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.
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…
Novel algorithm PSO improves density estimation for multimodal data.
problem Data log-density estimation for multimodal distributions.
method Probabilistic Surface Optimization (PSO) using virtual stochastic forces.
result PSO-LDE achieves superior log-density estimation accuracy.
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. 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.
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.
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.
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.
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.
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.
New stable metric found on a complex space.
problem Stability of a non-symmetric metric on a complex space.
method Proving stability with respect to the Einstein-Hilbert action.
result First known example of a non-symmetric metric of positive scalar curvature.
This paper introduces derivative-free optimization methods for deep learning model training.
problem Training deep learning models using gradient descent can get stuck in local optima.
method Population based methods and random search approaches.
result Derivative-free methods can improve deep learning model training.
Optimizes power systems with energy storage under uncertainty using scenario-based method.
problem Optimizing power systems with energy storage, intermittent renewable generation, and uncontrollable loads under uncertainty.
method Developed a novel solution method based on scenario optimization and strategic sampling to solve the chance-constrained optimal power system operation problem.
result The strategic sampling method significantly improves computational efficiency and data-driven convex approximation of power flow.
Multi-step-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multi-step-ahead…
A new method simplifies credit scoring by reducing rules from complex data.
problem Time-consuming and difficult classification of customer profiles for credit risk.
method Combines LVQ neural network with PSO optimization for reduced rules.
result Very satisfactory results in credit consumer financial institution database.
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.
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.
For a fixed compact Riemann surface X, of genus at least 2, we count the number of connected components of the moduli space of maximal Higgs bundles over X for the hermitian groups PSp(2n,R), PSO∗(2n), PSO0(2,n) and E6−14. Hence the same result follows for the number of connected components of the moduli …
Paper introduces derivative-free optimization methods for deep learning model training.
problem Training deep learning models with gradient descent can get stuck in local optima.
method Bayesian methods and Lipschitzian approaches for global optimization.
result Improves deep learning model training by avoiding local optima.
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.
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.
DeepPDF uses neural networks to estimate complex data distributions efficiently.
problem Efficiently estimating complex data distributions with high accuracy.
method DeepPDF uses a neural network to approximate a target pdf given samples, employing Probabilistic Surface Optimization (PSO) for stochastic optimization.
result DeepPDF achieves high inference accuracy for a wide range of target pdfs using a simple network structure.
The paper defines and calculates Reidemeister torsion for a specific class of representations.
problem Defining and calculating Reidemeister torsion for G-Anosov representations.
method Symplectic chain complex method to establish a novel formula for R-torsion.
result Reidemeister torsion is well-defined and calculated for G-Anosov representations.
Study on counting special-orthogonal Anosov orbits in geometric spaces.
problem Counting points in specific orbits of projective Anosov subgroups.
method Analysis of totally geodesic copies in Riemannian and pseudo-Riemannian spaces.
result Number of points in the orbit is finite and asymptotically exponential.
Geometric interpretation of Fock-Goncharov positivity and disk stabilization in symmetric space.
problem Understanding Fock-Goncharov positivity and its geometric implications.
method Geometric interpretation and bending deformations of Fuchsian representations.
result Stabilization of a uniform Finsler quasi-convex disk in the symmetric space.
The paper proves rigidity of surgeries on the figure-eight knot complement.
problem Infinitesimal projective rigidity of surgeries on the figure-eight knot complement.
method Computer-assisted proof and explicit representations of the knot complement.
result Proves infinitesimal projective rigidity for surgeries far from the ideal point.
Affine actions fail for Hitchin linear parts, except flat pseudo-Riemannian cases.
problem Properly discontinuous affine actions of surface groups with Hitchin linear part.
method Analysis of representations and pseudo-Riemannian metrics.
result Hitchin linear parts in SO(n,n−1) lead to non-properly discontinuous affine actions. New research shows certain arithmetic lattices can't be LERF.
problem Determining if arithmetic lattices are LERF.
method Analyzing trialitarian arithmetic lattices in PSO7,1(R). result Trialitarian arithmetic lattices in PSO7,1(R) are not LERF. We prove the Bloch conjecture : $ c_2(E) \in H^4_\cald (X,\bbz(2))$ is torsion for holomorphic rank two vector bundles E with an integrable connection over a complex projective variety X. We prove also the rationality of the Chern-Simons invariant of compact arithmetic hyperbolic three-manifolds. We give a sharp hi…
Study improves electricity price forecasting accuracy using a hybrid model.
problem Accurate short-term electricity price forecasting is challenging due to social and natural factors.
method Hybrid model combining GARMA, G-GARCH, Wavelet, LLWNN, and optimization algorithms.
result The hybrid model outperforms other models in Nord Pool Electricity markets.
Study uses online data to identify malicious websites, addressing imbalance.
problem Identifying malicious websites from many more benign ones.
method Integrated resampling approach combining SMOTE and PSO.
result Proposed approach outperforms other resampling methods.
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.
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.
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.
Strict plurisubharmonicity proven for Teichmüller energy on Hitchin representations.
problem Proving strict plurisubharmonicity of Teichmüller energy for Hitchin representations.
method Analyzing energy functional E on Teichmüller space associated to Hitchin representations. result Strict plurisubharmonicity of energy functional E proven. The paper constructs a symplectic groupoid for a specific Poisson structure.
problem Integrating the Adler-Gelfand-Dikii Poisson structure on Lie groups.
method Constructing a symplectic groupoid Morita equivalent to the quasi-symplectic groupoid.
result The constructed symplectic groupoid is Morita equivalent to the quasi-symplectic groupoid.
The paper shows BNSR-invariants of McCool groups are either dense or empty.
problem Characterizing BNSR-invariants of McCool groups.
method Understanding higher generation properties of abelian subgroups and using a general criterion for characters.
result Every BNSR-invariant Σm of a McCool group is either dense or empty. Hybrid models improve groundwater level prediction and uncertainty analysis.
problem Predicting and analyzing uncertainty of monthly groundwater levels.
method Six evolutionary optimization algorithms (GOA, CSO, WA, GA, KA, PSO) hybridized with ANFIS, ANN, and SVM.
result ANFIS-GOA outperformed other models in predicting groundwater levels.
Dual model predicts electricity spot prices using neural networks and wavelets.
problem Forecasting hourly electricity spot prices.
method Dual generalized long memory modelling with k-factor GARMA and G-GARCH models, using LLWNN and PSO for variance prediction.
result The hybrid k-factor GARMA-LLWNN model outperforms other methods in forecasting accuracy.
The study examines growth of quadratic forms under Anosov subgroups.
problem Growth of quadratic forms under Anosov subgroups.
method Analyzes exponential bounds and asymptotic counting functions for distances between geodesic copies of symmetric spaces.
result Shows asymptotic behavior of counting functions for certain choices of quadratic forms.
Future grid scenario analysis requires a major departure from conventional power system planning, where only a handful of most critical conditions is typically analyzed. To capture the inter-seasonal variations in renewable generation of a future grid scenario necessitates the use of computationally intensive time-seri…
Bayesian optimization reduces computational effort in aircraft design optimization.
problem High computational cost in industrial aircraft design optimization.
method Constrained Bayesian optimization (Super Efficient Global Optimization with Mixture of Experts)
result Significant computational efficiency improvements over existing Isight optimizers.
New model reduces hyperparameter optimization time and improves transfer learning.
problem Hyperparameter optimization for machine learning across multiple datasets.
method Developed a new ensemble model for Bayesian optimization that transfers knowledge between datasets.
result Substantially reduces optimization time and improves over state-of-the-art transfer hyperparameter optimization.
Bayesian optimization outperforms other methods in nano-optical shape optimization and parameter reconstruction.
problem Optimizing nano-optical structures with non-convex objective functions.
method Benchmarked five global optimization methods including Bayesian optimization.
result Bayesian optimization yields significantly better results in a fraction of the time.
Bayesian optimization method tackles combinatorial spaces, scalable for large data.
problem Optimization over combinatorial categorical spaces in natural sciences.
method Combines variational optimization and continuous relaxations for gradient-based optimization.
result Method performs comparably to state-of-the-art methods while scaling well.
New algorithm solves complex stopping problems with robust optimization.
problem Solving complex stochastic optimal stopping problems.
method Simulation-based robust optimization with exact reformulation as a zero-one bilinear program.
result Developed polynomial-time heuristics and algorithms for practical solution.