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.

Optimizing the Operation of Distributed Generation Using Genetic Algorithms

2013 ◽  
Vol 732-733 ◽  
pp. 691-696
Author(s):  
Jun Du ◽  
Dong Yan Zhao ◽  
Yu Bo Wang
Keyword(s):  
Genetic Algorithms ◽  
Stochastic Optimization ◽  
Distributed Generation ◽  
Profit Maximization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Matrix Operations ◽  
Time Period ◽  
Loop Matrix

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. Development of distributed generation units, along with concerns raised over the security of supply has prompted many customers to consider the installation of their own local capacity for generating electricity (and heat). This paper proposes a methodology for optimizing the operation of a portfolio of distributed units, based on profit maximization using genetic algorithms. The method is tested on a set of distributed units, demonstrating the ability to find good solutions in an acceptable time period.

scholarly journals Parallel Hybrid Particle Swarm Algorithm for Workshop Scheduling Based on Spark

Algorithms ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 262
Author(s):  
Tianhua Zheng ◽  
Jiabin Wang ◽  
Yuxiang Cai
Keyword(s):  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Hybrid Particle ◽  
Algorithm Efficiency ◽  
Parallel Hybrid ◽  
Swarm Algorithm ◽  
Mass Assembly ◽  
Scheduling Plan ◽  
Algorithm Parallelization

In hybrid mixed-flow workshop scheduling, there are problems such as mass production, mass manufacturing, mass assembly and mass synthesis of products. In order to solve these problems, combined with the Spark platform, a hybrid particle swarm algorithm that will be parallelized is proposed. Compared with the existing intelligent algorithms, the parallel hybrid particle swarm algorithm is more conducive to the realization of the global optimal solution. In the loader manufacturing workshop, the optimization goal is to minimize the maximum completion time and a parallelized hybrid particle swarm algorithm is used. The results show that in the case of relatively large batches, the parallel hybrid particle swarm algorithm can effectively obtain the scheduling plan and avoid falling into the local optimal solution. Compared with algorithm serialization, algorithm parallelization improves algorithm efficiency by 2–4 times. The larger the batches, the more obvious the algorithm parallelization improves computational efficiency.

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.

scholarly journals Parameters Optimization and Application of SVM Based on PCA-Particle Swarm Algorithm

2019 ◽  
pp. 325-330 ◽  
Author(s):  
Qingmi Yang
Keyword(s):  
Parameter Optimization ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Principal Component ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Particle Swarm Algorithm

The parameter optimization of Support Vector Machine (SVM) has been a hot research direction. To improve the optimization rate and classification performance of SVM, the Principal Component Analysis (PCA) - Particle Swarm Optimization (PSO) algorithm was used to optimize the penalty parameters and kernel parameters of SVM. PSO which is to find the optimal solution through continuous iteration combined with PCA that eliminates linear redundancy between data, effectively enhance the generalization ability of the model, reduce the optimization time of parameters, and improve the recognition accuracy. The simulation comparison experiments on 6 UCI datasets illustrate that the excellent performance of the PCA-PSO-SVM model. The results show that the proposed algorithm has higher recognition accuracy and better recognition rate than simple PSO algorithm in the parameter optimization of SVM. It is an effective parameter optimization method.

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