scholarly journals Performance optimization in video transmission over ZigBee using Particle Swarm Optimization

2016 ◽  
Author(s):  
Iman Samizadeh ◽  
Hassan Kazemian ◽  
Ken Fisher ◽  
Karim Ouazzane
Keyword(s):  
Particle Swarm Optimization ◽  
Performance Optimization ◽  
Video Transmission ◽  
Particle Swarm ◽  
Swarm Optimization

scholarly journals Beam Performance Optimization of Multibeam Imaging Sonar Based on the Hybrid Algorithm of Binary Particle Swarm Optimization and Convex Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Weijie Xia ◽  
Xue Jin ◽  
Fawang Dou
Keyword(s):  
Particle Swarm Optimization ◽  
Convex Optimization ◽  
Optimization Algorithm ◽  
Performance Optimization ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Two Factors ◽  
Imaging Sonar

It should be noted that the peak sidelobe level (PSLL) significantly influences the performance of the multibeam imaging sonar. Although a great amount of work has been done to suppress the PSLL of the array, one can verify that these methods do not provide optimal results when applied to the case of multiple patterns. In order to suppress the PSLL for multibeam imaging sonar array, a hybrid algorithm of binary particle swarm optimization (BPSO) and convex optimization is proposed in this paper. In this algorithm, the PSLL of multiple patterns is taken as the optimization objective. BPSO is considered as a global optimization algorithm to determine best common elements’ positions and convex optimization is considered as a local optimization algorithm to optimize elements’ weights, which guarantees the complete match of the two factors. At last, simulations are carried out to illustrate the effectiveness of the proposed algorithm in this paper. Results show that, for a sparse semicircular array with multiple patterns, the hybrid algorithm can obtain a lower PSLL compared with existing methods and it consumes less calculation time in comparison with other hybrid algorithms.

PERFORMANCE EVALUATION OF PARALLEL GENETIC AND PARTICLE SWARM OPTIMIZATION ALGORITHMS WITHIN THE MULTICORE ARCHITECTURE

2014 ◽  
Vol 13 (04) ◽  
pp. 1450024
Author(s):  
A. S. RADHAMANI ◽  
E. BABURAJ
Keyword(s):  
Particle Swarm Optimization ◽  
Performance Optimization ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Swarm Optimization ◽  
Multicore Architecture ◽  
Bacterial Foraging ◽  
Processor Performance ◽  
Almost All ◽  
Time And Energy

In recent studies we found that there are many optimization methods presented for multicore processor performance optimization, however each method is suffered from limitations. Hence in this paper we presented a new method which is a combination of bacterial Foraging Particle swarm Optimization with certain constraints named as Constraint based Bacterial Foraging Particle Swarm Optimization (CBFPSO) scheduling can be effectively implemented. The proposed Constraint based Bacterial Foraging Particle Swarm Optimization (CBFPSO) scheduling for multicore architecture, which updates the velocity and position by two bacterial behaviours, i.e. reproduction and elimination dispersal. The performance of CBFPSO is compared with the simulation results of GA, and the result shows that the proposed algorithm has pretty good performance on almost all types of cores compared to GA with respect to completion time and energy consumption.

Improved Particle Swarm Optimization (PSO) for Performance Optimization of Electronic Filter Circuit Designs

2012 ◽  
Vol 229-231 ◽  
pp. 1643-1650
Author(s):  
Chong Woon Kien ◽  
Neoh Siew Chin
Keyword(s):  
Particle Swarm Optimization ◽  
Performance Optimization ◽  
Particle Swarm ◽  
Design Parameters ◽  
Pass Band ◽  
Swarm Optimization ◽  
Pass Filter ◽  
High Pass Filter ◽  
And Performance ◽  
High Pass

This article discusses and analyzes particle swarm optimization (PSO) approach in the design and performance optimization of a 4th-order Sallen Key high pass filter. Three types of particle swarm features are studied: basic PSO, PSO with regrouped particles (PSO-RP) and PSO with diversity embedded regrouped particles (PSO-DRP). PSO-RP and PSO-DRP are proposed to solve the stagnation problem of basic PSO. Based on the developed PSO approaches, LTspice is employed as the circuit simulator for the performance investigation of the designed filter. In this paper, 12 design parameters of the Sallen Key high pass filter are optimized to satisfy the required constraints and specifications on gain, cut-off frequency, and pass band ripples. Overall results show that PSO with diversity embedded regrouped particles improve the conventional search of basic PSO and has managed to achieve the design objectives.

scholarly journals Review on the cost optimization of microgrids via particle swarm optimization

2019 ◽  
Vol 11 (1) ◽  
pp. 73-89 ◽  
Author(s):  
Sengthavy Phommixay ◽  
Mamadou Lamine Doumbia ◽  
David Lupien St-Pierre
Keyword(s):  
Particle Swarm Optimization ◽  
Performance Optimization ◽  
Optimal Power Flow ◽  
Cost Optimization ◽  
Particle Swarm ◽  
Optimal Operation ◽  
Optimization Techniques ◽  
Energy Management Strategy ◽  
Swarm Optimization ◽  
The Cost

AbstractEconomic analysis is an important tool in evaluating the performances of microgrid (MG) operations and sizing. Optimization techniques are required for operating and sizing an MG as economically as possible. Various optimization approaches are applied to MGs, which include classic and artificial intelligence techniques. Particle swarm optimization (PSO) is one of the most frequently used methods for cost optimization due to its high performance and flexibility. PSO has various versions and can be combined with other intelligent methods to realize improved performance optimization. This paper reviews the cost minimization performances of various economic models that are based on PSO with regard to MG operations and sizing. First, PSO is described, and its performance is analyzed. Second, various objective functions, constraints and cost functions that are used in MG optimizations are presented. Then, various applications of PSO for MG sizing and operations are reviewed. Additionally, optimal operation costs that are related to the energy management strategy, unit commitment, economic dispatch and optimal power flow are investigated.

scholarly journals Control and Stability Analysis of Double Time-Delay Active Suspension Based on Particle Swarm Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Kaiwei Wu ◽  
Chuanbo Ren
Keyword(s):  
Particle Swarm Optimization ◽  
Time Delay ◽  
Feedback Control ◽  
Performance Optimization ◽  
Particle Swarm ◽  
Active Suspension ◽  
Swarm Optimization ◽  
Scanning Method ◽  
Delay Feedback Control ◽  
Double Time

With the application of an active control unit in the suspension system, the phenomenon of time delay has become an important factor in the control system. Aiming at the application of time-delay feedback control in vehicle active suspension systems, this paper has researched the dynamic behavior of semivehicle four-degree-of-freedom structure including an active suspension with double time-delay feedback control, focusing on analyzing the vibration response and stability of the main vibration system of the structure. The optimal objective function is established according to the amplitude-frequency characteristics of the system, and the optimal time-delay control parameters are obtained by using the particle swarm optimization algorithm. The stability for active suspension with double time-delay feedback control by frequency-domain scanning method is analyzed, and the simulation model of active suspension with double time delay based on feedback control is finally established. The simulation results show that the active suspension with double time-delay feedback control could reduce the body’s vertical vibration acceleration, pitch acceleration, and other indicators significantly, whether under harmonic excitation or random excitation. So, it is indicating that the active suspension with double time-delay feedback control has a better control effect in improving the ride comfort of the car, and it has important reference value for further research on suspension performance optimization.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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