scholarly journals Determining of geometrical characteristic parameters of particle fractal aggregates from light scattering measurement signals

2020 ◽  
pp. 203-203
Author(s):  
Liang Xu ◽  
Zhen-Zong He ◽  
Jun-Kui Mao ◽  
Xing-Si Han
Keyword(s):  
Particle Swarm Optimization ◽  
Light Scattering ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Fractal Aggregates ◽  
Swarm Optimization ◽  
Repulsive Particle Swarm Optimization ◽  
Light Scattering Measurement ◽  
Scattering Measurement

Two kind of light scattering measurement methods, i.e. the forward light scattering measurement (FLSM) method and the angular light scattering measurement (ALSM) method, are applied to reconstruct the geometrical morphology of particle fractal aggregates. An improved Attractive and Repulsive Particle Swarm Optimization (IARPSO) algorithm is applied to reconstruct the geometrical structure of fractal aggregates. It has been confirmed to show better convergence properties than the original Particle Swarm Optimization (PSO) algorithm and the Attractive and Repulsive Particle Swarm Optimization (ARPSO) algorithm. Compared with the FLSM method, the ASLM method can obtain more accurate and robust results as the distribution of the fitness function value obtained by the ALSM method is more satisfactory. Meanwhile, the retrieval accuracy can be improved by increasing the number of measurement angles or the interval between adjacent measurement angles even when the random noises are added. All the conclusions have important guiding significance for the further study of the geometry reconstruction experiment of fractal aggregates.

scholarly journals Optimal Design of Multimissile Formation Based on an Adaptive SA-PSO Algorithm

Aerospace ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 21
Author(s):  
Shuangxi Liu ◽  
Fengping Huang ◽  
Binbin Yan ◽  
Tong Zhang ◽  
Ruifan Liu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Index System ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Evaluation Index System ◽  
Design Parameters ◽  
Swarm Optimization ◽  
Adaptive Pso

In an effort to maximize the combat effectiveness of multimissile groups, this paper proposes an adaptive simulated annealing–particle swarm optimization (SA-PSO) algorithm to enhance the design parameters of multimissile formations based on the concept of missile cooperative engagement. Firstly, considering actual battlefield circumstances, we establish an effectiveness evaluation index system for the cooperative engagement of missile formations based on the analytic hierarchy process (AHP). In doing so, we adopt a partial triangular fuzzy number method based on authoritative assessments by experts to ascertain the weight of each index. Then, considering given constraints on missile performance, by selecting the relative distances and angles of the leader and follower missiles as formation parameters, we design a fitness function corresponding to the established index system. Finally, we introduce an adaptive capability into the traditional particle swarm optimization (PSO) algorithm and propose an adaptive SA-PSO algorithm based on the simulated annealing (SA) algorithm to calculate the optimal formation parameters. A simulation example is presented for the scenario of optimizing the formation parameters of three missiles, and comparative experiments conducted with the traditional and adaptive PSO algorithms are reported. The simulation results indicate that the proposed adaptive SA-PSO algorithm converges faster than both the traditional and adaptive PSO algorithms and can quickly and effectively solve the multimissile formation optimization problem while ensuring that the optimized formation satisfies the given performance constraints.

scholarly journals Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization

2021 ◽  
Vol 11 (8) ◽  
pp. 3417
Author(s):  
Nafis Ahmed ◽  
Chaitali J. Pawase ◽  
KyungHi Chang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Human Beings ◽  
Swarm Optimization ◽  
Aerial Vehicles ◽  
Area Surveillance

Collision-free distributed path planning for the swarm of unmanned aerial vehicles (UAVs) in a stochastic and dynamic environment is an emerging and challenging subject for research in the field of a communication system. Monitoring the methods and approaches for multi-UAVs with full area surveillance is needed in both military and civilian applications, in order to protect human beings and infrastructure, as well as their social security. To perform the path planning for multiple unmanned aerial vehicles, we propose a trajectory planner based on Particle Swarm Optimization (PSO) algorithm to derive a distributed full coverage optimal path planning, and a trajectory planner is developed using a dynamic fitness function. In this paper, to obtain dynamic fitness, we implemented the PSO algorithm independently in each UAV, by maximizing the fitness function and minimizing the cost function. Simulation results show that the proposed distributed path planning algorithm generates feasible optimal trajectories and update maps for the swarm of UAVs to surveil the entire area of interest.

Optimization of PMSM Sensorless Control Based on Improved PSO Algorithm

2011 ◽  
Vol 383-390 ◽  
pp. 86-92
Author(s):  
Miao Wang Qian ◽  
Guo Jun Tan ◽  
Ning Ning Li ◽  
Zhong Xiang Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing Algorithm ◽  
Sensorless Control ◽  
Fitness Function ◽  
Permanent Magnet Synchronous Motor ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Extend Kalman Filter ◽  
Swarm Optimization ◽  
Manual Adjustment

For the problem that manual adjustment of the parameters of controller in sensorless control system costs too much time, manpower and always can not get a good result, a new method based on improved particle swarm optimization algorithm is proposed to optimize the parameters. The improved algorithm is based on the standard particle swarm optimization with the simulated annealing algorithm and chaotic search brought in. The speed of motor is estimated by the extend Kalman filter. The error between measured speed and estimated speed of the permanent magnet synchronous motor rotor is used as the fitness function in order that the parameters in the covariance matrix is adjusted.The result of simulation indicates that high estimation precision can be got and the motor represents steadily with few of ripple of the actual speed.With this method, the time of adjustment is reduced and manpower is saved. In addition, the validity of the method is proved in experiment with dSPACE.

Design on Filter Parameters with Hybrid PSO Algorithm

2013 ◽  
Vol 303-306 ◽  
pp. 1888-1891
Author(s):  
Yi Zhang ◽  
Ke Wen Xia ◽  
Gen Gu
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Filter Model ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization ◽  
Model Setting

In order to solve the problems in the optimization of filter parameters, such as large amounts of calculation and the complicated mathematical hypotheses, an approach to optimize filter parameters is presented based on the Hybrid Particle swarm optimization (HPSO) algorithm, which includes the establishing of filter model, setting up the fitness-function and optimizing filter parameters by HPSO algorithm. The application example shows that the optimization method improves the design accuracy and saves calculation, and HPSO algorithm is superior to PSO algorithm in optimization of filter parameters.

scholarly journals A New Approach to Sequence Construction With Good Correlation by Particle Swarm Optimization

2015 ◽  
Vol 11 (3) ◽  
pp. 127 ◽  
Author(s):  
Mahdiyar Sarayloo ◽  
Ennio Gambi ◽  
Susanna Spinsante
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Binary Sequence ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Binary Sequences ◽  
Swarm Optimization ◽  
Variable Population ◽  
Relationship Of ◽  
The Relationship

In this paper, a novel computationally affordable method to generate long binary sequences featuring desired properties is presented, based on the use of a number of shorter non linear binary sub-sequences. The paper shows the relationship of the Auto- and Cross-Correlation (AC, CC) ofthe generated long binary sequences with the AC and CC ofconstituent sub-sequences. It is also shown that the starting bit position of sub-sequences has an important role on AC and CC of the generated sequences. To generate the optimal long binary sequence from correlation points of view, Particle Swarm Optimization (PSO) algorithm is employed. All the techniques stated in the literature to improve the PSO are implemented and it is clearly shown that the constriction factor and the variable population size turn out to have a great impact on minimizing the fitness function (RMS of AC) representing the target Correlationproperties expected for the resulting long sequence. Possible application scenarios for the long sequences generated by the proposed method are also discussed and evaluated.

Adaptive Privacy Preservation Approach for Big Data Publishing in Cloud using K-Anonymization

2020 ◽  
Vol 13 ◽  
Author(s):  
Suman Madan ◽  
Puneet Goswami
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Particle Swarm Optimization ◽  
Classification Accuracy ◽  
Privacy Preservation ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Information Loss ◽  
Swarm Optimization

Background: Big data is an emerging technology that has numerous applications in the fields, like hospitals, government records, social sites, and so on. As the cloud computing can transfer large amount of data through servers it has found its importance in big data. Hence, it is important in cloud computing to protect the data so that the third party users cannot access the information from the users. Methods: This paper develops an anonymization model and adaptive Dragon Particle Swarm Optimization (adaptive Dragon-PSO) algorithm for privacy preservation in cloud environment. The development of proposed adaptive DragonPSO incorporates the integration of adaptive idea in the dragon-PSO algorithm. The dragon-PSO is the integration of Dragonfly Algorithm (DA) and Particle Swarm Optimization (PSO) algorithm. The proposed method derives the fitness function for the proposed adaptive Dragon-PSO algorithm to attain the higher value of privacy and utility. The performance of the proposed method was evaluated using the metrics, such as information loss and classification accuracy for different anonymization constant values. Conclusion: The proposed method provided a minimal information loss and maximal classification accuracy of 0.0110 and 0.7415, respectively when compared with the existing methods.

scholarly journals A hybrid PSO approach for solving non-convex optimization problems

2012 ◽  
Vol 22 (1) ◽  
pp. 87-105 ◽  
Author(s):  
Timothy Ganesan ◽  
Pandian Vasant ◽  
Irraivan Elamvazuthy
Keyword(s):  
Particle Swarm Optimization ◽  
Convex Optimization ◽  
Optimization Problems ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Convex Optimization Problems ◽  
Computational Performance ◽  
Engineering Design Problems

A hybrid PSO approach for solving non-convex optimization problemsThe aim of this paper is to propose an improved particle swarm optimization (PSO) procedure for non-convex optimization problems. This approach embeds classical methods which are the Kuhn-Tucker (KT) conditions and the Hessian matrix into the fitness function. This generates a semi-classical PSO algorithm (SPSO). The classical component improves the PSO method in terms of its capacity to search for optimal solutions in non-convex scenarios. In this work, the development and the testing of the refined the SPSO algorithm was carried out. The SPSO algorithm was tested against two engineering design problems which were; ‘optimization of the design of a pressure vessel’ (P1) and the ‘optimization of the design of a tension/compression spring’ (P2). The computational performance of the SPSO algorithm was then compared against the modified particle swarm optimization (PSO) algorithm of previous work on the same engineering problems. Comparative studies and analysis were then carried out based on the optimized results. It was observed that the SPSO provides a better minimum with a higher quality constraint satisfaction as compared to the PSO approach in the previous work.

Route Planning of Geomagnetic Aided Navigation for Vehicle under Matching Suitability Constraints

2013 ◽  
Vol 454 ◽  
pp. 39-42
Author(s):  
Peng Wang ◽  
Xiao Ping Hu ◽  
Mei Ping Wu ◽  
Hua Mu ◽  
Hai Ping Yuan
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Route Planning ◽  
Pso Algorithm ◽  
Planning Method ◽  
Experimental Results ◽  
Planning Problem ◽  
Swarm Optimization ◽  
Movement Distance

In geomagnetic aided navigation (GAN), the vehicle is expected to traverse the areas with excellent matching suitability in order to obtain high matching precision. The route planning problem under matching suitability constraints is studied based on particle swarm optimization (PSO) algorithm in this article. Firstly, the PSO algorithm is briefly introduced and the expanding space of route nodes is determined with the maneuverability constraints of the vehicle. Then the minimum movement distance, the ability of avoiding threats and the proximity to suitable-matching areas are considered to construct the fitness function of PSO algorithm. Further the route planning method under matching suitability constraints is proposed. Experimental results show that the proposed method is effective, and the vehicle can successfully avoid the threats and can traverse the suitable-matching areas.

scholarly journals Damage Identification of Bridge Based on Modal Flexibility and Neural Network Improved by Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Hanbing Liu ◽  
Gang Song ◽  
Yubo Jiao ◽  
Peng Zhang ◽  
Xianqiang Wang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Damage Identification ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Bp Network ◽  
Swarm Optimization ◽  
Modal Flexibility ◽  
Two Stages

An approach to identify damage of bridge utilizing modal flexibility and neural network optimized by particle swarm optimization (PSO) is presented. The method consists of two stages; modal flexibility indices are applied to damage localizing and neural network optimized by PSO is used to identify the damage severity. Numerical simulation of simply supported bridge is presented to demonstrate feasibility of the proposed method, while comparative analysis with traditional BP network is for its superiority. The results indicate that curvature of flexibility changes can identify damages with both single and multiple locations. The optimization of bias and weight for neural network by fitness function of PSO algorithm can realize favorable damage severity identification and possesses more satisfactory accuracy than traditional BP network.

Optimum Vibration Design of Fiber Metal Laminated Panels by Particle Swarm Optimization Algorithm

2011 ◽  
Author(s):  
Mohammad Homayoun Sadr ◽  
Hadi Ghashochi Bargh ◽  
Hoofar Pourzand ◽  
Masoud Karimi
Keyword(s):  
Particle Swarm Optimization ◽  
Plate Theory ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Good Efficiency ◽  
Design Variables ◽  
Laminated Panels ◽  
Fiber Metal

This paper illustrates the application of the particle swarm optimization (PSO) algorithm to the lay-up design of symmetrically fiber metal laminated panels for maximization of fundamental frequency. The design variables are the number of layers, the fiber orientation angles of inner composite layers, edge conditions and panel length/width ratios. The classical laminated plate theory (CLPT) is used to calculate the natural frequencies and the fitness function is computed with a semi-analytical finite strip method which has been developed on the basis of full energy methods. The performance of the PSO is also compared with the simple genetic algorithm and shows the good efficiency of the PSO algorithm. To check the validity, the results are corroborated by comparing with some other stacking sequences.

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