Article
Computer Science, Artificial Intelligence
Qiuyue Liu, Juan Zou, Shengxiang Yang, Jinhua Zheng
Summary: This paper proposes a new decision variable classification method for multiobjective evolutionary algorithms by analyzing the monotonicities of objectives. Based on this method, a new directional crossover method is designed for generating promising solutions. The paper also introduces an interval mapping strategy for obtaining solutions with good diversity. Experimental results demonstrate that the proposed algorithm has high competitiveness in dealing with many-objective optimization problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
A. C. Ramesh, G. Srivatsun
Summary: Overlap community detection in complex networks, such as social, biological, economic, and other real-world networks, has become an important research area in the past two decades. The community detection problem can be modeled as a multiobjective optimization problem and has been successfully solved using Evolutionary Algorithms (EA). A clique-based representation scheme is suitable for representing overlapping communities, but there are issues with heavy overlap and increased representation length. This paper proposed a merged-maximal-clique based representation scheme which reduces chromosome length and improves the efficiency of the EA for overlapping community detection.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Guangyuan Liu, Yangyang Li, Licheng Jiao, Yanqiao Chen, Ronghua Shang
Summary: This study introduces a new approach using a multiobjective evolutionary algorithm assisted stacked autoencoder for PolSAR image classification, which can adaptively optimize parameters and hyperparameters to achieve competitive results.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Automation & Control Systems
Kai Zhang, Gary G. Yen, Zhenan He
Summary: In this article, a recursive evolutionary algorithm EvoKnee(R) is proposed to directly search for global knee solutions and multiple local knee solutions using the minimum Manhattan distance approach, instead of a large number of Pareto optimal solutions. Unlike traditional approaches, only nondominated solutions in rank one are preserved in each generation, reducing computational cost and allowing quick convergence to knee solutions.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Meriem Hemici, Djaafar Zouache
Summary: This paper proposes a new multi-objective evolutionary algorithm called MP-MOEA, which is based on multi-population, to solve the multi-objective constrained portfolio optimization problem in finance. By using a multi-population strategy and two types of archives, the algorithm improves solution quality, accelerates convergence, and demonstrates superior performance in experiments.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Tingyang Wei, Jinghui Zhong
Summary: Researchers have proposed a Generalized Resource Allocation (GRA) framework to dynamically allocate computational resources, enhancing the performance of multi-objective EMTO algorithms. By designing a normalized attainment function, multi-step nonlinear regression, and flexible adjustment of resource allocation intensity, the framework has shown success in various domains.
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
(2021)
Article
Computer Science, Information Systems
Yingjie Zou, Yuan Liu, Juan Zou, Shengxiang Yang, Jinhua Zheng
Summary: Sparse large scale multiobjective optimization problems (sparse LSMOPs) have a high degree of sparsity in the decision variables of their Pareto optimal solutions. Existing evolutionary algorithms for sparse LSMOPs fail to achieve sufficient sparsity due to inaccurate location of nonzero decision variables and lack of interaction between the locating process and optimizing process. To address this, a dynamic sparse grouping evolutionary algorithm (DSGEA) is proposed, which groups decision variables with comparable numbers of nonzero variables and applies improved evolutionary operators for optimization. DSGEA outperforms current EAs in experiments on real-world and benchmark problems, achieving sparser Pareto optimal solutions with precise locations of nonzero decision variables.
INFORMATION SCIENCES
(2023)
Article
Automation & Control Systems
Zhengping Liang, Tingting Luo, Kaifeng Hu, Xiaoliang Ma, Zexuan Zhu
Summary: This article introduces a new indicator-based many-objective evolutionary algorithm, MaOEA-IBP, with boundary protection to address the challenges faced by traditional multiobjective evolutionary algorithms when dealing with MaOPs. Experimental results demonstrate that MaOEA-IBP achieves competitive performance compared to other algorithms across various benchmark MaOPs.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Management
Salvatore Corrente, Salvatore Greco, Benedetto Matarazzo, Roman Slowinski
Summary: In this paper, we propose an interactive evolutionary multiobjective optimization (IEMO) approach guided by a preference elicitation procedure inspired by artificial intelligence and decision psychology. The approach utilizes decision rules to influence the optimization process and has been proven to converge to the most interesting part of the Pareto front.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2024)
Article
Geochemistry & Geophysics
Xiaohua Xu, Zhanghai Ju, Jia Luo
Summary: In this simulation study, operational GNSS satellites are used for global navigation satellite system reflectometry (GNSS-R) measurement. Different constellations of satellites are designed and optimized using multiobjective evolutionary algorithms. The optimal constellations show similar performance in terms of coverage and revisited coverage with specific inclinations and orbital altitudes.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Information Systems
Mario Garza-Fabre, Aaron L. Sanchez-Martinez, Edwin Aldana-Bobadilla, Ricardo Landa
Summary: Evolutionary multiobjective algorithms are popular for clustering problems due to their ability to optimize multiple criteria and their robustness to changes in data characteristics. This paper proposes a learning-based approach to decision making in clustering by building a model that can estimate solution quality and facilitate the selection of the best choice. Experimental results demonstrate the effectiveness of this approach compared to existing decision-making strategies.
Article
Computer Science, Artificial Intelligence
Yinan Guo, Mingyi Huang, Guoyu Chen, Dunwei Gong, Jing Liang, Zekuan Yu
Summary: The study focused on dynamic constrained multiobjective optimization problems and proposed a dynamic constrained multiobjective evolutionary algorithm based on decision variable classification. By rationally combining different categories of decision variables to accelerate population convergence, the experimental results showed its superiority over other algorithms.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Engineering, Marine
Hand Ouelmokhtar, Yahia Benmoussa, Djamel Benazzouz, Mohamed Abdessamed Ait-Chikh, Laurent Lemarchand
Summary: This study utilizes an USV equipped with an on-board LiDAR to address the monitoring mission problem and proposes an efficient solution to minimize energy consumption in a bi-objective coverage path planning problem.
Article
Automation & Control Systems
Qinqin Fan, Yilian Zhang, Ning Li
Summary: The paper introduces an automatic selection strategy of multiobjective evolutionary algorithms based on performance indicators (MOEAS-PI). This strategy can effectively improve the efficiency and robustness of solving multiobjective optimization problems.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Zeneng She, Wenjian Luo, Xin Lin, Yatong Chang, Yuhui Shi
Summary: This paper focuses on the study of multiparty multiobjective optimization problems (MPMOPs) and proposes a new algorithm OptMPNDS3 to solve these problems. Comparisons with other algorithms on a problem suite show that OptMPNDS3 performs strongly and similarly.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Engineering, Environmental
William J. Raseman, Balaji Rajagopalan, Joseph R. Kasprzyk, William Kleiber
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2020)
Editorial Material
Engineering, Civil
David E. Rosenberg, Yves Filion, Rebecca Teasley, Samuel Sandoval-Solis, Jory S. Hecht, Jakobus E. van Zyl, George F. McMahon, Jeffery S. Horsburgh, Joseph R. Kasprzyk, David G. Tarboton
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
(2020)
Article
Green & Sustainable Science & Technology
M. A. DeRousseau, J. H. Arehart, J. R. Kasprzyk, W. V. Srubar
JOURNAL OF CLEANER PRODUCTION
(2020)
Article
Engineering, Civil
Brendan Purcell, Zachary A. Barkjohn, Joseph R. Kasprzyk, Ashlynn S. Stillwell
Summary: This paper quantitatively assesses the impacts of reclaimed water consumption downstream, using scenario analysis and a two-sample t-test to evaluate streamflow alteration. The potential downstream effects are linked to stakeholders' performance metrics, with examples from Illinois and New Mexico showing varying impacts on transportation and endangered species. Understanding downstream impacts is crucial for the legal and policy contexts surrounding the sustainability of reclaimed water projects.
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
(2021)
Article
Environmental Studies
Jen Henderson, Lisa Dilling, Rebecca Morss, Olga Wilhelmi, Ursula Rick
Summary: This study highlights the importance of social learning in enhancing adaptive capacity to unforeseen consequences, such as promoting holistic river management, expanding relationship boundaries, and creating spaces for safer experimentation.
WEATHER CLIMATE AND SOCIETY
(2021)
Article
Materials Science, Multidisciplinary
M. A. DeRousseau, J. R. Kasprzyk, W. V. Srubar
Summary: This study introduces simulation-optimization as a new paradigm for designing concrete mixtures, considering performance characteristics and allowing designers to quantify and visualize tradeoffs between critical concrete performance metrics. The framework demonstrates that local conditions of case studies dictate the most important parameters for optimal concrete mixture designs when considering multiple factors.
FRONTIERS IN MATERIALS
(2021)
Editorial Material
Environmental Sciences
Lisa Dilling, Maria Carmen Lemos, Nuvodita Singh
GLOBAL ENVIRONMENTAL CHANGE-HUMAN AND POLICY DIMENSIONS
(2021)
Article
Engineering, Environmental
Rebecca Smith, Edith Zagona, Joseph Kasprzyk, Nathan Bonham, Elliot Alexander, Alan Butler, James Prairie, Carly Jerla
Summary: Deep uncertainty refers to planning contexts where likelihood of future conditions cannot be determined, conflicting objectives and unpredictable outcomes exist. Evidence of its relevance in the Colorado River Basin is seen in severe and unexpected drought, diverse stakeholders and viewpoints. Decision Making under Deep Uncertainty (DMDU) aims to address these challenges. Reclamation has explored DMDU since 2012, using adaptation, vulnerability and robustness concepts to design strategies for supply-demand imbalance. This article presents the basis for continued exploration of DMDU techniques and how ongoing studies can contribute to future planning efforts in the Colorado River Basin.
JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION
(2022)
Article
Engineering, Civil
Jacob Kravits, Joseph Kasprzyk, Kyri Baker, Konstantinos Andreadis
Summary: This study introduces a geospatial and machine learning model for classifying dam hazard potential, highlighting the significance of utilizing a multiobjective approach for tuning model parameters.
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
(2021)
Article
Computer Science, Interdisciplinary Applications
Nathan Bonham, Joseph Kasprzyk, Edith Zagona
Summary: This paper introduces a decision-support framework called post-MORDM, which addresses the challenges of generating a large number of policies and disagreements among decision-makers in Many Objective Robust Decision Making (MORDM). It uses the Self-Organizing Map (SOM) technology to cluster policies, discover salient characteristics, and assess cause-effect relationships, aiming to create a structured platform that encourages negotiation and compromise.
ENVIRONMENTAL MODELLING & SOFTWARE
(2022)
Article
Meteorology & Atmospheric Sciences
Parthkumar A. Modi, Eric E. Small, Joseph Kasprzyk, Ben Livneh
Summary: This study investigates the relationship between snow water equivalent (SWE) and streamflow volume (AMJJ-V) during drought in small headwater catchments and proposes an adaptive sampling approach that dynamically selects training years based on antecedent SWE conditions to improve the accuracy of historical drought year prediction.
JOURNAL OF HYDROMETEOROLOGY
(2022)
Editorial Material
Engineering, Civil
Joseph Kasprzyk, Margaret Garcia
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
(2023)
Article
Geosciences, Multidisciplinary
Aaron Heldmyer, Ben Livneh, James McCreight, Laura Read, Joseph Kasprzyk, Toby Minear
Summary: Accurate representation of channel properties is crucial for forecasting in hydrologic models. However, there is considerable uncertainty in the parameterization of channel geometry and hydraulic roughness in the NOAA National Water Model due to data scarcity. This study aims to improve channel representativeness by updating channel geometry and roughness parameters using a large, previously unpublished hydraulic geometry dataset.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2022)
Article
Green & Sustainable Science & Technology
Jacob Kravits, Joseph R. Kasprzyk, Kyri Baker, Ashlynn S. Stillwell
Summary: This paper presents a novel formulation of the optimal power flow problem, aiming to minimize cost, water withdrawal, and water consumption. The formulation is applied in a realistic case study using global mapping and ranking sensitivity analyses, providing insights into vulnerabilities and potential issues in power systems.
ENVIRONMENTAL RESEARCH: INFRASTRUCTURE AND SUSTAINABILITY
(2022)
Article
Engineering, Environmental
William J. Raseman, Joseph R. Kasprzyk, R. Scott Summers, Amanda K. Hohner, Fernando L. Rosario-Ortiz
ENVIRONMENTAL SCIENCE-WATER RESEARCH & TECHNOLOGY
(2020)
Article
Computer Science, Interdisciplinary Applications
Jeffrey Wade, Christa Kelleher, Barret L. Kurylyk
Summary: This study developed a physically-based water temperature model coupled with the National Water Model (NWM) to assess the potential for water temperature prediction to be incorporated into the NWM at the continental scale. By evaluating different model configurations of increasing complexity, the study successfully simulated hourly water temperatures in the forested headwaters of H.J. Andrews Experimental Forest in Oregon, USA, providing a basis for integrating water temperature simulation with predictions from the NWM.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Shaun SH. Kim, Lucy A. Marshall, Justin D. Hughes, Lynn Seo, Julien Lerat, Ashish Sharma, Jai Vaze
Summary: A major challenge in hydrologic modelling is producing reliable uncertainty estimates outside of calibration periods. This research addresses the challenge by improving model structures and error models to more reliably estimate uncertainty. The combination of the RBS model and SPUE produces statistically reliable predictions and shows better matching performance in tests.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Juan Pedro Carbonell-Rivera, Javier Estornell, Luis Angel Ruiz, Pablo Crespo-Peremarch, Jaime Almonacid-Caballer
Summary: This study presents Class3Dp, a software for classifying vegetation species in colored point clouds. The software utilizes geometric, spectral, and neighborhood features along with machine learning methods to classify the point cloud, allowing for the recognition of species composition in an ecosystem.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Zhi Li, Daniel Caviedes-Voullieme, Ilhan Oezgen-Xian, Simin Jiang, Na Zheng
Summary: The optimal strategy for solving the Richards equation numerically depends on the specific problem, particularly when using GPUs. This study investigates the parallel performance of four numerical schemes on both CPUs and GPUs. The results show that the scaling of Richards solvers on GPUs is influenced by various factors. Compared to CPUs, parallel simulations on GPUs exhibit significant variation in scaling across different code sections, with poorly-scaled components potentially impacting overall performance. Nonetheless, using GPUs can greatly enhance computational speed, especially for large-scale problems.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ludovic Cassan, Leo Pujol, Paul Lonca, Romain Guibert, Helene Roux, Olivier Mercier, Dominique Courret, Sylvain Richard, Pierre Horgue
Summary: Methods and algorithms for measuring stream surface velocities have been continuously developed over the past five years to adapt to specific flow typologies. The free software ANDROMEDE allows easy use and comparison of these methods with image processing capabilities designed for measurements in natural environments and with unmanned aerial vehicles. The validation of the integrated algorithms is presented on three case studies that represent the targeted applications: the study of currents for eco-hydraulics, the measurement of low water flows and the diagnosis of hydraulic structures. The field measurements are in very good agreement with the optical measurements and demonstrate the usefulness of the tool for rapid flow diagnosis for all the intended applications.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Mariia Kozlova, Robert J. Moss, Julian Scott Yeomans, Jef Caers
Summary: This paper introduces a framework for quantitative sensitivity analysis using the SimDec visualization method, and tests its effectiveness on decision-making problems. The framework captures critical information in the presence of heterogeneous effects, and enhances its practicality by introducing a formal definition and classification of heterogeneous effects.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Chad R. Palmer, Denis Valle, Edward V. Camp, Wendy-Lin Bartels, Martha C. Monroe
Summary: Simulation games have been used in natural resource management for education and communication purposes, but not for data collection. This research introduces a new design process which involves stakeholders and emphasizes usability, relevance, and credibility testing criteria. The result is a finalized simulation game for future research.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Tao Wang, Chenming Zhang, Ye Ma, Harald Hofmann, Congrui Li, Zicheng Zhao
Summary: This study used numerical modeling to investigate the formation process of iron curtains under different freshwater and seawater conditions. It was found that Fe(OH)3 accumulates on the freshwater side, while the precipitation is inhibited on the seaward side due to high H+ concentrations. These findings enhance our understanding of iron transformation and distribution in subterranean estuaries.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Grant Hutchings, James Gattiker, Braden Scherting, Rodman R. Linn
Summary: Computational models for understanding and predicting fire in wildland and managed lands are becoming increasingly impactful. This paper addresses the characterization and population of mid-story fuels, which are not easily observable through traditional survey or remote sensing. The authors present a methodology to populate the mid-story using a generative model for fuel placement, which can be calibrated based on limited observation datasets or expert guidance. The connection of terrestrial LiDAR as the observations used to calibrate the generative model is emphasized. Code for the methods in this paper is provided.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Saswata Nandi, Pratiman Patel, Sabyasachi Swain
Summary: IMDLIB is an open-source Python library that simplifies the retrieval and processing of gridded meteorological data from IMD, enhancing data accessibility and facilitating hydro-climatic research and analysis.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Pengfei Wu, Jintao Liu, Meiyan Feng, Hu Liu
Summary: In this paper, a new flow distance algorithm called D infinity-TLI is proposed, which accurately estimates flow distance and width function using a two-segment-distance strategy and triangulation with linear interpolation method. The evaluation results show that D infinity-TLI outperforms existing algorithms and has a low mean absolute relative error.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)