Zero-Displacement-Error ITAE Standard Forms Based on Modified Particle Swarm Optimization

2015 ◽  
Vol 734 ◽  
pp. 539-542
Author(s):  
Ya Jian Xu ◽  
Yi Qun Yang
Keyword(s):  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Modified Particle Swarm Optimization ◽  
Displacement Error ◽  
Optimal Value ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Natural Selection Theory ◽  
Modified Pso

An optional method for calculating parameters of zero-displacement-error ITAE standard forms based on modified particle swarm algorithm is put forward. The modified PSO improved by random inertia weight and natural selection theory aim to overcome the disadvantage of algorithm such as easily trapping into local optimal solution and slow convergence in the late evolutionary. Experiments result shows that the modified algorithm can get a more accurate global optimal value and the performance of standard forms optimized by modified PSO is significantly improved.

Distribution System Restoration Based on Hybrid Particle Swarm

2013 ◽  
Vol 732-733 ◽  
pp. 662-668
Author(s):  
Li Zhi Tang ◽  
Jun Du ◽  
Kun Peng Xu ◽  
Xue Qing Qi
Keyword(s):  
Stochastic Optimization ◽  
Distribution System ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Hybrid Particle ◽  
Matrix Operations ◽  
Global Optimal ◽  
Loop Matrix ◽  
Network Fault

By the heuristic algorithm and particle swarm optimization algorithm combining hybrid particle swarm algorithm proposed combination of heuristic search and stochastic optimization,stochastic optimization process using a spanning tree and the loop matrix operations combined to ensure the system topology constraints to improve the efficiency of solution. The analysis shows that the proposed method calculation speed,easy to converge to the global optimal solution. It can effectively solve the problem of distribution network fault recovery.

Diagnosis for Vibration Fault of Steam Turbine Based on Modified Particle Swarm Optimization Support Vector Machine

2011 ◽  
Vol 128-129 ◽  
pp. 113-116 ◽  
Author(s):  
Zhi Biao Shi ◽  
Quan Gang Song ◽  
Ming Zhao Ma
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Steam Turbine ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization

Due to the influence of artificial factor and slow convergence of particle swarm algorithm (PSO) during parameters selection of support vector machine (SVM), this paper proposes a modified particle swarm optimization support vector machine (MPSO-SVM). A Steam turbine vibration fault diagnosis model was established and the failure data was used in fault diagnosis. The results of application show the model can get automatic optimization about the related parameters of support vector machine and achieve the ideal optimal solution globally. MPSO-SVM strategy is feasible and effective compared with traditional particle swarm optimization support vector machine (PSO-SVM) and genetic algorithm support vector machine (GA-SVM).

The Application of Quantum-Behaved Particle Swarm Algorithm in Intelligent Test Paper Generation

2013 ◽  
Vol 694-697 ◽  
pp. 2378-2382 ◽  
Author(s):  
Xin Ran Li
Keyword(s):  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Aggregation ◽  
Particle Swarm Algorithm ◽  
Test Paper ◽  
Inertia Weight ◽  
Local Optimal Solution ◽  
Swarm Algorithm ◽  
Low Efficiency

Aiming at solving the low efficiency and low quality of the existing test paper generation algorithm, this paper proposes an improved particle swarm algorithm, a new algorithm for intelligent test paper generation. Firstly, the paper conducts mathematically modeling based on item response theory. Secondly, in the new algorithm, the inertia weight is expressed as functions of particle evolution velocity and particle aggregation by defining particle evolution velocity and particle aggregation so that the inertia weight has adaptability. At the same time, slowly varying function is introduced to the traditional location updating formula so that the local optimal solution can be effectively overcome. Finally, simulation results show that compared with the quantum-behaved particle swarm algorithm, the proposed algorithm has better performance in success rate and composing efficiency.

Optimization of Neural Tree Based on an Improved Quantum Particle Swarm Optimization

2013 ◽  
Vol 475-476 ◽  
pp. 956-959 ◽  
Author(s):  
Hao Teng ◽  
Shu Hui Liu ◽  
Yue Hui Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization ◽  
Local Optimal Solution

In the model of flexible neural tree (FNT), parameters are usually optimized by particle swarm optimization algorithm (PSO). Because PSO has many shortcomings such as being easily trapped in local optimal solution and so on, an improved algorithm based on quantum-behaved particle swarm optimization (QPSO) is presented. It is combined with the factor of speed, gather and disturbance, so as to be used to optimize the parameters of FNT. This paper applies the improved quantum particle swarm optimization algorithm to the neural tree, and compares it with the standard particle swarm algorithm in the optimization of FNT. The result shows that the proposed algorithm is with a better expression, thus improves the performance of the FNT.

Infrared Image Contrast Enhancement Based on Modified Particle Swarm Optimization

2013 ◽  
Vol 760-762 ◽  
pp. 1389-1393
Author(s):  
Ren Tao Zhao ◽  
You Yu Wang ◽  
Hua De Li ◽  
Jun Tie
Keyword(s):  
Particle Swarm Optimization ◽  
Contrast Enhancement ◽  
Particle Swarm ◽  
Infrared Image ◽  
Optimal Solution ◽  
Image Contrast ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Image Contrast Enhancement ◽  
Modified Pso

Adaptive infrared image contrast enhancement is presented based on modified particle swarm optimization (PSO) and incomplete Beta Function. On the basis of traditional PSO, modified PSO integrates into the theory of Multi-Particle Swarm and evolution theory algorithm. By using separate search space optimal solution of multiple particles, the global search ability is improved. And in the iteration procedures, timely adjustment of acceleration coefficients is convenient for PSO to find the global optimal solution in the later iteration. Through infrared image simulation, experimental results show that the modified PSO is better than the standard PSO in computing speed and convergence.

Query Optimization Based on the Simulated Annealing and Particle Swarm Optimization

2012 ◽  
Vol 229-231 ◽  
pp. 1870-1873
Author(s):  
Ren Jie Song ◽  
Yan Wang
Keyword(s):  
Simulated Annealing ◽  
Query Optimization ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm ◽  
Optimization Method ◽  
Particle Swarm Algorithm ◽  
Algorithm Optimization ◽  
Optimal Value ◽  
Global Optimal ◽  
Annealing Algorithm

In order to allow the user to quickly and accurately search the required information, a query optimization method based on a simulated annealing and particle swarm hybrid algorithm is proposed. The basic idea is: the query population into two flat sub populations, a sub population by using simulated annealing algorithm optimization, another sub populations by using particle swarm algorithm optimization, comparison of two adaptive values, to find the global optimal value. The experimental results show that the mixed algorithm, can further improve the precision and recall of query optimization.

scholarly journals Improved Quantum-Behaved Particle Swarm Algorithm Based on Levy Flight

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Song Zheng ◽  
Xinwei Zhou ◽  
Xiaoqing Zheng ◽  
Ming Ge
Keyword(s):  
Particle Swarm ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Particle Swarm Algorithm ◽  
Lévy Flight ◽  
Levy Flight ◽  
Comparison Results ◽  
Local Optimal Solution ◽  
Quantum Behavior ◽  
Swarm Algorithm

To improve convergence speed and search accuracy, this paper proposes an improved quantum-behaved particle swarm optimization algorithm based on Levy flight. The improved algorithm reduces the probability of a local optimal solution through Levy flight and enhances the accuracy of the later search through a postsearch strategy. During the search process, the probability of quantum behavior is retained and the directivity of the particles is strengthened. According to the simulation comparison results, the improved quantum-behaved particle swarm algorithm exhibits faster convergence speed and higher accuracy.

scholarly journals The Modified Particle Swarm Optimization for a Special Case of the Assignment Problem: A Case Study in Chicken Transportation

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Naratip Supattananon ◽  
Raknoi Akararungruangkul
Keyword(s):  
Particle Swarm Optimization ◽  
Assignment Problem ◽  
Differential Evolution Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Swarm Optimization ◽  
Optimization Software ◽  
Modified Particle Swarm Optimization ◽  
Special Case ◽  
Modified Pso

This research aims at solving the special case of multistate assignment problem. The problem includes many special characteristics which are not normally included in the assignment problem. There are many types and conditions of vehicles included in the planning and different road conditions of traveling, which would have different effects on fuel consumption, which is the objective function of the study. The proposed problem is determined as a large and complicated problem making the optimization software unable to find an optimal solution within the proper time. Therefore, the researchers had developed a method for determining the optimal solution by using particle swarm optimization (PSO) in which the methods were developed for solving the proposed problem. This method is called the modified particle swarm optimization (modified PSO). The proposed method was tested with three groups of tested instances, i.e., small, medium, and large groups. The computational result shows that, in small-sized and medium-sized problems, the proposed method performed not significantly different from the optimization software, and in the large-sized problems, the modified PSO method gave 3.61% lower cost than the cost generated from best solution generated from optimization software within 72 hours and it gave 11.03% better solution than that of the best existing heuristics published so far (differential evolution algorithm).

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Plant Medicine ◽  
Suitability Index ◽  
Sensing Model ◽  
Local Optimal Solution ◽  
Global Optimal

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>

A Modified PSO-BP Algorithm in Hydraulic System Fault Diagnosis Application

2012 ◽  
Vol 182-183 ◽  
pp. 1145-1148 ◽  
Author(s):  
Zeng Shou Dong ◽  
Xiao Yu Zhang ◽  
Jian Chao Zeng
Keyword(s):  
Fault Diagnosis ◽  
Convergence Rate ◽  
Hydraulic System ◽  
Particle Swarm ◽  
Bp Algorithm ◽  
Particle Swarm Algorithm ◽  
Bp Network ◽  
Learning Factor ◽  
Swarm Algorithm ◽  
Modified Pso

BP neural network for failure pattern recognition has been used in hydraulic system fault diagnosis.However, its convergence rate is relatively small and always trapped at the local minima. So a new modified PSO-BP hydraulic system fault diagnosis method was proposed,which combined the respective advantages of particle swarm algorithm and BP algorithm. Firstly, the inertia weight and learning factor of the standard particle swarm algorithm was improved, then BP neural network’s weights and thresholds were optimized by modified PSO algorithm. BP network performance was ameliorated. The simulation results showed that this method improved the convergence rate of the BP network, and it could reduce the diagnostic errors.

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