Filters








5,083 Hits in 4.2 sec

A particle swarm optimization approach for estimating parameter confidence regions

Praveen Koduru, Stephen M. Welch, Sanjoy Das
2007 Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO '07  
In this paper, we propose a contour particle swarm optimization (C-PSO) technique and compare its performance against UCPR in predicting the confidence regions.  ...  However, parameter comparisons and/or statistical inference requires determination of parameter space confidence regions in addition to point estimates.  ...  A special case of this problem is when the levels are defined from probabilities, thus delimiting confidence regions for point parameter estimates.  ... 
doi:10.1145/1276958.1276969 dblp:conf/gecco/KoduruWD07 fatcat:ydfy75xktvhm7cq75fotjsejca

Dynamic Optimization with Particle Swarms (DOPS): a meta-heuristic for parameter estimation in biochemical models

Adithya Sagar, Rachel LeCover, Christine Shoemaker, Jeffrey Varner
2018 BMC Systems Biology  
DOPS is a promising meta-heuristic approach for the estimation of biochemical model parameters in relatively few function evaluations.  ...  Toward these challenges, we developed Dynamic Optimization with Particle Swarms (DOPS), a novel hybrid meta-heuristic that combined multi-swarm particle swarm optimization with dynamically dimensioned  ...  The grey shaded region represents the 99% confidence estimate of the mean simulated thrombin concentration.  ... 
doi:10.1186/s12918-018-0610-x pmid:30314484 pmcid:PMC6186122 fatcat:rujvtulmsrhwjpbxfcit6m37ym

Dynamic Optimization with Particle Swarms (DOPS): A meta-heuristic for parameter estimation in biochemical models [article]

Jeffrey Varner, Adithya Sagar, Rachel LeCover, Christine Shoemaker
2017 bioRxiv   pre-print
Conclusions: DOPS is a promising meta-heuristic approach for the estimation of biochemical model parameters in relatively few function evaluations.  ...  Toward these challenges, we developed Dynamic Optimization with Particle Swarms (DOPS), a novel hybrid meta-heuristic that combined multi-swarm particle swarm optimization with dynamically dimensioned  ...  The grey shaded region represents the 99% confidence estimate of the mean simulated thrombin concentration.  ... 
doi:10.1101/240580 fatcat:xnvtl22j75b4jpfv3ocbdhehje

q-Gaussian swarm quantum particle intelligence on predicting global minimum of potential energy function

Hiqmet Kamberaj
2014 Applied Mathematics and Computation  
In additional, we also provide a method for optimally allocating the swarm replicas among different q values.  ...  We present a newly developed Replica Exchange algorithm using q -Gaussian Swarm Quantum Particle Optimization (REX@q-GSQPO) method for solving the problem of finding the global optimum.  ...  Acknowledgements The author would like to thank the International Balkan University for the support.  ... 
doi:10.1016/j.amc.2013.12.036 fatcat:bovrr3qs7jgvzjthyu74wnzute

Parameter Estimation in Bayesian Networks Using Overlapping Swarm Intelligence

Nathan Fortier, John Sheppard, Shane Strasser
2015 Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15  
We introduce two new methods for parameter estimation in Bayesian networks based on particle swarm optimization (PSO). The first is a single swarm PSO, while the second is a multiswarm PSO algorithm.  ...  While Expectation Maximization (EM) is commonly used to perform parameter estimation in the context of latent variables, EM is a local optimization method that often converges to sub-optimal estimates.  ...  We propose a new algorithm for latent variable parameter estimation in Bayesian networks based on particle swarm optimization (PSO).  ... 
doi:10.1145/2739480.2754793 dblp:conf/gecco/FortierSS15 fatcat:eljoo33rqfadfavpviveojtf3q

A Robust Hybrid Multisource Data Fusion Approach for Vehicle Localization

Adda Redouane Ahmed Bacha, Dominique Gruyer, Alain Lambert
2013 Positioning  
This approach called Optimized Kalman Swarm (OKS) is a data fusion and filtering method, fusing data from a low cost GPS, an INS, an Odometer and a Steering wheel angle encoder.  ...  The OKS filter represents an intelligent cooperative-reactive localization algorithm inspired by dynamic Particle Swarm Optimization (PSO).  ...  We will start with an overview of the Particle Swarm Optimization. Then, we will present the Particle Filter (PF) approach.  ... 
doi:10.4236/pos.2013.44027 fatcat:s7t5hpimmvcs7mgxpfmycbzo6m

Robust Tracking Based on PSO and On-line AdaBoost

Huchuan Lu, Wenling Zhang, Fan Yang, Xiaojing Wang
2009 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing  
This new tracking framework, which is initialized with the region of the object at the first few frames, can automatically track the object at the remaining frames.  ...  Robust tracking is a challenging problem, due to intrinsic appearance variability of objects caused by inplane or out-plane rotation and extrinsic factors change such as illumination, occlusion, background  ...  In the interests of simplicity, advanced to the next frame, we adopt the particle swarm optimization (PSO) for tracking.  ... 
doi:10.1109/iih-msp.2009.37 dblp:conf/iih-msp/LuZYW09 fatcat:26dd5cddxbg2zpifw2plrmo2hi

Evolutionary Particle Swarm Optimization: A Metaoptimization Method with GA for Estimating Optimal PSO Models [chapter]

Hong Zhang, Masumi Ishikawa
2008 Lecture Notes in Electrical Engineering  
Sometimes particle swarm searches well by chance, even if parameter values are not appropriate for solving a given optimization problem.  ...  It declares that the swarm confidence factor plays an important role in finding a global optimal solution.  ... 
doi:10.1007/978-0-387-74935-8_5 fatcat:3vcenmuozjf7vhkxnv73azgeey

ParticleChromo3D: A Particle Swarm Optimization Algorithm for Chromosome and Genome 3D Structure Prediction from Hi-C Data [article]

David Vadnais, Michael Middleton, Oluwatosin Oluwadare
2021 bioRxiv   pre-print
Here, we propose a novel approach for 3D chromosome and genome structure reconstruction from Hi-C data using Particle Swarm Optimization approach called ParticleChromo3D.  ...  This algorithm begins with a grouping of candidate solution locations for each chromosome bin, according to the particle swarm algorithm, and then iterates its position towards a global best candidate  ...  ParticleChromo3D uses the Particle Swarm Optimization algorithm as the foundation of its solution approach for 3D chromosome reconstruction from Hi-C data.  ... 
doi:10.1101/2021.02.11.430871 fatcat:y3k6n5mbbvbt5ocncgpfsbpnl4

Multi-Scale Parameter Identification of Lithium-Ion Battery Electric Models Using a PSO-LM Algorithm

Wen-Jing Shen, Han-Xiong Li
2017 Energies  
This paper proposes a multi-scale parameter identification algorithm for the lithium-ion battery (LIB) electric model by using a combination of particle swarm optimization (PSO) and Levenberg-Marquardt  ...  In the proposed identification algorithm, a hybrid multi-swarm PSO algorithm [21] is first applied for the coarse-scale searching in the global space to find near-optimal parameter values.  ...  For a particle swarm with a population consisting of M n-dimensional particles, the velocity v m i,j and position x m i,j , i ∈ {1, 2, · · · , M}, j ∈ {1, 2, · · · , n} of the j-th dimension of the i-th  ... 
doi:10.3390/en10040432 fatcat:5wcwctedi5cnhf2pvajsre7ice

Constricted Particle Swarm Optimization based Algorithm for Global Optimization

Gonzalo Nápoles, Isel Grau, Rafael Bello
2012 POLIBITS Research Journal on Computer Science and Computer Engineering With Applications  
Particle Swarm Optimization (PSO) is a bioinspired meta-heuristic for solving complex global optimization problems.  ...  Once the particles have been attracted to a local optimum, they continue the search process within a minuscule region of the solution space, and escaping from this local optimum may be difficult.  ...  Ricardo Grau Abalo, from the Centre of Studies on Informatics, UCLV, for fruitful discussions and statistical advice.  ... 
doi:10.17562/pb-46-1 fatcat:45ayd4h4mbh6zclu5zfzblz3eu

Object Tracking via Multi-region Covariance and Particle Swarm Optimization

Bogdan Kwolek
2009 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance  
In this paper a particle swarm optimization based algorithm for object tracking in surveillance videos is proposed.  ...  The optimization aims at shifting the particles towards more promising regions in the search area. The region covariance is utilized in evaluation of the particle score.  ...  For this purpose an approach relying on Particle Swarm Optimization (PSO) has already been proposed in [5] . Particle swarm optimization was introduced by Kennedy and Eberhart [6] .  ... 
doi:10.1109/avss.2009.19 dblp:conf/avss/Kwolek09 fatcat:oasyzxklbrczhcbutf77za4gme

Unsupervised learning of background modeling parameters in multicamera systems

Konstantinos Tzevanidis, Antonis Argyros
2011 Computer Vision and Image Understanding  
The maximization of this fitness function through Particle Swarm Optimization leads to the adjustment of the foreground detection parameters.  ...  In this work we propose a novel, fully automatic method for the tuning of foreground detection parameters in calibrated multicamera systems.  ...  One such approach is Particle Swarm Optimization (PSO) [14] .  ... 
doi:10.1016/j.cviu.2010.09.003 fatcat:ugms4hw4xzd65ck7cwiigvhtva

OKPS: A Reactive/Cooperative Multi-Sensors Data Fusion Approach Designed for Robust Vehicle Localization

Adda Redouane Ahmed Bacha, Dominique Gruyer, Alain Lambert
2016 Positioning  
It combines the advantages of the Particle Filter (PF) and the metaheuristic Particle Swarm Optimization (PSO) for ego-vehicles localization applications.  ...  In addition to a simple fusion between the swarm optimization and the particular filtering (which leads to the Swarm Particle Filter), the OKPS uses some attributes of the Extended Kalman filter (EKF).  ...  Firstly intended for simulating social behavior, the Particle Swarm Optimization (PSO) [21] , is a metaheuristic optimization method improved for iterative optimization issues [22] - [25] .  ... 
doi:10.4236/pos.2016.71001 fatcat:hlbnnliohfbotgo7wchc3bqfdu

Replica Exchange using q-Gaussian Swarm Quantum Particle Intelligence Method [article]

Hiqmet Kamberaj
2013 arXiv   pre-print
We present a newly developed Replica Exchange algorithm using q -Gaussian Swarm Quantum Particle Optimization (REX@q-GSQPO) method for solving the problem of finding the global optimum.  ...  We compare the new algorithm with the standard Gaussian Swarm Quantum Particle Optimization (GSQPO) and q-Gaussian Swarm Quantum Particle Optimization (q-GSQPO) algorithms, and we found that the new algorithm  ...  Acknowledgements The author would like to thank the International Balkan University for the support.  ... 
arXiv:1312.7326v1 fatcat:owh5runbjbc7xnnm6xn3hy3rl4
« Previous Showing results 1 — 15 out of 5,083 results