scholarly journals Conjugate Direction Particle Swarm Optimization Based Approach to Determine Kinetic Parameters from Part of Adiabatic Data

2020 ◽  
Vol 11 (4) ◽  
pp. 82-88
Author(s):  
Xiao-Qiao Zhao ◽  
◽  
Hao Wang ◽  
Wen-Qian Wu ◽  
Wang-Hua Chen ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Kinetic Parameters ◽  
Particle Swarm ◽  
Local Extremum ◽  
Experimental Curve ◽  
Swarm Optimization ◽  
Detection Range ◽  
Conjugate Direction ◽  
Rise Rate ◽  
Stochastic Optimization Algorithm

Due to the limited detection range of the adiabatic equipment, it is difficult to get complete experimental curve of some materials and calculate the kinetic parameters. In this work, the conjugate direction particle swarm optimization (CDPSO) approach, as a global stochastic optimization algorithm, is proposed to estimate the kinetic parameters and complete experimental curve from part of adiabatic calorimetric data. This algorithm combines the conjugate direction algorithm (CD) which has the ability to escape from the local extremum and the global optimization ability of the particle swarm optimization (PSO) which finds the globally optimal solutions. One case was used to verify this method: 20% DTBP in toluene decompositions under adiabatic conditions. Comparing the experimental and calculated complete temperature curve, the accuracy of the fitted kinetic parameters calculated by no less than 70% temperature rise rate proportion of data is verified. The value of TD24 is well-deviated even used 10% proportion of data. The case reasonably proves the effectiveness of CDPSO algorithm in the estimation of kinetic parameters from part of adiabatic data.

scholarly journals An Enhanced Segment Particle Swarm Optimization Algorithm for Kinetic Parameters Estimation of the Main Metabolic Model of Escherichia Coli

Processes ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 963
Author(s):  
Mohammed Adam Kunna ◽  
Tuty Asmawaty Abdul Kadir ◽  
Muhammad Akmal Remli ◽  
Noorlin Mohd Ali ◽  
Kohbalan Moorthy ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Kinetic Parameters ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Metabolic Model ◽  
Parameters Estimation ◽  
Swarm Optimization ◽  
Distance Minimization ◽  
Kinetic Parameters Estimation

Building a biologic model that describes the behavior of a cell in biologic systems is aimed at understanding the physiology of the cell, predicting the production of enzymes and metabolites, and providing a suitable data that is valid for bio-products. In addition, building a kinetic model requires the estimation of the kinetic parameters, but kinetic parameters estimation in kinetic modeling is a difficult task due to the nonlinearity of the model. As a result, kinetic parameters are mostly reported or estimated from different laboratories in different conditions and time consumption. Hence, based on the aforementioned problems, the optimization algorithm methods played an important role in addressing these problems. In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. This method was proposed to increase the exploration and the exploitation of the Segment Particle Swarm Optimization algorithm (Se-PSO). The main metabolic model of E. coli was used as a benchmark which contained 172 kinetic parameters distributed in five pathways. Seven kinetic parameters were well estimated based on the distance minimization between the simulation and the experimental results. The results revealed that the proposed method had the ability to deal with kinetic parameters estimation in terms of time consumption and distance minimization.

scholarly journals Prediction of Evolution and Development Trend in Sports Industry Cluster Based on Particle Swarm Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Rui Cong ◽  
Hailong Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Development Trend ◽  
Industrial Clusters ◽  
Economic Benefits ◽  
Local Extremum ◽  
Industry Cluster ◽  
Swarm Optimization ◽  
Sports Industry

Sports industry cluster refers to the economic phenomenon that sports related enterprises gather in a large number in a specific area. For the sports enterprises in the cluster, they can obtain huge competitive advantages through enterprise agglomeration, thus obtaining better development and rich economic benefits. The optimization of particle swarm optimization is interlinked with the agglomeration of industrial clusters. Therefore, in view of the limitation of the standard particle swarm optimization (PSO) algorithm, an improved particle swarm optimization algorithm-diaphragm particle swarm optimization (D-PSO) was proposed and used to simulate the formation of sports industry clusters. D-PSO introduces the cell membrane processing mechanism of the biological system into the PSO algorithm, which improves the ability of the PSO algorithm to get rid of local extremum points. The competitiveness value of the sports industry cluster is the value of the objective function solved by the D-PSO algorithm. The geographical coordinates of the industrial cluster were the locations in the particle search space of the D-PSO algorithm. The D-PSO algorithm is used to simulate the aggregation process of enterprises in the cluster. Compared with the standard PSO, the D-PSO algorithm has better convergence performance and optimal rate. The results of case analysis show that the proposed method can effectively predict the development trend of sports industrial clusters.

scholarly journals An enhanced particle swarm optimization algorithm

2019 ◽  
Vol 9 (6) ◽  
pp. 4904
Author(s):  
Wameedh Riyadh Abdul-Adheem
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test Results ◽  
Swarm Optimization ◽  
The Social ◽  
Basic Particle ◽  
Stochastic Optimization Algorithm ◽  
Enhanced Particle Swarm Optimization

<p>In this paper, an enhanced stochastic optimization algorithm based on the basic Particle Swarm Optimization (PSO) algorithm is proposed. The basic PSO algorithm is built on the activities of the social feeding of some animals. Its parameters may influence the solution considerably. Moreover, it has a couple of weaknesses, for example, convergence speed and premature convergence. As a way out of the shortcomings of the basic PSO, several enhanced methods for updating the velocity such as Exponential Decay Inertia Weight (EDIW) are proposed in this work to construct an Enhanced PSO (EPSO) algorithm. The suggested algorithm is numerically simulated established on five benchmark functions with regards to the basic PSO approaches. The performance of the EPSO algorithm is analyzed and discussed based on the test results.</p>

An Improved Particle Swarm Optimization Algorithm with Invasive Weed

2012 ◽  
Vol 621 ◽  
pp. 356-359 ◽  
Author(s):  
Huan Zhao ◽  
Jiang Long Yu ◽  
Arash Tahmasebi ◽  
Pei Hong Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Local Optimum ◽  
Exploration And Exploitation ◽  
Invasive Weed ◽  
Swarm Optimization ◽  
Test Function ◽  
Stochastic Optimization Algorithm

This paper presents a hybrid algorithm based on invasive weed optimization (IWO) and particle swarm optimization (PSO), named IW-PSO. IWO is a relatively novel numerical stochastic optimization algorithm. By incorporating the reproduction and spatial dispersal of IWO into the traditional PSO, exploration and exploitation of the PSO can be enhanced and well balanced to achieve better performance. In a set of 15 test function problem, the parameters of IW-PSO were analyzed and selected, and the computational results show that IW-PSO can effectively obtain higher quality solutions so as to avoid being trapped in local optimum, comparing with PSO and IWO.

Estimation of kinetic parameters of an anaerobic digestion model using particle swarm optimization

2017 ◽  
Vol 120 ◽  
pp. 25-32 ◽  
Author(s):  
Jixiang Yang ◽  
Lunhui Lu ◽  
Wenjuan Ouyang ◽  
Yao Gou ◽  
Youpeng Chen ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Anaerobic Digestion ◽  
Kinetic Parameters ◽  
Particle Swarm ◽  
Swarm Optimization

scholarly journals Application of Improved Particle Swarm Optimization in Vehicle Crashworthiness

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Dawei Gao ◽  
Xiangyang Li ◽  
Haifeng Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Extremum ◽  
Local Optimum ◽  
Global Optimality ◽  
Critical Position ◽  
Swarm Optimization ◽  
Improved Pso

In the optimization design process, particle swarm optimization (PSO) is limited by its slow convergence, low precision, and tendency to easily fall into the local extremum. These limitations make degradation inevitable in the evolution process and cause failure of finding the global optimum results. In this paper, based on chaos idea, the PSO algorithm is improved by adaptively adjusting parameters r1 and r2. The improved PSO is verified by four standard mathematical test functions. The results prove that the improved algorithm exhibits excellent convergence speed, global search ability, and stability in the optimization process, which jumps out of the local optimum and achieves global optimality due to the randomness, regularity, and ergodicity of chaotic thought. At last, the improved PSO algorithm is applied to vehicle crash research and is used to carry out the multiobjective optimization based on an approximate model. Compared with the results before the improvement, the improved PSO algorithm is remarkable in the collision index, which includes vehicle acceleration, critical position intrusion, and vehicle mass. In summary, the improved PSO algorithm has excellent optimization effects on vehicle collision.

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