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13 results for “Pareto”
Multi-Objective Design of Actuators: Pareto fronts
<p>These are the best-known Pareto fronts for the 20 MODAct benchmark problems. Files are text files where each row is a point and each column an objective.</p> <p>Associated publication is under review.</p>
Dataset: Employing the Generalized Pareto Distribution to Analyze Extreme Rainfall Events on Consecutive Rainy Days in Thailand's Chi Watershed: Implications for Flood Management
<p>This data set is used to employing the generalized Pareto distribution to analyze extreme rainfall events on consecutive rainy days in Thailand's Chi watershed. A case of implications for flood management. Observational raw data from Thailand were provided by the Climate Information Services (CIS) at https://www.tmd.go.th/cis/main.php.</p>
Pareto front of the Bio-SOFC plant (Case 5) optimization
<p>Datasets of the article "Techno-Economic Optimization of an Integrated Biomass Waste Gasifier–Solid Oxide Fuel Cell Plant".</p> <p>With a growing energy demand in a carbon-constrained society, fuels cells powered by renewable fuels, and specifically solid waste, are seen as interesting contributors to the energy portfolio. The alternative energy industry needs to reduce costs, enhance efficiency, and demonstrate durability and reliability to be economically feasible and attractive. This paper addresses biomass waste gasification in distributed energy systems, using a solid oxide fuel cell (SOFC) to produce electricity and heat. The potential and optimal plant efficiency and layout (i.e., anode off-gas (AOG) recirculation point <em>via</em> small-scale turbomachinery and heat exchanger network) are analyzed through a multi-stage approach that includes scenario evaluation and multi-objective optimization <em>via</em> a hybrid optimization strategy with heuristics and mathematical programming. The results in this paper summarize the most convenient operating conditions and provide an optimized heat exchanger network (HEN). The AOG recirculation toward the gasifier combustor is the preferred option; the electrical and thermal efficiencies can separately go up to 49 and 47%, respectively. The combined total efficiency ranges between 76 and 82%, and the area of heat exchange, which corresponds to an amount of heat exchanged between 91 and 117 kW, is within 6–14 m<sup>2</sup>.</p>
Radiative-transfer dataset for "Distilling machine learning's added value: Pareto fronts in atmospheric applications"
<p>This dataset goes with the journal paper "Distilling machine learning's added value: Pareto fronts in atmospheric applications" by T. Beucler, A. Grundner, S. Shamekh, P. Ukkonen, M. Chantry, and R. Lagerquist.</p> <p>Subdirectory "training" contains unnormalized (in physical units) training data. Subdirectories "validation" and "testing" contain unnormalized validation and testing data. Subdirectory "training/for_pareto_paper_2024/simple" contains training data from the simple (clear-sky) dataset discussed in the paper; subdirectory "training/for_pareto_paper_2024/complex" contains training data from the complex (multi-cloud) dataset discussed in the paper. Subdirectories "validation/for_pareto_paper_2024/simple" and "validation/for_pareto_paper_2024/complex" are analogous but for the validation data; subdirectories "testing/for_pareto_paper_2024/simple" and "testing/for_pareto_paper_2024/complex" are analogous but for the testing data.</p> <p>Subdirectories beginning with "normalized_predictors" -- "normalized_predictors/training", "normalized_predictors/validation", "normalized_predictors/testing", "normalized_predictors/training/for_pareto_paper_2024/simple", "normalized_predictors/training/for_pareto_paper_2024/complex", etc. -- are analogous to the above but containing normalized predictors (in z-scores rather than physical units).</p> <p>Every file -- after unzipping, so that the extension is ".nc" rather than ".nc.gz" -- can be read by `example_io.read_file` in the ml4rt library (https://github.com/thunderhoser/ml4rt).</p>
Figure 2. Pareto diagram for L in Growth aspect of squid (Loligo chinensis) from the Banyuasin Coastal Waters, South Sumatra, Indonesia
Figure 2. Pareto diagram for L. chinensis from Banyuasin Coastal Waters of South Sumatra. This diagram indicated a graphical overview of frequency distribution in ranking order from the most frequent to the least frequent. The first length group at X-axis (101-110 mm) showed the most frequently that the specimen was found.
Dataset (Pareto fronts and sets) for the Multi-objective pathfinding problem
<p>The dataset was used in several publications. In contains global Pareto fronts and sets for various instances of the problem.</p>
Pareto Fronts and metrics for the optimization design water distribution networks (Hanoi, Fossolo, Modena, and Balerma)
<p>Approximations to the Best-known PFs using NSGA-II retrofitted by OPUS for Hanoi, Fossolo, Modena, and Balerma, Approximations to the Best-known PFs using NSGA-II for Hanoi, Fossolo, Modena and Balerma, Hypervolume (HV) and Modified Inverted Generational Distance (IGD+) for Hanoi, Fossolo, Modena, and Balerma.</p> <ul> <li>Hanoi (30 simulations)</li> <li>Fossolo (30 simulations)</li> <li>Modena (1 simulation)</li> <li>Balerma (1 simulation)</li> </ul>
Data for GECCO2023 Paper "Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness"
<p><strong>Data for Paper "Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness"</strong></p> <ul> <li><strong>instances.tar.xz</strong> contains 𝜌mnk-landscape instances</li> <li><strong>metrics.csv</strong> contains the (C)PLOS-net metric-values</li> <li><strong>performance.csv</strong> contains the performance of the different algorithms on each instance</li> <li><strong>merged.csv</strong> contains the merged data from the 2 csv files above</li> </ul> <p><strong>Reference</strong></p> <p>Arnaud Liefooghe, Gabriela Ochoa, Sébastien Verel, and Bilel Derbel. 2023. <strong>Pareto Local Optimal Solutions Networks with Compression, Enhanced Visualization and Expressiveness</strong>. In Genetic and Evolutionary Computation Conference (GECCO ’23), July 15–19, 2023, Lisbon, Portugal. ACM, New York, NY, USA, 9 pages. <a href="https://doi.org/10.1145/3583131.3590474">https://doi.org/10.1145/3583131.3590474</a></p> <p><strong>Abstract</strong></p> <p>The structure of local optima in multi-objective combinatorial optimization and their impact on algorithm performance are not yet properly understood. In this paper, we are interested in the representation of multi-objective landscapes and their multi-modality. More specifically, we revise and extend the network of Pareto local optimal solutions (PLOS-net), inspired by the well-established local optima network from single-objective optimization. We first define a compressed PLOS-net which allows us to enhance its perception while preserving the important notion of connectedness between local optima. We then study an alternative visualization of the (compressed) PLOS-net that focuses on good-quality solutions, improves the distinction between connected components in the network, and generalizes well to landscapes with more than 2 objectives. We finally define a number of network metrics that characterize the PLOS-net, some of them being strongly correlated with search performance. We visualize and experiment with small-size multi-objective nk-landscapes, and we disclose the effect of PLOS-net metrics against well-established multi-objective local search and evolutionary algorithms.</p>
Pareto-OptimaL allocatIon of Continuous IntEgration Resources
<p>Pareto-OptimaL allocatIon of Continuous IntEgration Resources (POLICIER) replication package</p>
Results for the Pareto analysis on the POTG Normal Instances
<p>The .xlsx file contains the results for the multi-objective POTG (double drop, Hybrid ILS heuristic) formulation. For each scenario, all dominated and non-dominated solutions are reported for the normal instances which are available at https://zenodo.org/records/10032290.</p> <p>The sizing configuration, cost and total wait times for 1. traditional charging station and 2. POTG framework (under the investment sizing) is reported. Additionally, the total time a driver would take to drive to a charging station under the traditional setup and the runtime for the Hybrid heuristic are also reported.</p>
Dataset for paper "The Weights can be Harmful: Pareto Search versus Weighted Search in Multi-Objective Search-Based Software Engineering"
<p>This contains the dataset matched with the code on the GitHub page: https://github.com/ideas-labo/pareto-vs-weight-for-sbse.</p>
Dataset of the paper: "POLICIER: Pareto-OptimaL allocatIon of Continuous IntEgration Resources"
<p>This repository contains the dataset of the paper "POLICIER: Pareto-OptimaL allocatIon of Continuous IntEgration Resources"</p>
Pareto Curves for Compositionally Model Checking String Diagrams of MDPs: Supplemental Material
<p>Supplemental material for the paper: Pareto Curves for Compositionally Model Checking String Diagrams of MDPs</p> <p>Submitted to TACAS 2024</p>
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