scholarly journals A New Ground-Based Pseudolite System Deployment Algorithm Based on MOPSO

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5364
Author(s):  
Wenjie Tang ◽  
Junping Chen ◽  
Chao Yu ◽  
Junsheng Ding ◽  
Ruyuan Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Convex Polyhedron ◽  
Particle Swarm ◽  
Coverage Area ◽  
Swarm Optimization ◽  
Digital Elevation ◽  
Signal Coverage ◽  
Optimal Deployment ◽  
The Mean ◽  
Elevation Model

Pseudolite deployment is the premise of ground-based pseudolite system networking, which affects the coverage and positioning accuracy of ground-based pseudolite systems. Optimal deployment algorithms can help to achieve a higher signal coverage and lower mean horizontal precision factor (HDOP) with a limited number of pseudolites. In this paper, we proposed a multi-objective particle swarm optimization (MOPSO) algorithm for the deployment of a ground-based pseudolite system. The new algorithm combines Digital Elevation Model (DEM) data and uses the mean HDOP of the DEM grid to measure the geometry of the pseudolite system. The signal coverage of the pseudolite system was calculated based on the visual area analysis with respect to reference planes, which effectively avoids the repeated calculation of the intersection and improves the calculation efficiency. A selected area covering 10 km×10 km in the Jiuzhaigou area of China was used to verify the new algorithm. The results showed that both the coverage and HDOP achieved were optimal using the new algorithm, where the coverage area can be up to approximately 50% and 30% more than using the existing particle swarm optimization (PSO) and convex polyhedron volume optimization (CPVO) algorithms, respectively.

Magnetic Remanence Prediction of NdFeB Magnets Based on a Novel Machine Learning Intelligence Approach Using a Particle Swarm Optimization Support Vector Regression

2014 ◽  
Vol 6 (4) ◽  
pp. 72-81 ◽  
Author(s):  
WenDe Cheng
Keyword(s):  
Magnetic Properties ◽  
Particle Swarm Optimization ◽  
Support Vector Regression ◽  
Particle Swarm ◽  
Support Vector ◽  
Chemical Compositions ◽  
Swarm Optimization ◽  
Ndfeb Magnets ◽  
Magnetic Remanence ◽  
The Mean

Studies have shown that the chemical compositions affecting the magnetic properties of NdFeB magnets. In order to get the right NdFeB magnets, it is advantageous to have an accurate model with which one can predict the magnetic properties with different components. In this paper, according to an experimental dataset on the magnetic remanence of NdFeB, a predicting and optimizing model using support vector regression (SVR) combined with particle swarm optimization (PSO) was developed. The estimated result of SVR agreed with the experimental data well. Test results of leave-one-out cross validation show that the mean absolute error does not exceed 0.0036, the mean absolute percentage error is solely 0.53%, and the correlation coefficient () is as high as 0.839. This implies that one can estimate an available combination of different proportion components by using support vector regression model to get suitable magnetic remanence of NdFeB.

scholarly journals Multiobjective Robust Design of the Double Wishbone Suspension System Based on Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xianfu Cheng ◽  
Yuqun Lin
Keyword(s):  
Sensitivity Analysis ◽  
Particle Swarm Optimization ◽  
Robust Optimization ◽  
Optimization Design ◽  
Particle Swarm ◽  
Design Sensitivity ◽  
Suspension System ◽  
Design Parameters ◽  
Swarm Optimization ◽  
The Mean

The performance of the suspension system is one of the most important factors in the vehicle design. For the double wishbone suspension system, the conventional deterministic optimization does not consider any deviations of design parameters, so design sensitivity analysis and robust optimization design are proposed. In this study, the design parameters of the robust optimization are the positions of the key points, and the random factors are the uncertainties in manufacturing. A simplified model of the double wishbone suspension is established by software ADAMS. The sensitivity analysis is utilized to determine main design variables. Then, the simulation experiment is arranged and the Latin hypercube design is adopted to find the initial points. The Kriging model is employed for fitting the mean and variance of the quality characteristics according to the simulation results. Further, a particle swarm optimization method based on simple PSO is applied and the tradeoff between the mean and deviation of performance is made to solve the robust optimization problem of the double wishbone suspension system.

scholarly journals WSN Node Optimal Deployment Algorithm Based on Adaptive Binary Particle Swarm Optimization

2021 ◽  
Vol 1 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Yujiang Li ◽  
Jinghua Cao
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Binary Particle Swarm Optimization ◽  
Network Coverage ◽  
Swarm Optimization ◽  
Network Nodes ◽  
Optimal Deployment

In order to optimize the deployment of wireless sensor network nodes, and avoid network energy consumption increase due to node redundancy and uneven coverage, the multi-objective mathematical optimization problem of area coverage is transformed into a function problem. Aiming at network coverage rate, node dormancy rate and network coverage uniformity, the idea of genetic algorithm mutation is introduced based on the discrete binary particle swarm optimization and the global optimal speed is mutated to avoid the algorithm falling into the local optimal solution. In order to further improve the optimization ability of the algorithm, the adaptive learning factor and inertia weight are introduced to obtain the optimal deployment algorithm of wireless sensor network nodes. The experimental results show that the algorithm can reduce the number of active nodes efficiently, improve coverage uniformity, reduce network energy consumption and prolong network lifetime under the premise that the coverage rate is greater than 90%, and compared with an algorithm called coverage configuration protocol, an algorithm called finding the minimum working sets in wireless sensor networks, and an algorithm called binary particle swarm optimization-g in literature, the number of active nodes in this algorithm is reduced by about 36%, 30% and 23% respectively.

Signal filtering method of variational mode decomposition and Euclidean distance based on optimizing parameters of classification particle swarm optimization algorithm

2021 ◽  
pp. 014233122198900
Author(s):  
Jingyi Lu ◽  
Xue Qu ◽  
Dongmei Wang ◽  
Jikang Yue ◽  
Lijuan Zhu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Noise Reduction ◽  
Euclidean Distance ◽  
Particle Swarm ◽  
Variational Mode Decomposition ◽  
Swarm Optimization ◽  
Signal Filtering ◽  
Mode Decomposition ◽  
Mean And Variance ◽  
The Mean

In order to deal with the problem that the noise of leakage signals from natural gas pipelines has a great influence on the feature extraction of pipeline leakage, this paper proposes a signal denoising method of variational mode decomposition (VMD) and Euclidean distance (ED) based on optimizing parameters of classification particle swarm optimization (CPSO) algorithm. First, CPSO algorithm is used to optimize parameters K and [Formula: see text] of VMD, adaptively. The sum of the ratio of the mean and variance of the cross-correlation coefficient and the ratio of the mean and variance of kurtosis is used as the fitness function of CPSO. Then, the optimized VMD is used to decompose the signal to obtain several intrinsic mode functions (IMFs). Finally, ED is used to select the effective modes, and the signal is reconstructed to achieve signal noise reduction. The corresponding evaluation indicators show that the accuracy and robustness of the improved method are better than other noise reduction methods. The denoising effect is significant, which proves that the algorithm proposed in this paper is effective in signal filtering.

scholarly journals Particle Swarm Optimization-Based Noise Filtering Algorithm for Photon Cloud Data in Forest Area

2019 ◽  
Vol 11 (8) ◽  
pp. 980 ◽  
Author(s):  
Jiapeng Huang ◽  
Yanqiu Xing ◽  
Haotian You ◽  
Lei Qin ◽  
Jing Tian ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Background Noise ◽  
Spatial Clustering ◽  
Particle Swarm ◽  
Noise Filtering ◽  
Swarm Optimization ◽  
Cloud Data ◽  
Filtering Algorithm ◽  
Statistical Algorithm ◽  
The Mean

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), was launched successfully in 15 September 2018. The ATLAS represents a micro-pulse photon-counting laser system, which is expected to provide more comprehensive and scientific data for carbon storage. However, the ATLAS system is sensitive to the background noise, which poses a tremendous challenge to the photon cloud noise filtering. Moreover, the Density Based Spatial Clustering of Applications with Noise (DBSCAN) is a commonly used algorithm for noise removal from the photon cloud but there has not been an in-depth study on its parameter selection yet. This paper presents an automatic photon cloud filtering algorithm based on the Particle Swarm Optimization (PSO) algorithm, which can be used to optimize the two key parameters of the DBSCAN algorithm instead of using the manual parameter adjustment. The Particle Swarm Optimization Density Based Spatial Clustering of Applications with Noise (PSODBSCAN) algorithm was tested at different laser intensities and laser pointing types using the MATLAS dataset of the forests located in Virginia, East Coast, and the West Coast, USA. The results showed that the PSODBSCAN algorithm and the localized statistical algorithm were effective in identifying the background noise and preserving the signal photons in the raw MATLAS data. Namely, the PSODBSCAN achieved the mean F value of 0.9759, and the localized statistical algorithm achieved the mean F value of 0.6978. For both laser pointing types and laser intensities, the proposed algorithm achieved better results than the localized statistical algorithm. Therefore, the PSODBSCAN algorithm could support the MATLAS photon cloud data noise filtering applicably without manually selecting parameters.

scholarly journals The Particle Swarm Optimization Algorithm with Adaptive Chaos Perturbation

2016 ◽  
Vol 11 (6) ◽  
pp. 804 ◽  
Author(s):  
Li Mengxia ◽  
Liao Ruiquan ◽  
Dong Yong
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing Algorithm ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Population Diversity ◽  
Swarm Optimization ◽  
Chaotic Mapping ◽  
Velocity Information ◽  
The Mean ◽  
Annealing Algorithm

Aiming at the two characteristics of premature convergence of particle swarm optimization that the particle velocity approaches 0 and particle swarm congregate, this paper learns from the annealing function of the simulated annealing algorithm and adaptively and dynamically adjusts inertia weights according to the velocity information of particles to avoid approaching 0 untimely. This paper uses the good uniformity of Anderson chaotic mapping and performs chaos perturbation to part of particles based on the information of variance of the population’s fitness to avoid the untimely aggregation of particle swarm. The numerical simulations of five test functions are performed and the results are compared with several swarm intelligence heuristic algorithms. The results shows that the modified algorithm can keep the population diversity well in the middle stage of the iterative process and it can improve the mean best of the algorithm and the success rate of search.

scholarly journals A Novel Adaptive Elite-Based Particle Swarm Optimization Applied to VAR Optimization in Electric Power Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Ying-Yi Hong ◽  
Faa-Jeng Lin ◽  
Syuan-Yi Chen ◽  
Yu-Chun Lin ◽  
Fu-Yuan Hsu
Keyword(s):  
Particle Swarm Optimization ◽  
Electric Power ◽  
Particle Swarm ◽  
Solution Space ◽  
Electric Power System ◽  
Stable Convergence ◽  
Swarm Optimization ◽  
Local Searches ◽  
The Mean ◽  
Global And Local

Particle swarm optimization (PSO) has been successfully applied to solve many practical engineering problems. However, more efficient strategies are needed to coordinate global and local searches in the solution space when the studied problem is extremely nonlinear and highly dimensional. This work proposes a novel adaptive elite-based PSO approach. The adaptive elite strategies involve the following two tasks: (1) appending the mean search to the original approach and (2) pruning/cloning particles. The mean search, leading to stable convergence, helps the iterative process coordinate between the global and local searches. The mean of the particles and standard deviation of the distances between pairs of particles are utilized to prune distant particles. The best particle is cloned and it replaces the pruned distant particles in the elite strategy. To evaluate the performance and generality of the proposed method, four benchmark functions were tested by traditional PSO, chaotic PSO, differential evolution, and genetic algorithm. Finally, a realistic loss minimization problem in an electric power system is studied to show the robustness of the proposed method.

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