Application of Substructure Discovery Based PSO Algorithm in Distribution Network Economic Evaluation

2012 ◽  
Vol 433-440 ◽  
pp. 7054-7059
Author(s):  
Xiao Hui Zhang ◽  
Zhi Gang Lu
Keyword(s):  
Economic Evaluation ◽  
Distribution Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Evaluation Index System ◽  
Practical Significance ◽  
Particle Swarm Algorithm ◽  
Local Optimum ◽  
Algorithm Optimization ◽  
Search Capability

In this paper, a hierarchical economic evaluation index system for distribution network is established. The various indicators of the distribution network and the correlation between the distribution network in order to form is proposed. Substructure discovery algorithm is used to data mining. To avoid falling into local optimum, efficient global search capability of particle swarm is applied to substructure algorithm optimization. Finally, an example demonstrates the reasonablity and effectivity of the substructure found algorithm applied into distribution network economic evaluation. Optimization speed with particle swarm algorithm is fast. This study have practical significance to network planning.

A Quantum-Behaved Particle Swarm Algorithm Combined with Chaotic Mutation

2012 ◽  
Vol 605-607 ◽  
pp. 2442-2446
Author(s):  
Xin Ran Li ◽  
Yan Xia Jin
Keyword(s):  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Local Optimum ◽  
Convergence Problem ◽  
Early Maturity ◽  
Classical Function ◽  
Function Test ◽  
Quantum Behavior ◽  
Swarm Algorithm

The article puts forward an improved PSO algorithm based on the quantum behavior——CMQPSO algorithm to improve premature convergence problem in particle swarm algorithm. The new algorithm first adopts Tent mapping initialization of particle swarm, searches each particle chaos, and strengthens the diversity of searching. Secondly, a method of effective judgment of early stagnation is embedded in the algorithm. Once the early maturity is retrieved, the algorithm mutates particles to jump out of the local optimum particle according to the structure mutation so as to reduce invalid iteration. The calculation of classical function test shows that the improved algorithm is superior to classical PSO algorithm and quantum-behaved PSO algorithm.

Integration Particle Swarm Algorithm and its Application in Neural Network

2014 ◽  
Vol 543-547 ◽  
pp. 2133-2136
Author(s):  
Jun Pan ◽  
Xu Cao
Keyword(s):  
Neural Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Algorithm Optimization ◽  
Bp Network ◽  
Node Number ◽  
The Neural Network ◽  
Swarm Algorithm ◽  
Hidden Layer

This paper puts forward a kind of evolutionary algorithm and the neural network combining with the new method of optimization of hidden layer nodes number of particle swarm algorithm of neural network. The BP neural network technology is a kind of more mature neural network method, but there are easy to fall into local minimum value, unable to accurately determine the number of hidden layer nodes of the network, the disadvantages such as slow convergence speed. This paper puts forward the optimization with hidden node number of particle swarm neural network (HPSO neural network) is the hidden layer of BP network node number as a particle swarm optimization (PSO) algorithm is an important optimization goal, network of hidden layer nodes and the number of each BP network weights and closed value together, common as particle swarm algorithm optimization goal.

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.

scholarly journals Application of Strongly Constrained Space Particle Swarm Optimization to Optimal Operation of a Reservoir System

2018 ◽  
Vol 10 (12) ◽  
pp. 4445 ◽  
Author(s):  
Lejun Ma ◽  
Huan Wang ◽  
Baohong Lu ◽  
Changjun Qi
Keyword(s):  
Particle Swarm Optimization ◽  
Computational Efficiency ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Operation ◽  
Computational Time ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Swarm Algorithm ◽  
Low Efficiency

In view of the low efficiency of the particle swarm algorithm under multiple constraints of reservoir optimal operation, this paper introduces a particle swarm algorithm based on strongly constrained space. In the process of particle optimization, the algorithm eliminates the infeasible region that violates the water balance in order to reduce the influence of the unfeasible region on the particle evolution. In order to verify the effectiveness of the algorithm, it is applied to the calculation of reservoir optimal operation. Finally, this method is compared with the calculation results of the dynamic programming (DP) and particle swarm optimization (PSO) algorithm. The results show that: (1) the average computational time of strongly constrained particle swarm optimization (SCPSO) can be thought of as the same as the PSO algorithm and lesser than the DP algorithm under similar optimal value; and (2) the SCPSO algorithm has good performance in terms of finding near-optimal solutions, computational efficiency, and stability of optimization results. SCPSO not only improves the efficiency of particle evolution, but also avoids excessive improvement and affects the computational efficiency of the algorithm, which provides a convenient way for particle swarm optimization in reservoir optimal operation.

An Improved Particle Swarm Optimization Algorithm with Adaptive Inertia Weights

2019 ◽  
Vol 18 (03) ◽  
pp. 833-866 ◽  
Author(s):  
Mi Li ◽  
Huan Chen ◽  
Xiaodong Wang ◽  
Ning Zhong ◽  
Shengfu Lu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Random Mutation ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Fitness Value ◽  
The One ◽  
Individual Particles

The particle swarm optimization (PSO) algorithm is simple to implement and converges quickly, but it easily falls into a local optimum; on the one hand, it lacks the ability to balance global exploration and local exploitation of the population, and on the other hand, the population lacks diversity. To solve these problems, this paper proposes an improved adaptive inertia weight particle swarm optimization (AIWPSO) algorithm. The AIWPSO algorithm includes two strategies: (1) An inertia weight adjustment method based on the optimal fitness value of individual particles is proposed, so that different particles have different inertia weights. This method increases the diversity of inertia weights and is conducive to balancing the capabilities of global exploration and local exploitation. (2) A mutation threshold is used to determine which particles need to be mutated. This method compensates for the inaccuracy of random mutation, effectively increasing the diversity of the population. To evaluate the performance of the proposed AIWPSO algorithm, benchmark functions are used for testing. The results show that AIWPSO achieves satisfactory results compared with those of other PSO algorithms. This outcome shows that the AIWPSO algorithm is conducive to balancing the abilities of the global exploration and local exploitation of the population, while increasing the diversity of the population, thereby significantly improving the optimization ability of the PSO algorithm.

scholarly journals A Mutual Technique for Reconfiguration of Feeder Network by Optimal Locating and Sizing of Distribution Generator

2019 ◽  
Vol 63 (4) ◽  
pp. 320-331
Author(s):  
Kothuri Ramakrishna ◽  
Basavaraja Banakara
Keyword(s):  
Radial Distribution ◽  
Reactive Power ◽  
Electrical Power ◽  
Distribution Network ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Optimum Location ◽  
Algorithm Optimization ◽  
Electrical Power System ◽  
Comparison Results

Common technique has been discussed in this paper for the reconfiguration of feeder network by optimal location and measuring of Distribution Generator (DG) in electrical power system. The consolidated execution of both Biography Based Optimization (BBO) and Particle Swarm Optimization (PSO) strategies are the curiosity of the proposed strategy. The optimization techniques are utilized for optimizing the optimum location and DG capacity for radial distribution network. The BBO algorithm requires radial distribution network voltage, real and reactive power for deciding the optimum location and capacity of the DG. Here, the input parameters of BBO are classified into sub parameters and permitted as the PSO algorithm optimization process. The PSO develops the sub solution with the assistance of sub parameters by issue synthesis. For identifying the optimum location and capacity of DG the BBO movement and mutation process is applied for the sub solution of PSO. At that point the proposed mutual technique is actualized in the MATLAB/simulink platform and by contrasting it with the BBO and PSO systems the effectiveness is scrutinized. The comparison results demonstrate the predominance of the proposed approach and affirm its capability to comprehend the issue.

scholarly journals Improved Adaptive Holonic Particle Swarm Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-22 ◽  
Author(s):  
Hao Li ◽  
Hongbin Jin ◽  
Hanzhong Wang ◽  
Yanyan Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Standard Test ◽  
Local Optimum ◽  
Swarm Optimization ◽  
State Switching ◽  
Grouping Strategy ◽  
Number Of Iterations

For the first time , the Holonic Particle Swarm Optimization (HPSO ) algorithm applies multiagent theory about the improvement in the PSO algorithm and achieved good results. In order to further improve the performance of the algorithm, this paper proposes an improved Adaptive Holonic Particle Swarm Optimization (AHPSO) algorithm. Firstly, a brief review of the HPSO algorithm is carried out, and the HPSO algorithm can be further studied in three aspects: grouping strategy, iteration number setting, and state switching discrimination. The HPSO algorithm uses an approximately uniform grouping strategy that is the simplest but does not consider the connections between particles. And if the particles with larger or smaller differences are grouped together in different search stages, the search efficiency will be improved. Therefore, this paper proposes a grouping strategy based on information entropy and system clustering and combines two grouping strategies with corresponding search methods. The performance of the HPSO algorithm depends on the setting of the number of iterations. If it is too small, it is difficult to search for the optimal and it wastes so many computing resources. Therefore, this paper constructs an adaptive termination condition that causes the particles to terminate spontaneously after convergence. The HPSO algorithm only performs a conversion from extensive search to exact search and still has the potential to fall into local optimum. This paper proposes a state switching condition to improve the probability that the algorithm jumps out of the local optimum. Finally, AHPSO and HPSO are compared by using 22 groups of standard test functions. AHPSO is faster in convergence than HPSO, and the number of iterations of AHPSO convergence is employed in HPSO. At this point, there exists a large gap between HPSO and the optimal solution, i.e., AHPSO can have better algorithm efficiency without setting the number of iterations.

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