Active magnetic compensation is required to achieve near-zero magnetic environment for Magnetocardiography (MCG). The coupling relations between the magnetic fields of passive field cause difficulties in compensation....
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ISBN:
(纸本)9798350348958;9798350348965
Active magnetic compensation is required to achieve near-zero magnetic environment for Magnetocardiography (MCG). The coupling relations between the magnetic fields of passive field cause difficulties in compensation. In this paper, we establish a multidirectional magnetic field decoupling model which build adjustive vector and unidirectional magnetic field matrix (AV-UMFM) equations. Based on particleswarmoptimization (PSO) algorithm, the adjustive vector of the model can be acquired despite ill-conditioned problems. Finally, adjustive vector controls currents to simultaneously compensate for multidirectional residual magnetic fields and builds magnetic compensation system.
Cloud computing has witnessed exponential growth in recent years, resulting in a significant surge in energy consumption and operational costs of cloud data centres. Efficiently allocating Virtual Machines (VMs) withi...
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Cloud computing has witnessed exponential growth in recent years, resulting in a significant surge in energy consumption and operational costs of cloud data centres. Efficiently allocating Virtual Machines (VMs) within these data centres is crucial to achieve energy efficiency and optimize resource utilization. System instability may result from repeated requests for computing resources. One of the most critical difficulties facing virtualization technology is finding the best way to stack virtual machines on top of physical devices in cloud data centers. The host must move virtual machines from overloaded to underloaded hosts as part of load balancing, which has an impact on energy consumption. We propose energy-efficient particle swarm optimization algorithm (EEVMPSO) for Virtual Machine allocation is designed to maximize the load balancing. System resources, including CPU, storage, and memory, are optimized using EEVMPSO. The energy-aware virtual machine migration using the particle swarm optimization algorithm for dynamic VMs placement and energy -efficient cloud data centers. We conducted extensive experiments and simulations to evaluate the performance of the proposed algorithm in comparison to existing VM allocation methods. The results demonstrate the superiority of our approach in achieving energy efficiency and resource optimization. The experimental result shown in the proposed method, consumption energy in comparison to the PAPSO, KHA, EALBPSO, and RACC-MDT algorithm by 10.86%, 18.22%, 25.8%, and 31.34%, respectively, demonstrated the improvements in the service level agreements violation 5.77%, 15.3%, 26.19%, and 30.4%, as well as the average CPU utilization 2.2%, 24%, 22.6%, and 14.6%.
Recently, the problem of high-dimensional feature selection (FS) has become a current research focus in the field of evolutionary algorithms (EAs). However, most EA-based FS methods still face challenges in effectivel...
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ISBN:
(纸本)9789819755806;9789819755813
Recently, the problem of high-dimensional feature selection (FS) has become a current research focus in the field of evolutionary algorithms (EAs). However, most EA-based FS methods still face challenges in effectively combining relevance to remove redundant features, leading to low search efficiency and difficulty in finding high-quality feature subsets. To address this issue, this paper proposes a particle swarm optimization algorithm based on selection and non-selection operators and a local search mechanism, denoted as SNSLS-PSO. First, we design selection and non-selection operators based on Relief-F and roulette wheel selection to improve the quality of the selected feature subsets. Second, we introduce a local search mechanism based on an adaptive mutation operator, thereby avoiding local optima and enhance population diversity. In addition, we enhance the quality of particle selection by designing mutation probabilities for gene positions. The experimental results indicate that, compared to state-of-the-art FS methods, our proposed SNSLS-PSO can more efficiently select feature subsets and demonstrate superior classification performance on 14 high-dimensional datasets.
This paper deals with the parameter estimation of Hammerstein-Wiener (H-W) nonlinear systems which have unknown time delay. The linear variable weight particleswarm method is formulated for such time delay systems. T...
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This paper deals with the parameter estimation of Hammerstein-Wiener (H-W) nonlinear systems which have unknown time delay. The linear variable weight particleswarm method is formulated for such time delay systems. This algorithm transforms the nonlinear system identification issue into a function optimization issue in the parameter space, then utilizes the parallel searching ability of the particleswarmoptimization and the iterative identification technique to realize the simultaneous estimation of all parameters and the unknown time delay. Finally, parameters in the linear submodule, nonlinear submodule and the time delay are separated from the optimum parameter. Moreover, two illustrative examples are exhibited to evaluate the effectiveness of the proposed method. The simulation results demonstrate that the derived method has fast convergence speed and high estimation accuracy for estimating H-W systems with unknown time delay, and it is applied to the identification of the bed temperature systems. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.
For the input and output constraints and uncertainties in batch processes, a 2D output feedback robust constrained model predictive control (MPC) method is designed by combining iterative learning control (ILC), MPC a...
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For the input and output constraints and uncertainties in batch processes, a 2D output feedback robust constrained model predictive control (MPC) method is designed by combining iterative learning control (ILC), MPC and output feedback. Firstly, an equivalent 2D-FM closed-loop prediction model is established by combining with the proposed output feedback controller. Then an optimization performance index function with terminal constraint is constructed to study its control optimization. According to the designed optimization performance index and Lyapunov stability theory, the feasible MPC problem is obtained by solving the linear matrix inequalities (LMIs). At the same time, the gain of the new output feedback control law is given to ensure that the performance index reaches the minimum upper bound under the constraints of input and output. In order to solve the manual adjustment problem of some parameters in the performance index function, the particleswarmoptimization (PSO) algorithm is introduced, and a better solution is found near the controller by using the search optimization method. Finally, taking the injection molding process as an example and comparing with the existing method without using PSO algorithms, it is proved that the above method is more feasible.
In this paper, a multi-objective particleswarm optimizer based on adaptive dynamic neighborhood (ADN-MOPSO) is proposed to locate multiple Pareto optimal solutions to solve multimodal multi-objective problems. In the...
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ISBN:
(纸本)9798350334722
In this paper, a multi-objective particleswarm optimizer based on adaptive dynamic neighborhood (ADN-MOPSO) is proposed to locate multiple Pareto optimal solutions to solve multimodal multi-objective problems. In the proposed algorithm, a spatial distance-based non-overlapping ring topology is used to form multiple subpopulations for parallel search to enhance the local search capability of the algorithm. In addition, an adaptive dynamic neighborhood selection strategy is proposed to balance the exploration and exploitation capabilities of the algorithm, allowing the size of the subpopulation to change automatically when the neighborhood switch time is met. To prevent the algorithm from premature convergence, a stagnation detection strategy is introduced to apply a Gaussian perturbation operation to the particles that fall into the neighborhood optimum. Finally, the proposed algorithm is used to solve multimodal multi-objective test problems and compared with existing multimodal multi-objective optimizationalgorithms. The results show that the proposed algorithm can obtain more Pareto solutions when solving different types of multimodal multi-objective functions.
With the development of information technology, computer technology has been increasingly applied to management. This study aims to address problems such as low efficiency, high cost, and unstable quality in engineeri...
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With the development of information technology, computer technology has been increasingly applied to management. This study aims to address problems such as low efficiency, high cost, and unstable quality in engineering project management. A research proposes an optimization method for engineering project efficiency management based on multi-objective particle swarm optimization algorithm. The research can find a balance between cost, schedule and quality through multi-objective optimization, thereby achieving the maximization of comprehensive benefits. The multi-objective particleswarmalgorithm combines a segmented pulse module and a cloud adaptation module. The segmented pulse module is used to improve the global search capability, while the cloud adaptation module achieves fast convergence and global optimization by dynamically adjusting inertia weights. The experimental results showed that the distribution index of the multi-objective particleswarmalgorithm was 0.084, and the average convergence index was 0.33. After optimizing the application of a new model in a certain construction project, the cost decreased from 651100 yuan to 456500 yuan. The construction time was reduced from 132 days to 106.19 days. The quality coefficient of the main body increased from 0.78 to 0.96, an increase of 23.08%. This results indicated that the particle swarm optimization algorithm could provide efficient, cost-effective, and high-quality optimization solutions for engineering project management. The new model could effectively reduce the engineering cost, significantly shorten the construction time, and improve the construction quality coefficient. In addition, in medical resource allocation, multi-objective particle swarm optimization algorithm can optimize the resource allocation plan, balancing fairness and efficiency. In transportation planning, multi-objective particle swarm optimization algorithm can optimize path planning, improve transportation efficiency and resou
Percutaneous puncture interventional therapy is an important method for pathological examination, local anesthesia, and local drug delivery in modern clinics. Due to the existence of complex obstacles such as nerves, ...
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Percutaneous puncture interventional therapy is an important method for pathological examination, local anesthesia, and local drug delivery in modern clinics. Due to the existence of complex obstacles such as nerves, arteries, bones and so on in the puncture path, it is a challenging work to design the optimal path for surgical needle. In this paper, we propose a new path planning method based on the adaptive intelligent particleswarmoptimization (PSO) algorithm with parameter adjustment mechanism. First, force and motion analysis are carried out on the bevel-tip flexible needle after piercing into human tissues, the motion model of the needle and the spatial transformation model of puncture route in three-dimensional space are obtained, respectively. Then, a multi-objective function is established, which includes puncture path length function, puncture error function and collision detection function. Finally, the optimal puncture path is obtained based on the adaptive intelligent PSO algorithm. The simulation results show that the newly proposed path planning method has higher efficiency, better adaptability to complex environments and higher accuracy than other path planning methods in literature.
In this paper, attitude tracking control of spacecraft is studied. Firstly, the fully actuated system models of rigid body satellite is established, and then the linear sliding mode controller is designed by direct pa...
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ISBN:
(纸本)9798350332162
In this paper, attitude tracking control of spacecraft is studied. Firstly, the fully actuated system models of rigid body satellite is established, and then the linear sliding mode controller is designed by direct parameterization method. One of the key problems in direct parameterization is the tuning of matrix parameters. Aiming at the difficulty of parameter setting of spacecraft attitude controller based on fully actuated system approach, a parameter setting method based on particle swarm optimization algorithm was proposed. particleswarmoptimization is used to adjust matrix parameters more quickly and accurately. At the same time, inertia weight factor is introduced to avoid the early local optimal phenomenon of particle swarm optimization algorithm. Simulation results reveal the effect of the proposed control approach.
Based on the theoretic study of location of general facilities, this paper makes an attempt to optimize the typical discrete element of the location of aviation rescue base through discrete binary particleswarm (PSO)...
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ISBN:
(纸本)9783642385247;9783642385230
Based on the theoretic study of location of general facilities, this paper makes an attempt to optimize the typical discrete element of the location of aviation rescue base through discrete binary particleswarm (PSO) algorithm in order to find out an optimized location method with more simplified calculation and more optimized result, which will finally provide a solid theoretical foundation for the location of aviation rescue base.
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