Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
29
datasets available to search
ShareScore release 0.9.0
Dataset results
29 results for “Multi-objective optimization”
Dataset of multi-objective optimization results for a new latent energy storage approach in buildings based on several phase change materials with different melting temperatures
<p>This dataset comprises the multi-objective optimization results obtained for a new latent energy storage approach based on several phase change materials (PCMs) with different melting temperatures in buildings. The results were obtained for a small office building in eight climate-representative locations according to the ASHRAE 169-2020 climate classification and within the WMO Region VI (Europe).</p> <p>The dataset contains:</p> <p>- The EnergyPlus baseline models employed as a case study for each climate.</p> <p>- The Pareto fronts obtained after the multi-objective optimization in each climate.</p> <p>- The EnergyPlus models for the best designs achieved on the Pareto fronts in terms of annual total load reductions.</p>
Reference dataset of multi-objective and multi-fidelity optimization in laser-plasma acceleration
<p>This repository contains a dataset used for the article "<em>Multi-objective and multi-fidelity Bayesian optimization of laser-plasma acceleration</em>" (<a href="https://arxiv.org/abs/2210.03484">arXiv:2210.03484</a>). The dataset consists of 2443 FBPIC particle-in-cell simulations of a laser wakefield accelerator that were selected using a Bayesian optimizer. The goal of the optimization was to perform multi-objective multi-fidelity optimization of electron beam parameters. The dataset contains simulations of different resolutions, accordingly with differing fidelities. The typical runtime at lowest (highest) resolution is approximately 1 (90) minutes.</p> <p>In the dataset we have <em>train_x </em>and <em>train_obj </em>numpy arrays with dimensions <em>(n,5)</em> and<em> (n,3)</em>, respectively. Here <em>n</em> is the number of FBPIC simulations. The five columns in <em>train_x </em>are [plasma density, upramp length, laser focus, downramp length, fidelity]. The fidelity parameter controls the resolution and hence the runtime of the simulation. The three columns in the <em>train_obj </em>are the [total charge, distance of median to target energy, bandwidth of electron beams]. For the distance, the target energy is fixed to 300 MeV and for the bandwidth is defined by the median absolute deviation around the median. The two columns have negative values since the optimizer assumes a maximization of all objectives while the distance and bandwidth in this study were being minimized.</p> <p>The different folders contain data of different kind of single and multi-objectives that were used to produce figures 2, 3, 5 in the associated paper. For more details please see the referred article. The folder "combined" contains the data of all simulations together and is most suitable for (5D x 3D) surrogate model generation.</p>
Materials Science Optimization Benchmark Dataset for Multi-Objective, Multi-Fidelity Optimization of Hard-Sphere Packing Simulations
<p>Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a “Turing test” of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In the fields of materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 494498 random hard-sphere packing simulations representing 206 CPU days worth of computational overhead were performed across nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters and results were logged to a free-tier shared MongoDB Atlas database. Two core tabular datasets resulted from this study: 1. a failure probability dataset containing unique input parameter sets and the estimated probabilities that the simulation will fail at each of the two steps, and 2. a regression dataset mapping input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets are used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This is in contrast with a more traditional approach that imposes a-priori assumptions such as Gaussian noise e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.</p> <p>For usage instructions, see https://matsci-opt-benchmarks.readthedocs.io/.</p>
ENTICE Multi-objective optimization framework synthetic evaluation data-sets
<p>This dataset contains synthetic usage data for evaluation of the multi-objective redistribution framework for distributed VMI repositories. </p> <p>The data is stored in a Java object and it should be directly loaded. </p> <p> </p>
Supplementary Data: Data Imbalance in Drug Response Prediction: Multi-Objective Optimization Approach in Deep Learning Setting
Open the record for dataset details and reuse information.
Experimental Results for the study "The Hypervolume Newton Method for Constrained Multi-Objective Optimization Problems"
<p>This repository contains the experiment results (raw data in NPZ and CSV format and Latex tables) for the study "The Hypervolume Newton Method for Constrained Multi-Objective Optimization Problems", which is accepted in <em>Mathematical and Computational Applications</em> journal.</p> <p>The preprint version of the related paper is already online: </p> <p> </p> <p>Wang, H.; Emmerich, M.; Deutz, A.; Hernández, V.A.S.; Schütze, O. The Hypervolume Newton Method for Constrained Multi-objective Optimization Problems. <em>Preprints</em> <strong>2022</strong>, 2022110103 (doi: <a href="http://10.20944/preprints202211.0103.v1">10.20944/preprints202211.0103.v1</a>).</p> <p><strong>Data description:</strong> we benchmarked<strong> </strong>three algorithms: (1) the standalone <strong>Hypervolume Netwon Method</strong> (HVN), (2) NSGA-III, and (3) the <strong>hybridization</strong> of the standalone HVN and NSGA-III on several artificial problems.</p> <ul> <li>For the standalone HVN algorithm, we tested it on three simple artificial test problems - P1, P2, and P3 (proposed in the above paper): <ul> <li>2D-example-50*.tex: problem P1</li> <li>3D-example1*.tex: problem P2</li> <li>3D-example2*.tex: problem P3</li> </ul> </li> <li>For NSGA-III and the hybridization, we tested them on the equality-constrained DTLZ and Inverted DTLZ (IDTLZ) problems: <ul> <li>Eq1DTLZ.*npz: DTLZ problems</li> <li>Eq1IDTLZ*.npz: IDTLZ problems</li> </ul> </li> </ul>
Materials Science Optimization Benchmark Dataset for High-dimensional, Multi-objective, Multi-fidelity Optimization of CrabNet Hyperparameters
Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a "Turing test" of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 173219 quasi-random hyperparameter combinations were generated across 23 hyperparameters and used to train CrabNet on the Matbench experimental band gap dataset. The results were logged to a free-tier shared MongoDB Atlas dataset. This study resulted in a regression dataset mapping hyperparameter combinations (including repeats) to MAE, RMSE, computational runtime, and model size for CrabNet model trained on the Matbench experimental band gap benchmark task1. This dataset is used to create a surrogate model as close as possible to running the actual simulations by incorporating heteroskedastic noise. Failure cases for bad hyperparameter combinations were excluded via careful construction of the hyperparameter search space, and so were not considered as was done in prior work. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This contrasts with a more traditional approach that imposes a-priori assumptions such as Gaussian noise, e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.
Multi-Objective Optimization of Load Test Designs
<p>Supplementary material for the paper: "Multi-Objective Optimization of Load Test Designs".</p> <p>Included in the supplementary material are the evaluation results and the proof and concept implementation.</p>
Data supporting "Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables"
<p>This repository contains the set of data and the code to reproduce the results shown in "Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables" published on Journal of Chemical Engineering and Data (DOI: 10.1021/acs.jced.3c00538).</p>
Research data supporting ""Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables""
<p>This repository contains the set of data and the code to reproduce the results shown in "Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables" published on Journal of Chemical Engineering and Data (DOI: 10.1021/acs.jced.3c00538).</p>
Multi-objective optimization of a renewable fuel supply chain regarding cost, land use, and water use - Supplementary Materials
<p><strong>This repository contains supporting data for: "Multi-objective optimization of a renewable fuel supply chain regarding cost, land use, and water use"</strong></p> <ol> <li> <p><strong>Input Data</strong>: This file includes the model input data.</p> </li> <li> <p><strong>Payoff Matrix Solution</strong>: This file contains all decision variables of payoff matrix solutions (Cost optimal, Land-use optimal, and Water-use optimal solution).</p> </li> <li><strong>Compromise solution</strong>: This file contains all decision variables of the compromise solution.</li> <li> <p><strong>Pareto Curve</strong>: This file presents the Pareto curve solution. </p> </li> <li><strong>Pareto Curve Solution</strong>: This file contains all decision variables in each of the Pareto curve solutions. </li> </ol>
Multi-objective optimization of equation of state molecular parameters: SAFT-VR Mie models for water
<p>Supporting document containing information on the Pareto points and thermodynamic calculations.</p>
When and How to Transfer Knowledge in Dynamic Multi-objective Optimization
<p>This file is the output data obtained when running the experiments of the paper below:</p> <p>Ruan, G., Minku, L.L., Menzel, S., Sendhoff, B., Yao, X., "When and How to Transfer Knowledge in Dynamic Multi-objective Optimization," 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 2019, pp. 2034-2041. </p> <p> </p> <p>Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is able to transfer useful information from one problem to help solving another related problem. This paper aims to investigate when and how transfer learning works or fails in dynamic multi-objective optimization. Through computational analyses on a number of dynamic bi- and tri-objective benchmark problems, we show that transfer learning fails on problems with fixed Pareto optimal solution sets and under small environmental changes. We also show that the Gaussian kernel function used in the existing transfer learning-based method is not always adequate. Therefore, transfer learning should be avoided when dealing with problems for which transfer learning fails and other kernel functions should be used when the Gaussian kernel is inadequate. This paper proposes novel strategies and kernel functions that can be used in such cases. Experimental studies have demonstrated the superiority of our proposed techniques to state-of-the-art methods, on a number of dynamic bi- and tri-objective test problems.</p>
Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization
<p>This file is the output data obtained when running the experiments from the paper below:</p> <p>Ruan, G., Minku, L., Menzel, S., Sendhoff, B., Yao., “Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization” <em>2020 IEEE Congress on Evolutionary Computation</em></p> <p>Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is believed to be able to transfer useful information from one problem instance to help solving another related problem instance. This paper aims to study how effective transfer learning is in dynamic multi-objective optimization (DMO). Through computation time analysis of transfer learning, we show that the ‘inner’ optimization problem introduced by transfer learning is very time-consuming. In order to enhance the efficiency, two alternatives are computationally investigated on a number of dynamic bi- and tri-objective test problems. Experimental results have shown that the greatly enhanced efficiency does not result in much degeneration on the performance of transfer learning. Considering the high computational cost of transfer learning, it is likely that the original purpose of using transfer learning in DMO might be negated. In other words, the computation time saved in optimization is eaten up by computationally expensive transfer learning. As a result, there is less gain than expected in the overall computational efficiency. To verify this, experiments have been conducted, regarding using computational cost of transfer learning to optimize randomly generated solutions. The results have demonstrated that the convergence and diversity of final solutions generated from the random solutions are significantly better than those generated from transferred solutions under the same total computational budget.</p>
Data from: Multi-objective optimization for plant germplasm collection conservation of genetic resources based on molecular variability
Germplasm collections play a significant role among strategies for conservation of diversity. It is common to select a core collection to represent the genetic diversity of a germplasm collection, in order to minimize the cost of conservation, while ensuring the maximization of genetic variation. We aimed to solve two main problems: (1) to select a set of individuals, from an in situ data set, that is genetically complementary to an existing germplasm collection, and (2) to define a core collection for a germplasm collection. We proposed a new multi-objective optimization (MOO) approach based on principles of systematic conservation planning (SCP) incorporating heterozygosity information; therefore, optimization takes genotypic diversity and variability patterns into account as well. As a case study, we used Dipteryx alata microsatellite loci information from two sources, an ex situ germplasm collection located at the Agronomy School of the Federal University of Goiás (UFG-AS), and an in situ data set composed of 642 sampled individual trees. We were able to identify within a population of several individuals, the exact accessions/samples that should be chosen in order to preserve the species diversity. We found that material from nine in situ individual trees are enough to complement the UFG-AS germplasm collection as it is, and that it is possible to define a core collection of 20 individual trees representing all studied genetic diversity. Moreover, we defined a method (a protocol) to deal with large amounts of accessions in the context of MOO. The proposed approach can be used to help constructing collections with maximal allelic richness and can also be extended to the in situ conservation. As far as we know, this is the first time that principles of SCP and the MOO approach are applied to the problem of complementing a germplasm collection and of finding a core collection for a germplasm collection.
Distributed Multi-objective Optimization in Cyber-Physical Energy Systems
<p>The data includes results for a distributed multi-objective optimization in Cyber-Physical Energy Systems. The respective implementation for the scenarios can be found here: https://github.com/Digitalized-Energy-Systems/MOO-CPES/releases/tag/Distributed_Multi-objective_Optimization_in_Cyber-Physical_Energy_Systems<br>In this case, a multi-agent system exists for the optimization in which agents represent chp units or wind plants. For the optimization using the agents, the agents have to fulfill a target schedule, with contains of the sum of all unit schedules. Regarding the target schedule, three objectives are considered: minimizing the difference between the<br>produced power in sum and the given target schedule, minimizing the emissions and minimizing the uncertainties.</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.
Data used for analysis in "Calibrating tropical forest coexistence in ecosystem demography models using multi-objective optimization through population-based parallel surrogate search"
Open the record for dataset details and reuse information.
Multi-Scale Computational Design of Metal-Organic Frameworks for Carbon Capture Using Machine Learning and Multi-Objective Optimization
<p>This repository contains CIF files for metal-organic frameworks and Grand canonical Monte Carlo (GCMC) simulation results for the article <em>Multi-Scale Computational Design of Metal-Organic Frameworks for Carbon Capture Using Machine Learning and Multi-Objective Optimization</em> by Zijun Deng and Lev Sarkisov.</p>
Multi-objective near-optimal necessary conditions for multi-sectoral planning - Dataset
<p>Dataset (input and output) of the paper 'Multi-objective near-optimal necessary conditions for multi-sectoral planning'</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.