Empirical comparison of 18 hyperparameter tuning algorithms for SVM.
problem Tuning hyperparameters C and γ for SVM with RBF kernel. method Compared 18 search algorithms on 115 real-life data sets.
result Trees of Parzen estimators and particle swarm optimization perform similarly to grid search.
Enhanced Particle Swarm Optimization improves ANN classification accuracy and stability.
problem Improving classification accuracy of ANN models.
method Proposes an enhanced Particle Swarm Optimization for ANN training.
result Significant improvement in classification accuracy through stability analysis.
Optimized HMM using PSO overcomes constraints for better solutions.
problem Finding global optimal solutions for HMM parameters.
method Constrained Particle Swarm Optimization (PSO) to solve HMM parameters, re-normalization and re-mapping to enforce constraints.
result PSOHMM finds better solutions and converges faster than BWHMM.
Paper uses averaging from many particle filters to approximate posterior predictive distributions.
problem Approximating posterior predictive distributions efficiently and accurately.
method Particle swarm filter algorithm that averages many particle filter approximations.
result Law of large numbers and central limit theorem support the method's effectiveness.
Introduces a network-based framework to analyze swarm intelligence.
problem Lack of a common framework to compare swarm-based algorithms.
method Introduces an interaction network to examine swarm-based systems.
result Shows how swarm-based algorithms can be studied as systems.
The paper learns particle swarming models from data using Gaussian processes.
problem Understanding the link between individual interaction rules and swarming behavior.
method Proposes a learning approach using Gaussian processes to model latent radial interaction functions and scalar parameters in non-collective friction forces.
result Establishes that a coercivity condition is sufficient for recoverability and provides a finite-sample analysis showing optimal convergence rates.
The paper connects PSO and CBO methods using stochastic modeling and mean-field limits.
problem Global optimization problems with particle swarm optimization and consensus based optimization.
method Stochastic differential equations and mean-field approximation to derive macroscopic hydrodynamic equations.
result Derives mean-field approximation for PSO and links it to CBO methods.
Optimizes K-means clustering with PSO for better accuracy.
problem Improving the accuracy of K-means clustering.
method Uses Particle Swarm Optimization (PSO) to find optimal initial centroids for K-means.
result Optimal centroids found using PSO lead to better clustering accuracy.
New algorithm uses PSO to optimize DNN training parameters in distributed systems.
problem Reducing synchronization frequency in DNN training leads to poor convergence.
method Integrates PSO into distributed training to automatically compute new parameters.
result Proposed algorithm outperforms synchronous methods in distributed DNN training.
A new optimizer, MVO, improves nonlinear regression performance.
problem Finding optimal coefficients in nonlinear regression models.
method Multi-Verse Optimizer (MVO) compared to Particle Swarm Optimizer (PSO).
result MVO statistically outperforms PSO in 10 nonlinear regression problems.
A new method uses PSO to optimize sentence weights for user-oriented document summaries.
problem Handling information overload in documents through efficient summarization.
method Particle Swarm Optimization (PSO) to identify and weight sentence features.
result Improved accuracy in summarization compared to previous methods.
AdaSwarm optimizes deep learning models with swarm intelligence, outperforming Adam.
problem Optimization challenges in deep learning models.
method AdaSwarm uses EMPSO to approximate gradients for optimization.
result AdaSwarm outperforms Adam and handles various loss functions.
PSO optimizes model parameters in nonstandard distributions.
problem Estimating model parameters in nonstandard distributions using existing algorithms.
method Particle Swarm Optimization (PSO) as an alternative optimization routine.
result PSO produces more optimal or convergent results than existing algorithms.
Modified swarm optimizers improve GD initialization for EEG spatial filtering.
problem Gradient Descent fails in complex, multi-scale convexity scenarios.
method Modified Swarm-based Optimizers (ICA and PSO) applied to GD initialization.
result The modified optimizers outperform baseline optimizers in EEG classification and loss function fitness.
Metaheuristics optimize portfolios with pre-assignment and margin trading for better risk-adjusted returns.
problem Maximizing returns while minimizing risk in portfolio optimization.
method Incorporates pre-assignment constraints and margin trading strategies using Genetic Algorithms and Particle Swarm Optimization.
result Metaheuristic-based portfolio optimization yields superior risk-adjusted returns compared to traditional methods.
Metaheuristics improve yield curve estimation for Costa Rica.
problem Estimating the yield curve for Costa Rica using historical data.
method Used Nelson-Siegel and Svensson models with four metaheuristics (Ant colony, Genetic, Particle Swarm, Simulated Annealing) for optimization.
result Metaheuristics achieved better results than classical methods, especially Particle Swarm and Simulated Annealing.
Optimal income crossover found using particle swarm optimization.
problem Determining the crossover point between two income distributions.
method Particle swarm optimization for finding crossover income, temperature, and Pareto index.
result Optimization method finds boundaries of two income distributions.
A new time-series clustering method using slope-based similarity and PSO.
problem Clustering time-series data efficiently and accurately.
method Developed a novel slope-based similarity measure combined with Particle Swarm Optimization (PSO) for clustering.
result The proposed similarity measure outperforms existing measures in clustering time-series data.
mFI-PSO generates effective adversarial images for DNNs.
problem Vulnerability of DNNs to small perturbations in images.
method Uses mFI for pixel selection and PSO for objective functions.
result mFI-PSO effectively designs flexible adversarial images.
META-DES.Oracle uses meta-learning and feature selection to improve ensemble selection accuracy.
problem Dynamic Ensemble Selection (DES) issues with classifier competence estimation.
method META-DES.Oracle integrates multiple criteria and an Oracle-based meta-feature selection scheme.
result META-DES.Oracle significantly improves classification accuracy compared to previous methods.
AdversarialPSO uses fewer queries to create high-success-rate adversarial examples.
problem Creating imperceptible adversarial examples with high success rates in black-box settings.
method AdversarialPSO, a black-box attack using Particle Swarm Optimization.
result Achieved high success rates (99.6% on CIFAR-10, 96.3% on MNIST, 82.0% on Imagenet) with fewer queries.
Bayesian Active Learning improves protein docking accuracy and uncertainty quantification.
problem Uncertainty quantification in protein docking optimization.
method Bayesian Active Learning (BAL) for optimization and uncertainty quantification of protein docking.
result BAL significantly improves docking accuracy and provides tight confidence intervals.
Optimizes trading portfolios considering risk and profit.
problem Balancing risk and profit in trading portfolios.
method Risk-Aware Trading Swarm (RATS) algorithm.
result RATS improves portfolio performance and risk management.
Study defines and optimizes bank reliability using LR and PSO.
problem Lack of reliability concept in financial services.
method Logistic Regression (LR) for initial estimation, Particle Swarm Optimization (PSO) for optimization.
result Optimal financial ratios maximize bank reliability.
Method for explaining machine learning survival models using counterfactuals.
problem Tackles the challenge of explaining survival models in machine learning.
method Introduces a condition based on the difference of mean times to event for counterfactual explanation. Reduces the problem to a convex optimization problem for Cox models and applies Particle Swarm Optimization for other models.
result Demonstrates the effectiveness of the proposed method through numerical experiments.
Hybrid ANN model predicts firm bankruptcy with improved PSO and SA.
problem Predicting firm failure for investors and decision makers.
method Hybrid ANN model using improved PSO and SA for variable selection and optimization.
result The hybrid model outperforms in convergence and accuracy compared to traditional methods.
PSO improves G-optimal designs for up to 5 factors, reducing computation time.
problem Computing highly G-optimal designs for response surface models is computationally expensive. method Extended Particle Swarm Optimization (PSO) for optimal design problems.
result PSO generates improved G-optimal designs for up to 5 factors with comparable computational cost. This paper improves combine harvester performance using ANN-PSO hybrid model.
problem Improving performance of combine harvesters to minimize waste and reduce maintenance.
method Proposes a hybrid machine learning model combining artificial neural networks and particle swarm optimization.
result Demonstrates higher accuracy and stability in predicting optimal performance of combine harvesters.
Numerical optimization is an important tool in the field of computational physics in general and in nano-optics in specific. It has attracted attention with the increase in complexity of structures that can be realized with nowadays nano-fabrication technologies for which a rational design is no longer feasible. Also, …
PBO methods improve RNN performance in learning long-term dependencies.
problem Training RNNs to learn long-term dependencies is challenging.
method Population-based global optimisation (PBO) techniques, including evolution strategies and particle swarm optimisation.
result PBO methods lead to performance improvements in RNNs for volatility forecasting.
Novel LSTM network predicts pulsar timing residuals with few-shot data.
problem Predicting pulsar timing residuals with limited data.
method Long Short-Term Memory (LSTM) network optimized with model-agnostic meta-learning and particle swarm optimization.
result Robust generalization and accurate predictions across high-frequency test domains with minimal data.
ARS visualization improves t-SNE dynamics with tunable attraction and repulsion.
problem Improve data visualization techniques for complex data sets.
method ARS framework based on t-SNE dynamics with normalized interactions and tunable kernels.
result ARS visualization provides better control over cluster tightness and spacing.
Derives a fluid model for fish swarming in arbitrary dimensions.
problem Modeling fish swarming behavior in various dimensions.
method Develops a novel n-dimensional model using bundle geometry and variational formulation. result Derives the self-organized hydrodynamic model from the interacting particle system.
Optimizes bus schedules to improve on-time performance.
problem Improving on-time performance of public transit systems.
method Formulated as a single-objective optimization task, solved using greedy algorithm, GA, and PSO.
result Enhanced bus timetables leading to better on-time performance.
PSO optimizes hyperparameters for edge ML models in FL.
problem Efficient local training and inference at edge devices.
method Particle Swarm Optimization (PSO) for hyperparameter tuning.
result PSO reduces the number of communication rounds by 2 orders of magnitude.
New methods solve inverse structural modification problems using random projections.
problem Quantifying changes in modal properties of structures given limited data.
method First-order gradient-based methods are inefficient. Particle swarm optimization is used instead. Random projections reduce dimensionality.
result Random projections can reduce dimensionality by 80-99%, making optimization problems more tractable.
Addressing the issue of SVMs parameters optimization, this study proposes an efficient memetic algorithm based on Particle Swarm Optimization algorithm (PSO) and Pattern Search (PS). In the proposed memetic algorithm, PSO is responsible for exploration of the search space and the detection of the potential regions with…
Optimizes plasmonic mirror filters using multi-fidelity Gaussian processes.
problem Optimizing transmission properties of plasmonic mirror color filters.
method Combining numerical methods with FDTD simulations and multi-fidelity Gaussian processes.
result Demonstrates improved optimization performance with multi-fidelity Gaussian processes.
The Bouncy Particle Sampler is a novel rejection-free non-reversible sampler for differentiable probability distributions over continuous variables. We generalize the algorithm to piecewise differentiable distributions and apply it to generic binary distributions using a piecewise differentiable augmentation. We illust…
Transformers approximate mean-field dynamics of indistinguishable particles.
problem Approximating the dynamics of indistinguishable particles in complex systems.
method Using transformers to model the mean-field dynamics of interacting particle systems.
result Theoretical bounds on the distance between true and transformer-obtained mean-field dynamics.
We study a distributed framework for stochastic optimization which is inspired by models of collective motion found in nature (e.g., swarming) with mild communication requirements. Specifically, we analyze a scheme in which each one of N>1 independent threads, implements in a distributed and unsynchronized fashion,…
Paper proposes a federated learning framework for UAV swarms, optimizing convergence rate and energy consumption.
problem Challenges of centralized ML in UAV swarms due to limited connections and large data volumes.
method Distributed federated learning within UAV swarm, joint power allocation and scheduling design.
result Joint design strategy reduces communication rounds by up to 35%.
PolySwarm uses a swarm of LLMs to predict and arbitrage prediction markets.
problem Real-time prediction market trading and latency arbitrage inefficiencies.
method PolySwarm employs a swarm of 50 diverse LLMs, Bayesian combination, and risk-controlled execution.
result Swarm aggregation outperforms single-model baselines in prediction tasks.
DBS uses swarm intelligence to cluster data without needing a global objective function.
problem Clustering high-dimensional data with unknown natural clusters.
method Databionic swarm (DBS) system that self-organizes and adapts to data structures.
result DBS outperforms traditional clustering methods in scenarios with no prior knowledge.
DeepSwarm uses swarm intelligence to optimize CNN architectures.
problem Optimizing Convolutional Neural Networks (CNNs).
method Ant Colony Optimization (ACO) for neural architecture search.
result DeepSwarm outperforms existing methods on MNIST, Fashion-MNIST, and CIFAR-10 datasets.
Method identifies IPS governing equations from particle data efficiently.
problem Identify governing equations of interacting particle systems efficiently.
method Combines mean-field theory and WSINDy for large N and M. result Converges with rate O(N−1/2) for N≥100. Proposes a new framework for predicting stock market movements using sparse neural architectures.
problem Challenging problem of predicting stock market movements using technical indicators.
method Multi-criteria optimization approach to evolve sparse neural architectures.
result Evolved parsimonious networks with better generalization capabilities.
We obtain the conditions for the emergence of the swarm intelligence effect in an interactive game of restless multi-armed bandit (rMAB). A player competes with multiple agents. Each bandit has a payoff that changes with a probability pc per round. The agents and player choose one of three options: (1) Exploit (a …