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12 results for “particle swarm”
Experimental Results for the study "A Modular Hybridization of Particle Swarm Optimization and Differential Evolution"
<p>This repository contains the experiment results and R scripts to analyze the data for the study "A Modular Hybridization of Particle Swarm Optimization andDifferential Evolution", which is accepted in <em>The Genetic and Evolutionary Computation Conference</em> (GECCO) '20 conference: </p> <p>Rick Boks, Hao Wang, and Thomas Bäck. 2020. A Modular Hybridization of Particle Swarm Optimization and Differential Evolution. In <em>Genetic and Evolutionary Computation Conference Companion (GECCO ’20 Companion), July 8–12, 2020, Cancún, Mexico. </em>ACM, New York, NY, USA, 8 pages. <a href="http://https: //doi.org/10.1145/3377929.3398123">https: //doi.org/10.1145/3377929.3398123</a></p> <p>Bibtex:</p> <pre><code class="language-markdown">@inproceedings{BoksWB20, author = {Rick Boks and Hao Wang and Thomas B\"ack}, title = {{A Modular Hybridization of Particle Swarm Optimization and Differential Evolution}}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, {GECCO} 2020, Canc\'un, Mexico, July 8-12, 2020}, publisher = {{ACM}}, year = {2020}, url = {https://doi.org/10.1145/3321707.3321816}, doi = {doi.org/10.1145/3377929.3398123, }</code></pre> <p><strong>Data description:</strong> we benchmarked <strong>800 </strong>different<strong> </strong>hybridizations of the Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms on a well-known continuous black-box problem set called <a href="https://coco.gforge.inria.fr/">COCO/BBOB</a>, which consists of 24 test functions. 30 independent runs are conducted for each algorithm on each problem.</p> <ul> <li>'ERT.csv': a data frame with columns DIM (5D or 20D), funcId (F1-24), algId (algorithm names), target (<span class="math-tex">\(10^{\{-8,-7, \ldots, 1\}}\)</span>), ERT (expected running time), and sd (standard deviation).</li> <li>'raw-data.csv': the running time recorded in each independent run. </li> <li>'analysis.R': the R script that generates ERT tables in the paper.</li> <li>'ecdf.R': the R script that renders the ECDF (empirical cumulative distribution function) plots in the paper.</li> </ul>
Data and Code for: Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals
<p>This repository contains the datasets and scripts used to obtain the figures of the paper "Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals".</p> <p>The repository is organized as follows:<br> - Part I) Monte Carlo simulation codes</p> <p>- Part II) Monte Carlo simulations using the parameter configuration "Param. 1" of Shi et al. (1999)</p> <p>- Part III) Monte Carlo simulation using the parameter configuration "Param. 1" of Shi et al. (1999) and a limited ion composition search space</p> <p>- Part IV) Monte Carlo simulations using the parameter configuration "Param. 2" of Wang et al. (2012)</p> <p>- Part V) Monte Carlo simulation using the parameter configuration "Param. 2" of Wang et al. (2012) and a limited ion composition search space</p> <p>- Part VI) Codes to generate all figures of the manuscript</p> <p>All datasets and scripts were generated and tested using Matlab 2017. Simulations have been executed in parallel on a SLURM cluster, compilation and running scripts are provided.</p>
Figures -using Particle Swarm Optimization (PSO)-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>The performance of WML quantification is evaluated using clustering algorithms. When the<br> image is pre-processed, contrast of the image is enhanced. The resulting enhanced image is<br> clustered using the effective clustering algorithms. Figure 3 represents the input image for WML<br> detection. In order to increase robustness, the noisy medical image is pre-processed. Figure 4<br> depicts the pre-processed image. Bright contrast stretching, which is one of the image enhancement<br> (pre-processing) techniques is applied. After pre-processing the enhanced image is subjected to<br> clustering. Three clustering models are proposed to provide accurate results.</p> <p>All scans obtained from different image clustering models are manually ranked based on<br> values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and<br> GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO)<br> are applied on a dataset of 208 images and ranking is done in terms of under detected, over<br> detected, properly detected as shown in figure 8 and figure 9. The number of images detected<br> properly in GFCM is comparatively high than FCM and GPC.</p>
A niching particle swarm optimization strategy combined with cluster analysis for the multimodal inversion of surface waves
<p>The data include two study cases used for multimodal surface wave inversion.</p> <p>For case 1, the data present a combination of active and passive surface wave methods.</p> <p>For case 3, we use Rayleigh waves to detect a low-velocity soft interlayer underneath the road.</p> <p>Detailed description can be found in the data description document.</p>
Datasets for Application of particle swarm optimization to understand the mechanism of action of allosteric inhibitors of the enzyme HSD17ß13
<p>Datasets used in the publication '</p> <p>Application of particle swarm optimization to understand the</p> <p>mechanism of action of allosteric inhibitors of the enzyme HSD17ß13'</p>
Data and Code for: Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals
<p>This repository contains the datasets and scripts used to obtain the figures of the paper "Performance Evaluation of the Particle Swarm Optimization Algorithm to Unambiguously Estimate Plasma Parameters from Incoherent Scatter Radar Signals".</p> <p>The repository is organized as follows:<br> - Part I) Monte Carlo simulation codes</p> <p>- Part II) Monte Carlo simulations using the parameter configuration "Param. 1" of Shi et al. (1999)</p> <p>- Part III) Monte Carlo simulation using the parameter configuration "Param. 1" of Shi et al. (1999) and a limited ion composition search space</p> <p>- Part IV) Monte Carlo simulations using the parameter configuration "Param. 2" of Wang et al. (2012)</p> <p>- Part V) Monte Carlo simulation using the parameter configuration "Param. 2" of Wang et al. (2012) and a limited ion composition search space</p> <p>- Part VI) Monte Carlo simulations using the parameter configuration "Param. 2" of Wang et al. (2012) with uncertainty on the a priori plasma parameters obtained from the Plasma Line</p> <p>- Part VII) Codes to generate all figures of the manuscript</p> <p>All datasets and scripts were generated and tested using Matlab 2017. Simulations have been executed in parallel on a SLURM cluster, compilation and running scripts are provided.</p>
Improved hybrid particle swarm optimizer with sine-cosine acceleration coefficients for transient electromagnetic inversion
<p>The Xishan Landslide Research area is located in Sichuan Lixian County, Southwest China. </p> <p>The N.O. 1 profile from Li et al., (2020), containing 15 measuring points, was chosen to test data fitting of IH-PSO-SCAC and to determine the hydrogeological structure of landslides.</p> <p> </p>
Multi-objective particle swarm optimization and training of datasheet-based load dependent lithium-ion voltage models
Open the record for dataset details and reuse information.
Enhanced Weighted Quantum Particles Swarm Optimization in Electromagnetic Contenst
<p>This article describes how the EWQPSO (Enhanced Weigthed Quantum Particle Swarm Optimization) optimization algorithm can also be used in an electromagnetic context for the circuit synthesis of an antenna (in this case of a sinuous antenna) and how, this version of the algorithm , is better than its previous versions (WQPSO, QPSO and PSO).</p>
Improved hybrid particle swarm optimizer with sine-cosine acceleration coefficients for transient electromagnetic inversion(2)
<p>The datas are gained in<Nonlinear programming genetic algorithm in transient electromagnetic inversion>. Li F P, Yang H Y, Liu X H, et al.</p> <p>Geophysical and Geochemical Exploration,2017,41(2) :347-353.</p> <p>http://doi.org /10.11720/wtyht.2017.2.24</p>
Multiple Objective Particle Swarm Optimization Postural Instability Gait Disorder
ClinicalTrials.gov study NCT05893186. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Aim 3 Particle Swarm Optimization PIGD
ClinicalTrials.gov study NCT05934747. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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Allen Brain Atlas
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.