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Evolutionarily Optimized Electromagnetic Sensor Measurements for Robust Surgical Navigation
2019
IEEE Sensors Journal
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Eventually, the output of adaptive particle swarm optimization is the optimal solution Q k with respect to the maximal fitness at time k after G iterations: Q k = arg max cg∈C G f (c g ), (13) where C ...
In particular, the observation-boosted differential evolution outperforms the adaptive particle swarm optimization. ...
doi:10.1109/jsen.2019.2928835
fatcat:rn357myt2vbdzf3tyzghojasda
Parameter Estimation of Conditional Random Fields Model By Improved Particle Swarm Optimizer
2011
Journal of Computers
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Index Terms-Conditional Random Fields Model, Particle Swarm Optimizer, Parameter Estimation, Aggregation degree of particle swarm; Relative change ratio of loglikelihood ...
A new parameter estimation algorithm based on improved particle swarm optimizer is proposed to improve the precision and recall rate of conditional random fields model. ...
PARAMETER ESTIMATION OF CONDITIONAL RANDOM FIELDS BY IMPROVED PARTICLE SWARM OPTIMIZER
A.Improved Particle Swarm Optimizer Kennedy and Eberhart [16, 25] originally designed particle swarm optimizer ...
doi:10.4304/jcp.6.8.1628-1633
fatcat:d4l2qlgzbfa3nbmx2zjmvac77q
An immersion and invariance based input voltage and resistive load observer for DC–DC boost converter
2019
SN Applied Sciences
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Considering optimal performance of observer, an improved particle swarm optimization algorithm is employed to determine observer gains. ...
In this paper, a new nonlinear observer is presented for DC-DC boost converter using immersion and invariance (I&I) technique. ...
Improved particle swarm optimization algorithm (IPSO) Particle swarm optimization is a social search algorithm modeled on the basis of the social behavior of bird flocks [24] . ...
doi:10.1007/s42452-019-1880-7
fatcat:qz23qhn3t5byziuzjwwun5e6oa
Boosted Relief Feature Subset Selection and Heterogeneous Cross Project Defect Prediction using Firefly Particle Swarm Optimization
2020
International journal of recent technology and engineering
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To produce optimal defect prediction in the Heterogeneous environment, the knowledge of particle swarm optimization by inducing firefly algorithm. ...
This work used five different software groups with six different datasets to perform heterogeneous cross project defect prediction using firefly particle swarm optimization. ...
Boosted Relief Feature Selection with Heterogeneous Cross Project Defect Prediction using Firefly Particle Swarm Optimization. ...
doi:10.35940/ijrte.e6333.018520
fatcat:t3lf5l3h7vbobeg5fktv7ndlxe
Node localization algorithm of Non-line-of-sight Environment Based on Particle Swarm Optimization
2016
International Journal of Future Generation Communication and Networking
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PSO (Particle Swarm Optimization) algorithm is a fine choice. ...
Node localization algorithm of NLOS (Non-line-of-sight) environment based on PSO (particle swarm optimization) is proposed aiming at NLOS range error. ...
For this reason, this article put forward NLOS+PSO of WSN to NLOS based on particle swarm optimization to alleviate the influence of NLOS ranging error on position estimation so as to boost location accuracy ...
doi:10.14257/ijfgcn.2016.9.8.26
fatcat:gtluq6bnvrht3id7f3aoou4sqa
Improved Dynamic Response of DC to DC Converter Using Hybrid PSO Tuned Fuzzy Sliding Mode Controller
2016
Circuits and Systems
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The model of the hybrid particle swarm optimization tuned fuzzy sliding mode controller is implemented using Sim Power Systems toolbox of MATLAB SIMULINK. ...
In this paper, the dynamic and transient response of phase shift series resonant DC/DC converter are improved using hybrid particle swarm optimization tuned fuzzy sliding mode controller under starting ...
For every particle, evaluate the desired optimization fitness function. 3. Compare optimized fitness evaluation with pbest, which is the particle with best local fitness value. 4. ...
doi:10.4236/cs.2016.76080
fatcat:45nm7h2wrnejrbtf6x5mbpibde
Improving Pharmacological Research of HIV-1 Integrase Inhibition Using Differential Evolution - Binary Particle Swarm Optimization and Nonlinear Adaptive Boosting Random Forest Regression
2015
2015 IEEE International Conference on Information Reuse and Integration
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This comparative study uses a non-linear Random Forest Regression (RFR) strategy with Adaptive Boosting (AdaBoost) to generate QSAR models with greater predictive accuracy in identifying optimal Aryl β-Diketo ...
Swarm Optimization (DE-BPSO) algorithm to select and identify drug descriptors having the greatest inhibitory effect on HIV-1 Integrase, respectively. ...
This algorithm works by combining the mutation and recombination rules of Differential Evolution (DE) with a discretized version of Particle Swarm Optimization known as (Binary) Particle Swarm Optimization ...
doi:10.1109/iri.2015.80
dblp:conf/iri/GalvanHM15
fatcat:mhh7yyiewfhgtg6dyblw7l3v7i
Optimization Method Based Mppt For Wind Power Generators
2009
Zenodo
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This paper proposes the method combining artificial neural network with particle swarm optimization (PSO) to implement the maximum power point tracking (MPPT) by controlling the rotor speed of the wind ...
With the measurements of wind speed, rotor speed of wind generator and output power, the artificial neural network can be trained and the wind speed can be estimated. ...
PARTICLE SWARM OPTIMIZATION PSO is a population-based searching algorithm. ...
doi:10.5281/zenodo.1078399
fatcat:2cxdnzvmfnbo7jjzjiq6jl7yse
A fast converging particle swarm optimization through targeted, position-mutated, elitism (PSO-TPME)
[article]
2022
arXiv
pre-print
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We dramatically improve convergence speed and global exploration capabilities of particle swarm optimization (PSO) through a targeted position-mutated elitism (PSO-TPME). ...
The three key innovations address particle classification, elitism, and mutation in the cognitive and social model. ...
Beneficial to boost particle swarm diversity and mitigate the risk of falling into local optimum. ...
arXiv:2207.00900v2
fatcat:to6datxqp5hxzlz55aflwdapdq
Neural Networks And Particle Swarm Optimization Based Mppt For Small Wind Power Generator
2009
Zenodo
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This paper proposes the method combining artificial neural network (ANN) with particle swarm optimization (PSO) to implement the maximum power point tracking (MPPT) by controlling the rotor speed of the ...
Second, the method mentioned above is applied to estimate and control the optimal rotor speed of the wind turbine so as to output the maximum power. ...
PARTICLE SWARM OPTIMIZATION (PSO) PSO is a population-based searching algorithm. ...
doi:10.5281/zenodo.1334614
fatcat:6aoadfzktbernnjom65xbitd5a
Adaptive Hyperparameter Fine-Tuning for Boosting the Robustness and Quality of the Particle Swarm Optimization Algorithm for Non-Linear RBF Neural Network Modelling and Its Applications
2023
Mathematics
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A method is proposed for recognizing and predicting non-linear systems employing a radial basis function neural network (RBFNN) and robust hybrid particle swarm optimization (HPSO) approach. ...
A PSO is coupled with a spiral-shaped mechanism (HPSO-SSM) to optimize the PSO performance by mitigating its constraints, such as sluggish convergence and the local minimum dilemma. ...
Then, as the global finest fitness gbest of the whole swarm, estimate the finest fitness value. ...
doi:10.3390/math11010242
fatcat:urvkx34ugzd2vizsb5uexn3mg4
Maximum Power Point Tracking using modified Particle Swarm Optimization Technique
2022
International Journal for Research in Applied Science and Engineering Technology
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Keywords: Photovoltaic(PV), Particle Swarm Optimization(PSO), Modified Particle Swarm Optimization(MPSO), Partial shading condition(PSC), Velocity step function. ...
Hence, the proposed algorithm, which is based on the modified particle-swarm optimization (MPSO) technique, increases the output power of PV systems under such abnormal conditions and has a better performance ...
This work presents the performance analysis of Solar PV Boost converter fed circuit under constant irradiation conditions and under partial shading conditions. ...
doi:10.22214/ijraset.2022.42951
fatcat:4iuimprjsnf3jkw5dhnwdztrde
Combining Particle Swarm Optimization based Feature Selection and Bagging Technique for Software Defect Prediction
2013
International Journal of Software Engineering and Its Applications
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Particle swarm optimization is applied to deal with the feature selection, and bagging technique is employed to deal with the class imbalance problem. ...
Particle swarm optimization is applied to deal with the feature selection, and bagging technique is employed to deal with the class imbalance problem. ...
strategies, ant colony optimization and particle swarm optimization. ...
doi:10.14257/ijseia.2013.7.5.16
fatcat:ysrzxuwdhjhsxpctn5iqmykdla
A Swarm based Optimization of the XGBoost Parameters
2019
Australian Journal of Intelligent Information Processing Systems
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The Particle Swarm Optimization (PSO) algorithm was utilized to select the XGBoost parameters. The proposed method was deployed over a monthly rainfall prediction task. ...
The results have also shown that the XGBoost with PSO can be utilized as an efficient forecasting algorithm. ...
The particle swarm optimization algorithm was utilized to search for the optimal parameters that will reveal the best prediction accuracy. The swarm algorithm attributes were explored and analysed. ...
dblp:journals/ajiips/HaidarVH19
fatcat:x2q3l3ram5aj5lafv3kokjp7ia
Swarm-supported outdoor localization with sparse visual data
2010
Robotics and Autonomous Systems
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A modification of Particle Swarm Optimization, a popular optimization technique especially in dynamically changing environments, is developed and fit to the localization problem, including self-adaptive ...
The localization of mobile systems with video data is a challenging field in robotic vision research. ...
Theoretically, if the number of particles is very large, the particle filter estimate approaches the optimal Bayesian state estimate [1] , which is optimal with respect to the system models. ...
doi:10.1016/j.robot.2009.09.012
fatcat:3diz4qkq5jgkjfa5ixwku5ha7u
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