[HTML][HTML] An improved artificial rabbits optimization for accurate and efficient infinite impulse response system identification
Abstract Identifying models with Infinite Impulse Response (IIR) is crucial in signal
processing and system identification. This paper addresses the challenges of IIR model …
processing and system identification. This paper addresses the challenges of IIR model …
Electrocardiogram signal compression using tunable-Q wavelet transform and meta-heuristic optimization techniques
Electrocardiogram (ECG) signals are the biomedical signals commonly used in the
prognosis of cardiovascular diseases. ECG recordings need to be stored and transferred …
prognosis of cardiovascular diseases. ECG recordings need to be stored and transferred …
DGS-SCSO: enhancing sand cat swarm optimization with dynamic pinhole imaging and golden sine algorithm for improved numerical optimization performance
This paper introduces DGS-SCSO, a novel optimizer derived from Sand Cat Swarm
Optimization (SCSO), aiming to overcome inherent limitations in the original SCSO …
Optimization (SCSO), aiming to overcome inherent limitations in the original SCSO …
A hyper-parameter tuning approach for cost-sensitive support vector machine classifiers
In machine learning, hyperparameter tuning is strongly useful to improve model
performance. In our research, we concentrate our attention on classifying imbalanced data …
performance. In our research, we concentrate our attention on classifying imbalanced data …
A novel solver for multi-objective optimization: dynamic non-dominated sorting genetic algorithm (DNSGA)
Q Long, G Li, L Jiang - Soft Computing, 2022 - Springer
Non-dominated sorting is a critical component of all multi-objective evolutionary algorithms
(MOEAs). A large percentage of computational cost of MOEAs is spent on non-dominated …
(MOEAs). A large percentage of computational cost of MOEAs is spent on non-dominated …
A resource allocation-based multi-objective evolutionary algorithm for large-scale multi-objective optimization
W Yang, J Liu, W Zhang, X Zhang - Soft Computing, 2023 - Springer
In large-scale multi-objective optimization problems (LSMOPs), multiple conflicting
objectives and hundreds even thousands of decision variables are contained. Therefore, it is …
objectives and hundreds even thousands of decision variables are contained. Therefore, it is …
A new method for designing of stable digital IIR filter using hybrid method
In this paper, a new technique for designing of a stable digital infinite impulse response filter,
with improved performance in passband and stopband regions using quantum particle …
with improved performance in passband and stopband regions using quantum particle …
A new design method for stable IIR filters with nearly linear-phase response based on fractional derivative and swarm intelligence
In this paper, a new design method based on fractional derivative (FD) is proposed for
designing digital stable infinite impulse response (IIR) filters with nearly linear-phase …
designing digital stable infinite impulse response (IIR) filters with nearly linear-phase …
Time–cost–quality–CO2 emissions optimization in construction management using slime mold algorithm opposition tournament mutation
The concurrent time–cost–quality–CO2 (TCQC) emission trade-off optimization in projects in
urban areas is difficult because the factors always contradict each other. This study …
urban areas is difficult because the factors always contradict each other. This study …
A hybrid moth flame optimization and variable neighbourhood search technique for optimal design of IIR filters
T Mittal - Neural Computing and Applications, 2022 - Springer
In this manuscript, a hybrid optimization technique, which integrates moth flame optimization
(MFO) technique and variable neighbourhood search (VNS) heuristic, has been proposed to …
(MFO) technique and variable neighbourhood search (VNS) heuristic, has been proposed to …