scholarly journals Automatic Circuit Design and Optimization using Modified PSO Algorithm

2016 ◽  
Vol 9 (4) ◽  
pp. 192-197 ◽  
Author(s):  
Subhash Patel ◽  
◽  
Rajesh A. Thakker ◽  
2011 ◽  
Vol 474-476 ◽  
pp. 1093-1098 ◽  
Author(s):  
Xue Song Yan ◽  
Qing Hua Wu ◽  
Cheng Yu Hu ◽  
Qing Zhong Liang

This work investigates the application of Particle Swarm Optimization (PSO) algorithms in the field of evolutionary electronics. PSO was developed under the inspiration of behavior laws of bird flocks, fish schools and human communities. PSO achieves its optimum solution by starting from a group of random solution and then searching repeatedly. We propose the new means for designing electronic circuits and introduce the modified PSO algorithm. For the case studies this means has proved to be efficient, experiments show that we have better results.


2021 ◽  
Vol 13 (8) ◽  
pp. 4246
Author(s):  
Shih-Wei Yen ◽  
Wei-Hsin Chen ◽  
Jo-Shu Chang ◽  
Chun-Fong Eng ◽  
Salman Raza Naqvi ◽  
...  

This study investigated the kinetics of isothermal torrefaction of sorghum distilled residue (SDR), the main byproduct of the sorghum liquor-making process. The samples chosen were torrefied isothermally at five different temperatures under a nitrogen atmosphere in a thermogravimetric analyzer. Afterward, two different kinetic methods, the traditional model-free approach, and a two-step parallel reaction (TPR) kinetic model, were used to obtain the torrefaction kinetics of SDR. With the acquired 92–97% fit quality, which is the degree of similarity between calculated and real torrefaction curves, the traditional method approached using the Arrhenius equation showed a poor ability on kinetics prediction, whereas the TPR kinetic model optimized by the particle swarm optimization (PSO) algorithm showed that all the fit qualities are as high as 99%. The results suggest that PSO can simulate the actual torrefaction kinetics more accurately than the traditional kinetics approach. Moreover, the PSO method can be further employed for simulating the weight changes of reaction intermediates throughout the process. This computational method could be used as a powerful tool for industrial design and optimization in the biochar manufacturing process.


2021 ◽  
Vol 50 (3) ◽  
pp. 546-557
Author(s):  
J. KUMARNATH ◽  
K. BATRI

Due to huge size of the data and quick transmission of data between the nodes present in the optical network, a condition of network traffic is created among the nodes of the network. This issue of traffic can be overcome by employing numerous traffic grooming techniques. In this research paper, the best suitable shortest path is determined by the multi objective modified PSO algorithm and an innovative visibility graph based Iterative Hungarian Traffic grooming algorithm is implemented to reduce the blocking ratio through improving the allocation of bandwidth between the users. Then finally the performance analysis is carried out by means of performance measures such as traffic throughput, transceivers count, average propagation delay, blocking ratio, and success ratio. It can be inferred that the proposed work obtains enhanced outcomes when compared to the other existing techniques.


Author(s):  
Shafiullah Khan ◽  
Shiyou Yang ◽  
Obaid Ur Rehman

Purpose The aim of this paper is to explore the potential of particle swarm optimization (PSO) algorithm to solve an electromagnetic inverse problem. Design/methodology/approach A modified PSO algorithm is designed. Findings The modified PSO algorithm is a more stable, robust and efficient global optimizer for solving the well-known benchmark optimization problems. The new mutation approach preserves the diversity of the population, whereas the proposed dynamic and adaptive parameters maintain a good balance between the exploration and exploitation searches. The numerically experimental results of two case studies demonstrate the merits of the proposed algorithm. Originality/value Some improvements, such as the design of a new global mutation mechanism and introducing a novel strategy for learning and control parameters, are proposed.


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