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
Chemistry, Multidisciplinary
Beatriz C. Silva, Carine Menezes Rebello, Aliirio E. Rodrigues, Ana M. Ribeiro, Alexandre F. P. Ferreira, Idelfonso B. R. Nogueira
Summary: This study proposes a novel strategy for the design of adsorption heat pumps, which involves simultaneous optimization and material screening using the particle swarm optimization (PSO) approach. The proposed framework effectively evaluates different adsorbents and temperature intervals to find the optimal solution in terms of maximum performance and minimum heat supply cost. This approach provides a fast and intuitive evaluation of multiple design and operation variables.
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
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
Chemistry, Analytical
Anu Bajaj, Ajith Abraham, Saroj Ratnoo, Lubna Abdelkareim Gabralla
Summary: The emerging areas of IoT and sensor networks bring numerous software applications daily. To keep up with the ever-evolving expectations of clients and the competitive market, software updates are necessary. This paper proposes an improved quantum-behaved particle swarm optimization approach for regression testing, which outperforms other algorithms in prioritizing test cases.
Article
Computer Science, Information Systems
E. I. Elsedimy, Sara M. M. AboHashish, Fahad Algarni
Summary: Cardiovascular disease is a leading cause of death globally, and early detection plays a vital role in reducing the risk and improving recovery chances. Existing diagnostic methods for CVD are costly, have adverse side effects, and show low detection rates. This study proposes a novel heart disease detection model, QPSO-SVM, based on the quantum-behaved particle swarm optimization algorithm and support vector machine classification model, which achieves high prediction accuracies and outperforms other state-of-the-art models.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
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
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
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)
Article
Computer Science, Information Systems
Qiang Li, Songyong Liu, Mengdi Gao
Summary: Optimization of shovel-plate parameters is crucial for improving the performance of a roadheader. This study investigates the variations in loading capacity and shovel grubbing force and proposes multiobjective optimization methods using the ideal point and gray weight methods. The particle swarm optimization algorithm is applied for parameter optimization, resulting in significant improvements in the shovel plate's mass, resistance, and loading capacity. Simulated excavation experiments demonstrate reduced stress and increased fatigue life and safety factor. This optimization provides a theoretical basis and reference values for shovel plate design and can be applied in multiobjective optimization in engineering.
Article
Engineering, Electrical & Electronic
Yu Zhou, Lin Gao, Dong Wang, Wenhui Wu, Zhiqiang Zhou, Tingqun Ye
Summary: In this study, an improved localized feature selection method based on multiobjective binary particle swarm optimization was proposed to address fault diagnosis by utilizing the local distribution of data without the need for balancing strategies.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Physics, Multidisciplinary
Huidong Ling, Xinmu Zhu, Tao Zhu, Mingxing Nie, Zhenghai Liu, Zhenyu Liu
Summary: This paper proposes a parallel multiobjective PSO weighted average clustering algorithm based on Apache Spark. The algorithm divides the entire dataset into multiple partitions and caches the data in memory using distributed parallel and memory-based computing of Apache Spark. The local fitness value of each particle is calculated in parallel according to the data in each partition, reducing the communication of data in the network. Additionally, a weighted average calculation of the local fitness values is performed to improve the problem of unbalanced data distribution affecting the results.
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
Engineering, Mechanical
Anshuman Kumar Sahu, Siba Sankar Mahapatra, Marco Leite, Saurav Goel
Summary: This study addresses the engineering challenge of selecting the appropriate electrode material for electro-discharge machining. A hybrid intelligent algorithm is used, and experimental design and multi-objective optimization methods are employed to improve machining efficiency and output responses.
JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING
(2022)
Article
Multidisciplinary Sciences
Shihong Yin, Qifang Luo, Guo Zhou, Yongquan Zhou, Binwen Zhu
Summary: This paper proposes a hybrid equilibrium optimizer slime mould algorithm (EOSMA) to efficiently solve the inverse kinematics problem of complex manipulators. A multi-objective version of EOSMA (MOEOSMA) is also introduced. Experimental results comparing with other algorithms reveal that this method performs well in terms of accuracy and computation time.
SCIENTIFIC REPORTS
(2022)
Article
Automation & Control Systems
Xiangnan Zhong, Haibo He
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2020)
Article
Automation & Control Systems
Xiong Yang, Haibo He
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2020)
Article
Engineering, Electrical & Electronic
Hepeng Li, Zhiqiang Wan, Haibo He
IEEE TRANSACTIONS ON SMART GRID
(2020)
Article
Engineering, Electrical & Electronic
Fanrong Wei, Zhiqiang Wan, Haibo He
IEEE TRANSACTIONS ON SMART GRID
(2020)
Article
Engineering, Electrical & Electronic
Fanrong Wei, Zhiqiang Wan, Haibo He, Xiangning Lin
IEEE TRANSACTIONS ON SMART GRID
(2020)
Article
Automation & Control Systems
Hong Huang, Guangyao Shi, Haibo He, Yule Duan, Fulin Luo
IEEE TRANSACTIONS ON CYBERNETICS
(2020)
Article
Computer Science, Artificial Intelligence
Wei Ding, Lizhong Yao, Yanyan Li, Wei Long, Jun Yi, Tiantian He
Summary: This paper introduces a novel method based on a multi-sampling inherited hybrid annealed particle filter neural network (MSI-HAPFNN) which improves the self-adaptive ability of the object system to working conditions and the prediction accuracy of power consumption in an AEMS. Through the introduction of neural network and particle filter weights and the use of adaptive inheritance method, the model achieves features of multi-sampling and inheritance. The proposed model has been tested on a real world system for aluminium electrolysis manufacturing and shows significant improvement.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Lizhong Yao, Wei Long, Jun Yi, Taifu Li, Dedong Tang, Qingzheng Xu
Summary: This paper presents a new tournament selection method based on multilayer cultural characteristics in evolutionary multitasking, which significantly improves optimization performance by fully considering cultural features.
Article
Computer Science, Artificial Intelligence
Wei Ding, Lizhong Yao, Yanyan Li, Wei Long, Jun Yi
Summary: This paper introduces a novel model construction algorithm, combining improved clustering kernel function smoothing technique and particle filter neural network, which can improve prediction accuracy when dealing with non-Gaussian systems.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Automation & Control Systems
Jun Yi, Ling Wu, Wei Zhou, Haibo He, Lizhong Yao
Summary: Non-negative matrix factorization (NMF) and its variants are suitable for monitoring industrial processes with physically meaningless negative values, but are only effective for linear separable problems and not for nonlinear monitoring. The integration of the kernel-based method with projective NMF can enhance fault detection accuracy, and the KPNMF-FNN method further reduces the original variables. The proposed approach greatly reduces the time and storage space required while maintaining high fault detection rate and low false alarm rate.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Lizhong Yao, Wei Ding, Tiantian He, Shouxin Liu, Ling Nie
Summary: This paper presents a novel framework of multiobjective incremental learning based on a multi-source filter neural network (MSFNN) for the electrolytic aluminum process (EAP). The framework utilizes unscented Kalman filter (UKF) and density kernel estimation method to guide the importance function of particle filter (PF) and adjust weights in real time. The proposed model outperforms other recent filtering network models in terms of relative prediction errors. The successful establishment of this framework provides a model foundation for multiobjective optimization problems in the EAP.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Jun Yi, Wei Zhang, Junren Bai, Wei Zhou, Lizhong Yao
Summary: In this article, a novel MFEA based on improved dynamical decomposition (MFEA/IDD) is proposed for solving many-objective optimization problems (MaOPs). The MFEA/IDD algorithm integrates the advantages of multitasking optimization and decomposition-based evolutionary algorithms, and it effectively balances convergence and diversity while reducing the total number of function evaluations for solving MaOPs.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Energy & Fuels
Lizhong Yao, Yu Zhang, Tiantian He, Haijun Luo
Summary: In this study, a natural gas pipeline leakage detection model based on acoustic signal is proposed, which integrates acoustic feature processing techniques and feature reconstruction to collaboratively solve the problems of background noise coverage, lack of effective features, and low fault identification accuracy. The proposed method achieves a fault identification accuracy of 95.17% on the GPLA-12 dataset, demonstrating optimal performance and broad application prospects.
Article
Computer Science, Artificial Intelligence
Lusi Li, Haibo He, Jie Li
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2020)
Article
Computer Science, Artificial Intelligence
Hong Huang, Zhengying Li, Haibo He, Yule Duan, Song Yang
PATTERN RECOGNITION
(2020)
