A Review of Particle Swarm Optimization

Jain, N. K.; Nangia, Uma; Jain, Jyoti

doi:10.1007/s40031-018-0323-y

Cited by 156 publications

(63 citation statements)

References 37 publications

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“…The membership degree is calculated according to Formula (14). The membership degree matrix can be described as: (18) where Pi is the membership degree of the sample Xi. The state category corresponding to the maximum value of Pi is the state category of the sample Xi.…”

Section: Results Analysismentioning

confidence: 99%

A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory

Shang

Zheng

et al. 2019

Energies

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Power transformers are important equipment in power systems and their reliability directly concerns the safety of power networks. Dissolved gas analysis (DGA) has shown great potential for detecting the incipient fault of oil-filled power transformers. In order to solve the misdiagnosis problems of traditional fault diagnosis approaches, a novel fault diagnosis method based on hypersphere multiclass support vector machine (HMSVM) and Dempster–Shafer (D–S) Evidence Theory (DET) is proposed. Firstly, proper gas dissolved in oil is selected as the fault characteristic of power transformers. Secondly, HMSVM is employed to diagnose transformer fault with selected characteristics. Then, particle swarm optimization (PSO) is utilized for parameter optimization. Finally, DET is introduced to fuse three different fault diagnosis methods together, including HMSVM, hybrid immune algorithm (HIA), and kernel extreme learning machine (KELM). To avoid the high conflict between different evidences, in this paper, a weight coefficient is introduced for the correction of fusion results. Results indicate that the fault diagnosis based on HMSVM has the highest probability to identify transformer faults among three artificial intelligent approaches. In addition, the improved D–S evidence theory (IDET) combines the advantages of each diagnosis method and promotes fault diagnosis accuracy.

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Section: Results Analysismentioning

confidence: 99%

A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory

Shang

Zheng

et al. 2019

Energies

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“…All the optimal positions of all particles is the global optimal position pgD. The equation and parameter of particle movement are as follows (Shi and Eberhart, 1998, Jain et al, 2018). ViDj+1=ωViDj+c1r1false(pitaliciDj-xitaliciDjfalse)+c2r2false(pitalicgDj-xitaliciDjfalse)xiDj+1=xiDj+viDj+1where i stands for the particle; j the current iteration amount; d the particle dimension; xiDj and viDj are the velocity and position in the j iteration; non-negative constant …”

Section: Methodsmentioning

confidence: 99%

Prediction model of PSO-BP neural network on coliform amount in special food

Deng

Xiao

et al. 2019

Saudi Journal of Biological Sciences

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Special food safety supervision by means of intelligent models and methods is of great significance for the health of local people and tourists. Models like BP neural network have the problems of low accuracy and poor robustness in food safety prediction. So, firstly, the principal component analysis was used to extract the key factors that influenced the amount of coliform communities, which was applied to reduce the dimension of this model as the input variable of BP neural network. Secondly, both the particle swarm optimization (PSO) and BP neural network were implemented to optimize initial weights and threshold to obtain the optimal parameter, and a model was constructed to predict the amount of coliform bacteria in Dai Special Snacks, Sa pie, based on PSO-BP neural network model. Finally, the predicted value of the model is verified. The results show that MSE is 0.0097, MAPE is 0.3198 and MAE is 0.0079, respectively. It was clear that PSO-BP model was better accuracy and robustness. That means, this model can effectively predict the amount of coliform. The research has important guiding significance for the quality and the production of Sa pie.

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“…According to the PSO algorithm theory [30,31,32], a population size is 50 and randomly initialized in the limited beam surface. The maximum iteration number is 100.…”

Section: Establishing the Multi-objective Particle Swarm Optimizatmentioning

confidence: 99%

Multi-Objective Particle Swarm Optimization of Sensor Distribution Scheme with Consideration of the Accuracy and the Robustness for Deformation Reconstruction

Zhao

Bao

Xue

et al. 2019

Sensors

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For the inverse finite element method (iFEM), an inappropriate scheme of strain senor distribution would cause severe degradation of the deformation reconstruction accuracy. The robustness of the strain–displacement transfer relationship and the accuracy of reconstruction displacement are the two key factors of reconstruction accuracy. Previous research studies have been focused on single-objective optimization for the robustness of the strain–displacement transfer relationship. However, researchers found that it was difficult to reach a mutual balance between robustness and accuracy using single-objective optimization. In order to solve this problem, a bi-objective optimal model for the scheme of sensor distribution was proposed for this paper, where multi-objective particle swarm optimization (MOPSO) was employed to optimize the robustness and the accuracy. Initially, a hollow circular beam subjected to various loads was used as a case to perform the static analysis. Next, the optimization model was established and two different schemes of strain sensor were obtained correspondingly. Finally, the proposed schemes were successfully implemented in both the simulation calculation and the experiment test. It was found that the results from the proposed optimization model in this paper proved to be a promising tool for the selection of the scheme of strain sensor distribution.

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A Review of Particle Swarm Optimization

Cited by 156 publications

References 37 publications

A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory

A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory

Prediction model of PSO-BP neural network on coliform amount in special food

Multi-Objective Particle Swarm Optimization of Sensor Distribution Scheme with Consideration of the Accuracy and the Robustness for Deformation Reconstruction

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