# Particle Swarm Optimization

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researchsquare.com Nov 11, 2026
Backdoor detection stops reading only the last layer: CLEAR fuses cross-layer extreme responses for 99.54% average TPR on Tiny-ImageNet
CLEAR moves backdoor sample detection from single-layer activations to cross-layer extreme responses: batch normalization statistics align hidden-layer features to a common scale, each channel's spatial minimum becomes the anomaly representation, and particle swarm optimization searches layer weights. On CIFAR-10 and Tiny-ImageNet, CLEAR reaches average true positive rates of 97.54% and 99.54% with average false positive rates of 0.16% and 0.03%, above AC, SCALE-UP, AIBD and CCD; models retrained on its purified data stay close to benign accuracy.
CLEAR moves backdoor sample detection from single-layer activations to cross-layer extreme responses: batch normalization statistics align hidden-layer features to a common scale, each channel's spatial minimum becomes the anomaly representation, and particle swarm optimization searches layer weights. On CIFAR-10 and Tiny-ImageNet, CLEAR reaches average true positive rates of 97.54% and 99.54% with average false positive rates of 0.16% and 0.03%, above AC, SCALE-UP, AIBD and CCD; models retrained on its purified data stay close to benign accuracy.
Hui Li（First author）Wanwei Huang（Corresponding author）
CLEAR Backdoor Detection Particle Swarm Optimization Clustering
arXiv Nov 9, 2026
EMRI parameter inference no longer needs a true-value start: a neural network narrows the search, and a six-month signal yields posteriors in about ten hours
Bayesian parameter inference for extreme-mass-ratio inspirals (EMRIs) has relied on narrow priors centered on the true parameters or Markov chains initialized near them, information real observations do not provide. A deep-learning-assisted framework first uses a neural network to produce approximate posteriors from the data and shrink the search region, then particle swarm optimization finds a high-likelihood starting point, and staged Markov chain Monte Carlo sampling produces the final posteriors. On two noise-free six-month simulated signals, the resulting posteriors agree closely with reference analyses initialized near the true values, and once the network is trained each signal takes about ten hours.
Bayesian parameter inference for extreme-mass-ratio inspirals (EMRIs) has relied on narrow priors centered on the true parameters or Markov chains initialized near them, information real observations do not provide. A deep-learning-assisted framework first uses a neural network to produce approximate posteriors from the data and shrink the search region, then particle swarm optimization finds a high-likelihood starting point, and staged Markov chain Monte Carlo sampling produces the final posteriors. On two noise-free six-month simulated signals, the resulting posteriors agree closely with reference analyses initialized near the true values, and once the network is trained each signal takes about ten hours.
Jian-Jun Song（First author）
Neural Posterior Estimation Markov Chain Monte Carlo Particle Swarm Optimization EMRI Bayesian Parameter Inference
researchsquare.com Oct 19, 2026
Ovarian cancer screening moves past a single marker: PSO-tuned fusion of four rankings lifts full-pipeline ablation accuracy to 85.51%
Early ovarian cancer diagnosis leans on serum markers and routine laboratory tests, yet redundant, noisy and correlated variables in high-dimensional clinical data degrade both classification and interpretability. A hybrid biomarker selection pipeline fuses correlation analysis, mutual information, XGBoost importance and SHAP relevance with particle-swarm-optimized adaptive weights, feeding an attention-enhanced multi-scale 1D convolutional network; on a public Kaggle ovarian cancer clinical dataset under stratified 10-fold cross-validation it reports 93.35% accuracy and 90.66% ROC-AUC. In ablation, the full pipeline reaches 85.51% accuracy, 19.33 percentage points above a baseline 1D-CNN without selection or attention.
Early ovarian cancer diagnosis leans on serum markers and routine laboratory tests, yet redundant, noisy and correlated variables in high-dimensional clinical data degrade both classification and interpretability. A hybrid biomarker selection pipeline fuses correlation analysis, mutual information, XGBoost importance and SHAP relevance with particle-swarm-optimized adaptive weights, feeding an attention-enhanced multi-scale 1D convolutional network; on a public Kaggle ovarian cancer clinical dataset under stratified 10-fold cross-validation it reports 93.35% accuracy and 90.66% ROC-AUC. In ablation, the full pipeline reaches 85.51% accuracy, 19.33 percentage points above a baseline 1D-CNN without selection or attention.
Sathyabama Institute of Science and Technology
Ratnala Mani Deepika（First author）
O-AHBS AMS-1D-CNN Ovarian Cancer Prediction Particle Swarm Optimization SHAP
CAUCHY Jurnal Matematika Murni dan Aplikasi Sep 28, 2026
Seven KNN hyperparameter tuning strategies compared across 15 datasets: PSO ranks best overall but significantly beats only random search
Under a nested cross-validation framework, this study compared seven hyperparameter tuning strategies for classical KNN over a mixed search space comprising an integer neighborhood size k, a categorical distance metric, and a continuous Minkowski exponent p—grid search, random search, Bayesian optimization, genetic algorithm, surrogate optimization, particle swarm optimization (PSO), and grey wolf optimizer—evaluating them on 15 public classification datasets using accuracy, macro AUC, cross-validation loss, cross-entropy loss, and runtime, and found that PSO achieved the best overall mean rank but held a statistically significant advantage only over random search under Holm-corrected Wilcoxon signed-rank tests.
Under a nested cross-validation framework, this study compared seven hyperparameter tuning strategies for classical KNN over a mixed search space comprising an integer neighborhood size k, a categorical distance metric, and a continuous Minkowski exponent p—grid search, random search, Bayesian optimization, genetic algorithm, surrogate optimization, particle swarm optimization (PSO), and grey wolf optimizer—evaluating them on 15 public classification datasets using accuracy, macro AUC, cross-validation loss, cross-entropy loss, and runtime, and found that PSO achieved the best overall mean rank but held a statistically significant advantage only over random search under Holm-corrected Wilcoxon signed-rank tests.
Telkom University Universitas Islam Negeri Maulana Malik Ibrahim
Meta Kallista（First author）
Hyperparameter Optimization k-Nearest Neighbors Particle Swarm Optimization Bayesian Optimization Grey Wolf Optimizer

You’re all caught up

Backdoor detection stops reading only the last layer: CLEAR fuses cross-layer extreme responses for 99.54% average TPR on Tiny-ImageNet

EMRI parameter inference no longer needs a true-value start: a neural network narrows the search, and a six-month signal yields posteriors in about ten hours

Ovarian cancer screening moves past a single marker: PSO-tuned fusion of four rankings lifts full-pipeline ablation accuracy to 85.51%

Seven KNN hyperparameter tuning strategies compared across 15 datasets: PSO ranks best overall but significantly beats only random search