An Improved Particle Swarm Optimization Algorithm and Its Application to the Extreme Value Optimization Problem of Multivariable Function

Min, Cai

doi:10.1155/2022/1935272

Cited by 4 publications

(8 citation statements)

References 20 publications

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“…These algorithms tend to generate suboptimal solutions within the search space without achieving better solutions. As a result feature selection yield suboptimal performance in the model, consume valuable time, and getting traped in local optima [15,16].…”

Section: B Previous Studiesmentioning

confidence: 99%

“…Feature Selection, especially PSO tends to have low performance without optimization. Generally, the best results can be obtained when parameter tuning is performed or when various PSO techniques are utilized [15]. According to [17], there are several techniques to enhance the PSO method, including hybridization, improved strategies such as fuzzy logic and mutation, and the utilization of different PSO variants such as binary and chaotic.…”

Section: B Previous Studiesmentioning

confidence: 99%

“…Based on this background, we proposed a model to optimize the PSO algorithm by hybridizing with the GWO algorithm, as previously mentioned by [17], doing a hybrid on PSO allows this algorithm to get more optimal results. We used PSO over GA because particle swarm optimization algorithms are easier to use, require fewer adjustable parameters, and are simpler to comprehend compared to other bionic algorithms like genetic algorithms [15]. According to [19] the right classifier is needed to be able to reduce high dimensional data and to get better performance.…”

Section: B Previous Studiesmentioning

confidence: 99%

“…Particle swarm optimization (PSO) is a remarkably effective metaheuristic approach that has been effeciently employed to acquire an optimal subset of features containing crucial information within a feasible time [29]. PSO begins by generating a set of random solutions and iteratively seeks for the optimal solution [15]. The PSO algorithm's concept and development were inspired by the social behaviors of fish schools and flocks of birds.…”

Section: Feature Selection 1 Pso Feature Selectionmentioning

confidence: 99%

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Optimizing Software Defect Prediction Models: Integrating Hybrid Grey Wolf and Particle Swarm Optimization for Enhanced Feature Selection with Popular Gradient Boosting Algorithm

Angga Maulana Akbar,

Herteno,

Saputro

et al. 2024

j.electron.electromedical.eng.med.inform

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Software defects, also referred to as software bugs, are anomalies or flaws in computer program that cause software to behave unexpectedly or produce incorrect results. These defects can manifest in various forms, including coding errors, design flaws, and logic mistakes, this defect have the potential to emerge at any stage of the software development lifecycle. Traditional prediction models usually have lower prediction performance. To address this issue, this paper proposes a novel prediction model using Hybrid Grey Wolf Optimizer and Particle Swarm Optimization (HGWOPSO). This research aims to determine whether the Hybrid Grey Wolf and Particle Swarm Optimization model could potentially improve the effectiveness of software defect prediction compared to base PSO and GWO algorithms without hybridization. Furthermore, this study aims to determine the effectiveness of different Gradient Boosting Algorithm classification algorithms when combined with HGWOPSO feature selection in predicting software defects. The study utilizes 13 NASA MDP dataset. These dataset are divided into testing and training data using 10-fold cross-validation. After data is divided, SMOTE technique is employed in training data. This technique generates synthetic samples to balance the dataset, ensuring better performance of the predictive model. Subsequently feature selection is conducted using HGWOPSO Algorithm. Each subset of the NASA MDP dataset will be processed by three boosting classification algorithms namely XGBoost, LightGBM, and CatBoost. Performance evaluation is based on the Area under the ROC Curve (AUC) value. Average AUC values yielded by HGWOPSO XGBoost, HGWOPSO LightGBM, and HGWOPSO CatBoost are 0.891, 0.881, and 0.894, respectively. Results of this study indicated that utilizing the HGWOPSO algorithm improved AUC performance compared to the base GWO and PSO algorithms. Specifically, HGWOPSO CatBoost achieved the highest AUC of 0.894. This represents a 6.5% increase in AUC with a significance value of 0.00552 compared to PSO CatBoost, and a 6.3% AUC increase with a significance value of 0.00148 compared to GWO CatBoost. This study demonstrated that HGWOPSO significantly improves the performance of software defect prediction. The implication of this research is to enhance software defect prediction models by incorporating hybrid optimization techniques and combining them with gradient boosting algorithms, which can potentially identify and address defects more accurately

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Section: B Previous Studiesmentioning

confidence: 99%

Section: B Previous Studiesmentioning

confidence: 99%

Section: B Previous Studiesmentioning

confidence: 99%

Section: Feature Selection 1 Pso Feature Selectionmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing Software Defect Prediction Models: Integrating Hybrid Grey Wolf and Particle Swarm Optimization for Enhanced Feature Selection with Popular Gradient Boosting Algorithm

Angga Maulana Akbar,

Herteno,

Saputro

et al. 2024

j.electron.electromedical.eng.med.inform

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“…PSO also has weaknesses that are very influential in the implementation process. In the case of PSO, high-dimensional datasets also have the effect of causing convergence to be premature, making it less effective in handling noisy attributes [6] [7]. PSO often requires more iterations and produces much more complex models when applied to high-dimensional datasets [8].…”

Section: Introductionmentioning

confidence: 99%

Improving with Hybrid Feature Selection in Software Defect Prediction

Pratama,

Herteno,

Faisal

et al. 2024

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Software defect prediction (SDP) is used to identify defects in software modules that can be a challenge in software development. This research focuses on the problems that occur in Particle Swarm Optimization (PSO), such as the problem of noisy attributes, high-dimensional data, and premature convergence. So this research focuses on improving PSO performance by using feature selection methods with hybrid techniques to overcome these problems. The feature selection techniques used are Filter and Wrapper. The methods used are Chi-Square (CS), Correlation-Based Feature Selection (CFS), and Forward Selection (FS) because feature selection methods have been proven to overcome data dimensionality problems and eliminate noisy attributes. Feature selection is often used by some researchers to overcome these problems, because these methods have an important function in the process of reducing data dimensions and eliminating uncorrelated attributes that can cause noisy. Naive Bayes algorithm is used to support the process of determining the most optimal class. Performance evaluation will use AUC with an alpha value of 0.050. This hybrid feature selection technique brings significant improvement to PSO performance with a much lower AUC value of 0.00342. Comparison of the significance of AUC with other combinations shows the value of FS PSO of 0.02535, CFS FS PSO of 0.00180, and CS FS PSO of 0.01186. The method in this study contributes to improving PSO in the SDP domain by significantly increasing the AUC value. Therefore, this study highlights the potential of feature selection with hybrid techniques to improve PSO performance in SDP.

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