Applied Soft Computing
ISSN: 1568-4946
Datasets associated with articles published in Applied Soft Computing
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- Data for: A multi-attribute group decision making method considering both the correlation coefficient and hesitancy degrees under interval-valued intuitionistic fuzzy Environmentthe calculate process of my application example
- Dataset
- Data for: Nonlinear based Chaotic Harris Hawks Optimizer: Algorithm and Internet of Vehicles ApplicationTo alleviate the drawbacks of this algorithm, a modified version called Nonlinear based Chaotic Harris Hawks Optimization (NCHHO) is proposed in this paper. NCHHO uses chaotic and nonlinear control parameters to improve HHO’s optimization performance. The main goal of using the chaotic maps in the proposed method is to improve the exploratory behavior of HHO. In addition, this paper introduces a nonlinear control parameter to adjust HHO’s exploratory and exploitative behaviours. The performance of NCHHO’s is developed using a variety of chaotic maps to identify the most effective one and tested on several well-known benchmark functions. The results demonstrate the NCHHO algorithm provides very competitive, often superior, results compared to the other algorithms. The paper also considers solving an Internet of Vehicles optimization problem that showcases the applicability of NCHHO.
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- Data for: A Topology-based Single-Pool Decomposition Framework for Large-Scale Global OptimizationSPDF-based methods.rar contains the MATLAB implementation of the SPDG and the TSPDG methods.
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- Data for: Recommending Bug Report Assignment using the Learning-to-Rank OptimizationDataset of Bug Reports and their corresponding fixes (commit code changes)
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- Data for: Nonlinear systems modelling based on self-organizing fuzzy neural network with hierarchical pruning schemeWe provided research data and and MATLAB source codes about SOFNN-HPS algorithm, and also provided some comparison algorithm source coded. Researchers can use these data and codes for scientific research and experimental comparison. These data and codes cannot be used for commercial purposes.
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- Data for: Parameter optimization for the nonlinear grey Bernoulli model and its application in predicting biomass energy consumption143 yearly time-series data under different scenarios, sizes, and backgrounds, are used to validate the effectiveness of prediction models. All of them are from the M4 Forecasting Competition dataset.
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- Data for: Ensemble Probabilistic Prediction Approach for Uncertainty Modeling of Crude Oil PriceThe daily and weekly Europe Brent (BRE) spot price data (Dollars per Barrel) without missing sample points, collected from U.S. Energy Information Administration, were considered as studied data in this paper.
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- Data for: Accelerating Gaussian Process Surrogate Modeling using Compositional Kernel Learning and Multi-stage Sampling frameworkThe source codes are uploaded for the replication of results in the following manuscript: Accelerating Gaussian Process Surrogate Modeling using Compositional Kernel Learning and Multi-stage Sampling framework Once the zip file is unzipped, please read "readme.pdf" to see the implementation and results.
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- Sequences of Kinematic features Extracted from Repetitive Unilateral Upper-limb MovementsThe data include 13 kinematic features extracted during three repetitive movements performed by 12 healthy subjects and 14 stroke survivors. This data collection has been approved by Nanyang Technological University Institutional Review Board (IRB-2018-03-036). See readme.pdf for more details.
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- Data for: Evolutionary Learning Based Simulation Optimization for Stochastic Job Shop Scheduling ProblemsThe Stochastic Job shop Scheduling Problem results Dateset proposed by Ghasemi et.al.
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