Particle swarm optimization with chaotic velocity clamping (CVC-PSO)

Mojarrad, Mohammad; Ayubi, Peyman

doi:10.1109/ikt.2015.7288811

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…Barani et al [36] proposed a new improved Particle Swarm Optimization (PSO) combined with Chaotic Cellular Automata (CCA). Similarly, Mojarrad and Ayubi [37] proposed a novel approach in particle swarm optimization (PSO) that combines chaos and velocity clamping with the aim of eliminating its known disadvantage that enforces particles to continue searching in search space boundaries. However, as the credit datasets are typically high-dimensional, class-imbalanced, and are of large sample sizes, Liu et al [38] recently proposed an Evolutionary Multi-Objective Soft Subspace Clustering (EMOSSC) algorithm for credit risk assessment.…”

Section: Literature Reviewmentioning

confidence: 99%

Credit risk assessment mechanism of personal auto loan based on PSO-XGBoost Model

Rao

Liu

Goh

2022

Complex Intell. Syst.

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As online P2P loans in automotive financing grows, there is a need to manage and control the credit risk of the personal auto loans. In this paper, the personal auto loans data sets on the Kaggle platform are used on a machine learning based credit risk assessment mechanism for personal auto loans. An integrated Smote-Tomek Link algorithm is proposed to convert the data set into a balanced data set. Then, an improved Filter-Wrapper feature selection method is presented to select credit risk assessment indexes for the loans. Combining Particle Swarm Optimization (PSO) with the eXtreme Gradient Boosting (XGBoost) model, a PSO-XGBoost model is formed to assess the credit risk of the loans. The PSO-XGBoost model is compared against the XGBoost, Random Forest, and Logistic Regression models on the standard performance evaluation indexes of accuracy, precision, ROC curve, and AUC value. The PSO-XGBoost model is found to be superior on classification performance and classification effect.

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Section: Literature Reviewmentioning

confidence: 99%

Credit risk assessment mechanism of personal auto loan based on PSO-XGBoost Model

Rao

Liu

Goh

2022

Complex Intell. Syst.

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“…Researchers have developed PSO variants that improve the performance of the SPO by using various techniques to modify the controlling parameters. Velocity clamping is one of the early important ones used to modify the PSO controlling parameters [32]. Proper choice of the velocity clamping parameter prevents the velocity of the particles from diverging and hence promoting convergence towards the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

“…The adaptive inertia weight strategies that use the distances of the particles to their personal best and global best positions have been widely proposed in the literature [37][38][39][40]. Random and chaotic theory based inertia weights have also been proposed with some degree of success [32,41]. Most PSO variants based on modification of inertia weight use the popular time-varying techniques [1].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Particle Swarm Optimization Algorithm via Adaptive Dynamic Inertia Weight and Acceleration Coefficients

Sekyere,

Effah,

Okyere

2024

J. Electron. Electric. Eng.

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The particle swarm optimization (PSO) algorithm counts among the most popular metaheuristic algorithms based on swarm intelligence. Since the publication of the first article on this optimization technique, researchers have developed many PSO variants with some improvement in its performance. The PSO optimization performance hinges on its ability to achieve a good exploration-exploitation balance. The most common method that helps to improve exploration-exploitation balance is modifying the PSO three controlling parameters, namely the inertia weight and two acceleration coefficients. In this paper a PSO variant that combines adaptive dynamic inertia weight and adaptive dynamic acceleration coefficients to enhance the exploration-exploitation balance of the PSO is proposed. The enhanced PSO algorithm called Adaptive Dynamic Inertia Weight and Acceleration Coefficient Optimization (ADIWACO) algorithm is tested on seven well-known standard test functions comprising four unimodal and three multimodal ones. The performance of the PSO is then compared with that of the standard PSO (SPSO) and four existing PSO variants. The experimental results comprising optimum value, runtime, mean value, standard deviation and convergence rate, and confirmed by the results of ranking statistics and Wilcoxon signed rank test conducted on the experimental results, indicate significantly better performance by the proposed PSO algorithm.

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“…The state of the algorithms is revealed by the distance among populations and can be measured by the Shannon entropy; a smaller entropy represents a saturated space and that the diversity be reduced. 20 Other related evolutionary algorithms enhanced by chaotic maps are described in [21][22][23] . In those algorithms, the generation of random values for the different parameters in the model is replaced by the application of chaotic maps for the generation of those parameters.…”

Section: Introductionmentioning

confidence: 99%

Chaotic genetic algorithm and the effects of entropy in performance optimization

Fuertes

Vargas

Alfaro

et al. 2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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This work proposes a new edge about the Chaotic Genetic Algorithm (CGA) and the importance of the entropy in the initial population. Inspired by chaos theory the CGA uses chaotic maps to modify the stochastic parameters of Genetic Algorithm (GA). The algorithm modifies the parameters of the initial population using chaotic series and then analyzes the entropy of such population. This strategy exhibits the relationship between entropy and performance optimization in complex search spaces. Our study includes the optimization of nine benchmark functions using eight different chaotic maps for each of the benchmark functions. The numerical experiment demonstrates a direct relation between entropy and performance of the algorithm.

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Particle swarm optimization with chaotic velocity clamping (CVC-PSO)

Cited by 7 publications

References 23 publications

Credit risk assessment mechanism of personal auto loan based on PSO-XGBoost Model

Credit risk assessment mechanism of personal auto loan based on PSO-XGBoost Model

An Enhanced Particle Swarm Optimization Algorithm via Adaptive Dynamic Inertia Weight and Acceleration Coefficients

Chaotic genetic algorithm and the effects of entropy in performance optimization

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