature convergence in quantum-behaved particle swarm optimization algorithm, quantum-behaved particle swarm optimization algorithm with adaptive mutation based on q-Gaussian distribution is proposed. q-Gaussian mutation operator is applied to the mean best position of particles to overcome the drawback of premature convergence caused by loss of diversity in the population. In the evolution of population, adaptive adjustment of the nonextensive entropic index q balances exploration and exploitation. The simulation results of testing four standard benchmark functions and traveling salesman problem show that quantum-behaved particle swarm optimization algorithm with adaptive mutation based on q-Gaussian distribution has best optimization performance and robustness.

关键词： quantum-behaved particle swarm optimization algorithm Adaptive mutation q-Gaussian distribution

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quantum computing intelligence algorithm for structural topology optimization

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APPLIED MATHEMATICAL MODELLING 2025年 137卷

作者： Wang, Zhenghuan Wang, Xiaojun Beihang Univ Inst Solid Mech Sch Aeronaut Sci & Engn Beijing 100083 Peoples R China

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 optimization algorithm, quantum-behaved particle swarm optimization (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.

关键词： Structural topology optimization Geometry feature driven quantum-behaved particle swarm optimization algorithm Wing rib design Unmanned aerial vehicle design

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Localization Technology Based on quantum-behaved particle swarm optimization algorithm for Wireless Sensor Network

Localization Technology Based on Quantum-behaved Particle Sw...

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2nd International Conference on Advanced Design and Manufacturing Engineering (ADME 2012)

作者： Zhao, Ji Fu, Yi Wang, Han-bo Wuxi City Coll Vocat Technol Dept Elect Informat Engn Wuxi 214000 Jiangsu Peoples R China

ISBN: (纸本)9783037855034

This paper proposed a distributed iterative localization technology of wireless sensor networks (WSNs) to solve the problem of node localization. In this approch, once the nodes get localized, they act as references for the rest of nodes to localize. The ranging-based localization problem is formulated as a multidimensional optimization issue, and the quantum-behaved particle swarm optimization algorithm (QPSO) is used to exploit their quick convergence to quality solutions. Finally, the simulation results compared with the particle swarm optimization algorithm (PSO) algorithm show that QPSO outperforms the PSO and improve the node position accuracy, which prove the validity of the presented method.

关键词： quantum-behaved particle swarm optimization algorithm particle swarm optimization algorithm location wireless sensor networks

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An improved QPSO algorithm and its application in fuzzy portfolio model with constraints

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SOFT COMPUTING 2021年第12期25卷 7695-7706页

作者： He, Guang Lu, Xiao-li Chongqing Technol & Business Univ Coll Math & Stat Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Res Ctr Econ Upper Reaches Yangtze River Chongqing 400067 Peoples R China

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.

关键词： quantum-behaved particle swarm optimization algorithm Fuzzy return rate Background risk Hybrid distribution Contraction– expansion coefficient

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QPSO algorithm based on Levy flight and its application in fuzzy portfolio

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APPLIED SOFT COMPUTING 2021年 99卷 106894-106894页

作者： Lu, Xiao-li He, Guang Chongqing Technol & Business Univ Res Ctr Econ Upper Reaches Yangtze River Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Coll Math & Stat Chongqing 400067 Peoples R China

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.

关键词： Fuzzy portfolio model Transaction cost quantum-behaved particle swarm optimization algorithm Levy flight Contraction-expansion coefficient

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