Article
Computer Science, Software Engineering
Kang Liang, Xiukai Zhang, Oleg Krakhmalev
Summary: SLSL-QPSO is a software that improves the Quantum-behaved Particle Swarm Optimization (QPSO) algorithms by leveraging the concept of living and death as swarm layers. Experimental results demonstrate its superior performance in finding better optimal solutions compared to other algorithms.
Article
Automation & Control Systems
Song Liu, Shumin Zhou, Xiujuan Lu, Fang Gao, Feng Shuang, Sen Kuang
Summary: This paper presents a Lyapunov control scheme to drive finite-dimensional closed and Markovian open quantum systems into any target pure state with high fidelity and short time. The control law is established using a Lyapunov function and the optimal eigenvalues are searched using the quantum-behaved particle swarm optimization algorithm. A improved constrained QPSO algorithm is proposed for open systems with small denominator in the control law. Numerical simulations on different quantum systems demonstrate the effectiveness of the proposed control scheme.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Biochemical Research Methods
Tao Liu, Zhong Ren, Junli Wu, Chengxin Xiong, Wenping Peng
Summary: A photoacoustic detection system was established to accurately identify blood authenticity. The overlap of signals and spectra limited the accurate identification. To improve the correct rate, wavelet neural network (WNN) and quantum-behaved particle swarm optimization (QPSO) algorithm were employed, along with dynamic contraction-expansion coefficients and truncated mean stabilization strategy (TMSS) to enhance the performance. The method showed excellent performance in blood authenticity identification.
JOURNAL OF BIOPHOTONICS
(2022)
Article
Mathematics
Yeerjiang Halimu, Chao Zhou, Qi You, Jun Sun
Summary: This paper proposes a quantum-behaved particle swarm optimization (QPSO) algorithm on Riemannian manifolds named RQPSO to solve the issues of non-convex manifold global convergence and non-differentiable mathematical models. Experimental results show that RQPSO outperforms traditional algorithms in terms of calculation speed and optimization efficiency.
Article
Computer Science, Artificial Intelligence
Vankayala Sai Rugveth, Kiran Khatter
Summary: Particle swarm optimization (PSO) is a nature-inspired swarm intelligence algorithm that is driven by a social psychological model. This study focuses on a new quantum-behaved PSO method called Gaussian quantum-behaved particle swarm optimization (GQPSO) and its performance on different optimization problems. By conducting a full parametric sensitivity analysis, the optimal parameter set for GQPSO is identified.
Article
Computer Science, Information Systems
Xiaotong Li, Wei Fang, Shuwei Zhu
Summary: This research proposes an improved BQPSO algorithm to solve the 0-1 knapsack problem. The algorithm optimizes the discretization issue by introducing a mapping strategy and a transfer function. It also addresses infeasible solutions and local optima problems with a new repair method and a diversity maintenance mechanism. Experimental results show its superiority over other ten algorithms in terms of convergence speed and search performance.
INFORMATION SCIENCES
(2023)
Review
Computer Science, Artificial Intelligence
Emine Bas
Summary: This paper proposes a novel technique for handling big data optimization problems by detecting actual EEG signals while eliminating additional brain activity patterns. The study compares the performance of different algorithms on various datasets and statistically evaluates the results, finding that IPSO-Q can serve as an alternative algorithm for solving Big(Opt) problems.
NEURAL PROCESSING LETTERS
(2023)
Article
Environmental Sciences
Rui Zhang, Kaijie Xu, Yinghui Quan, Shengqi Zhu, Mengdao Xing
Summary: This study proposes a DOA detection method using Quantum-Behaved Particle Swarm Optimization for signal subspace reconstruction, effectively addressing missed detection and reduced accuracy due to low SNR and snapshot deficiency. The approach improves DOA detection performance when signals have varying SNR levels and small snapshots, with results showing superior estimation performance.
Article
Mathematics
Qi You, Jun Sun, Feng Pan, Vasile Palade, Bilal Ahmad
Summary: The paper integrates the QPSO algorithm with the MOEA/D framework to propose the DMO-QPSO algorithm, which improves performance through diversity control mechanisms and the introduction of non-dominated solutions. Experiments show that DMO-QPSO outperforms other algorithms in solving multi-objective problems.
Article
Engineering, Electrical & Electronic
Baoshan Ma, Jishuang Qi, Yiming Wu, Pengcheng Wang, Di Li, Shuxin Liu
Summary: The outbreak of COVID-19 has posed unprecedented challenges worldwide. Parametric modeling and analysis are crucial in understanding and controlling the pandemic, with the proposed improved quantum-behaved particle swarm optimization algorithm showing good accuracy and convergence when estimating parameters of the SEIR model.
DIGITAL SIGNAL PROCESSING
(2022)
Article
Computer Science, Information Systems
Washington Velasquez, Freddy Jijon-Veliz, Manuel S. S. Alvarez-Alvarado
Summary: This paper presents a robust algorithm that uses three quantum-behaved swarm optimization techniques to minimize the number of sensor nodes in a wireless sensor network (WSN). The algorithm aims to allocate a minimum number of sensors in forested areas while maintaining connectivity in highly vegetated environments. The proposed approach incorporates a propagation model to locate sensor nodes, calculate separation distances, verify line-of-sight compliance, and avoid intrusions in the first Fresnel zone. Results show the superiority of the quantum-behaved swarm optimization algorithms compared to traditional particle swarm optimization (PSO).
Article
Acoustics
Ning Yu, Zhaoxia Li, Yinfeng Wu, Renjian Feng
Summary: The article introduces an active noise control system based on evolutionary computation algorithm and proposes a path abruptly change-quantum-behaved particle swarm optimization algorithm to address the issue of system unable to re-converge in steady state. Experimental results demonstrate that the proposed algorithm can efficiently enhance noise reduction performance, accurately detect path changes, and re-converge to the new global optimum.
JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Qi You, Jun Sun, Vasile Palade, Feng Pan
Summary: This paper proposes a hybrid quantum-behaved particle swarm optimization algorithm (QPSO-DGS) with dynamic grouping searching strategy, which aims to solve the premature convergence issue in complex optimization problems. Experimental results show that QPSO-DGS has promising performance in terms of solution accuracy and convergence speed, especially on multimodal problems.
INTELLIGENT DATA ANALYSIS
(2023)
Article
Engineering, Mechanical
Yao Sun, Yi Wan, Haifeng Ma, Xichang Liang
Summary: This research proposes a quantitative analytic algorithm for optimizing the design of nuclear power plant emergency robots by analyzing and evaluating the workspace and optimizing the structural parameters of the robot. The method includes constructing a kinematic model of the mechanical arm and proposing an optimization algorithm based on the alpha shape to accurately describe the manipulator workspace. The suggested algorithm effectively optimizes the design of the master and slave robotic arms of the nuclear power plant emergency robots.
Article
Mathematics
Joo Hyun Moon, Kyun Ho Lee, Haedong Kim, Dong In Han
Summary: This study proposes an improved Gaussian quantum-behaved particle swarm optimization (GQPSO) algorithm for the thermal-economic optimization of a crossflow plate-fin heat exchanger (PFHE). The algorithm shows enhanced optimization performance by quickly searching for better global optimal solutions. The results demonstrate the efficiency of the improved GQPSO approach in finding more suitable solutions for the optimization problem.
