A Survey of Algorithms, Applications and Trends for Particle Swarm Optimization

Fang, Jingzhong; Liu, Weibo; Chen, Linwei; Lauria, Stanislao; Miron, Alina; Liu, Xiaohui

doi:10.53941/ijndi0201002

Cited by 28 publications

(6 citation statements)

References 145 publications

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“…This value is shared and updated across all particles in the swarm. The PSO formula to update the velocity and position of particles can be broken down into the following [16]:…”

Section: Background and Explanationmentioning

confidence: 99%

Optimizing Hyperparameters for Customer Churn Prediction with PSO-Enhanced Composite Deep Learning Techniques

Sedighimanesh,

Gheisari

2024

Preprint

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Background: In today’s competitive market, predicting customer churn with high accuracy is crucial for enterprises to maintain growth and profitability. Traditional predictive models often lack in accuracy due to the complexity of customer behavior.Objective: This research aims to improve the accuracy of predicting customer churn by utilizing the Particle Swarm Optimization (PSO) algorithm for optimizing the hyperparameters of a composite deep learning model. The performance of this enhanced model is evaluated against traditional models such as LSRM_GRU, LSTM, GRU, and CNN_LSTM to demonstrate the effectiveness of PSO in hyperparameter tuning.Methods: A composite deep learning approach was employed, integrating various neural network architectures to leverage their strengths in modeling complex customer interactions. The PSO algorithm was used to optimize the model’s hyperparameters. Customer transaction and interaction data from different business operations served as the dataset for testing and analyzing the model’s performance. Evaluation metrics including accuracy, precision, recall, F1 score, and ROC AUC were utilized for a detailed comparison with established models.Findings: The PSO-enhanced composite deep learning model showed superior performance across all metrics, significantly outperforming the LSRM_GRU, LSTM, GRU, and CNN_LSTM models. Notably, improvements in ROC AUC and F1 score highlight the robustness and balanced precision-recall trade-off of the proposed model, demonstrating its effectiveness in identifying potential churners.Conclusion: The integration of PSO for hyperparameter optimization in composite deep learning models for customer churn prediction has proven to significantly enhance predictive accuracy and performance metrics over conventional models. This underscores the potential of evolutionary algorithms in improving deep learning applications. Future research should explore the scalability of this approach across various sectors and further refine predictive accuracy with evolving customer data.

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“…This value is shared and updated across all particles in the swarm. The PSO formula to update the velocity and position of particles can be broken down into the following [16]:…”

Section: Background and Explanationmentioning

confidence: 99%

Optimizing Hyperparameters for Customer Churn Prediction with PSO-Enhanced Composite Deep Learning Techniques

Sedighimanesh,

Gheisari

2024

Preprint

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“…Since the original PSO algorithm was proposed, numerous variants have been developed to address its defciencies, such as local trapping and premature convergence. Te schemes of the variants include introducing diferent strategies, modifying parameter settings, and combining them with other algorithms, which are utilized to enhance the performance and convergence speed of the PSO algorithm [42,43].…”

Section: Pso-awdv Algorithmmentioning

confidence: 99%

Design of PMSM Dual-Loop Control Systems Integrating LADRC and PI Controllers via an Improved PSO Algorithm

Song,

Wang,

2024

International Transactions on Electrical Energy Systems

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This paper is concerned with the design of a dual-loop control system for permanent magnet synchronous motor (PMSM). An improved linear extended state observer (LESO) with excellent estimation capability is employed to develop an improved linear active disturbance rejection control (LADRC) suitable for PMSM speed regulation, achieving outstanding disturbance suppression in PMSM speed control. The use of an internal model control scheme to initialize the parameters of the proportional-integral- (PI-) based current controller simplifies the search space of the control system parameter optimization. An improved particle swarm optimization (PSO) algorithm is applied to optimize the controller parameters, thereby enhancing the overall system performance. Finally, through a series of simulations and experiments, we validate that our proposed controller exhibits superior performance compared to some other control methods.

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“…Note that the decay rate of the closed-loop system (10) is an important performance index in evaluating the control performance. To further improve the algorithm's feasibility and reduce conservatism, the AUB and the decay rate can be optimized jointly by using the well-known particleswarm-optimization algorithm [3], [5], [7], [55].…”

Section: Summing Up Both Sides Of the Above Inequality Frommentioning

confidence: 99%

Ultimately Bounded PID Control for T–S Fuzzy Systems Under FlexRay Communication Protocol

Wang

Zou

et al. 2023

IEEE Trans. Fuzzy Syst.

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This paper investigates the ultimately bounded proportional-integral-derivative (PID) control problem for a class of discrete-time Takagi-Sugeno fuzzy systems subject to unknown-but-bounded noises and protocol constraints. The signal transmissions from sensors to the remote controller are realized via a communication network, where the FlexRay protocol is employed to flexibly schedule the information exchange. Such FlexRay protocol is characterized by both the time-and eventtriggered mechanisms which are conducted in a cyclic manner. By using a piecewise approach, the measurement outputs affected by the FlexRay protocol are established based on a switching model. Then, a fuzzy PID controller is proposed with a concise and realizable structure. To evaluate the performance of the controlled system, a special time-sequence is introduced that accounts for the behavior of the FlexRay protocol. Subsequently, a general framework is obtained to verify the boundedness of the closed-loop system and then the controller gains are designed by minimizing the bound of the concerned variables. In the end, a simulation study is conducted to validate the effectiveness of the developed control scheme.

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A Survey of Algorithms, Applications and Trends for Particle Swarm Optimization

Cited by 28 publications

References 145 publications

Optimizing Hyperparameters for Customer Churn Prediction with PSO-Enhanced Composite Deep Learning Techniques

Optimizing Hyperparameters for Customer Churn Prediction with PSO-Enhanced Composite Deep Learning Techniques

Design of PMSM Dual-Loop Control Systems Integrating LADRC and PI Controllers via an Improved PSO Algorithm

Ultimately Bounded PID Control for T–S Fuzzy Systems Under FlexRay Communication Protocol

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