Bibliografías temáticas / Multiobjective sparse optimization

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Literatura académica sobre el tema "Multiobjective sparse optimization"

Autor: Grafiati
Publicado: 10 de marzo de 2023
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Multiobjective sparse optimization"

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Huang, Junhao, Weize Sun, and Lei Huang. "Joint Structure and Parameter Optimization of Multiobjective Sparse Neural Network." Neural Computation 33, no. 4 (2021): 1113–43. http://dx.doi.org/10.1162/neco_a_01368.

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This work addresses the problem of network pruning and proposes a novel joint training method based on a multiobjective optimization model. Most of the state-of-the-art pruning methods rely on user experience for selecting the sparsity ratio of the weight matrices or tensors, and thus suffer from severe performance reduction with inappropriate user-defined parameters. Moreover, networks might be inferior due to the inefficient connecting architecture search, especially when it is highly sparse. It is revealed in this work that the network model might maintain sparse characteristic in the early
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Chen, Zhi-Kun, Feng-Gang Yan, Xiao-Lin Qiao, and Yi-Nan Zhao. "Sparse Antenna Array Design for MIMO Radar Using Multiobjective Differential Evolution." International Journal of Antennas and Propagation 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/1747843.

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A two-stage design approach is proposed to address the sparse antenna array design for multiple-input multiple-output radar. In the first stage, the cyclic algorithm (CA) is used to establish a covariance matrix that satisfies the beam pattern approximation for a full array. In the second stage, a sparse antenna array with a beam pattern is designed to approximate the desired beam pattern. This paper focuses on the second stage. The optimization problem for the sparse antenna array design aimed at beam pattern synthesis is formulated, where the peak side lobe (PSL) is weakly constrained by the
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Cocchi, Guido, Tommaso Levato, Giampaolo Liuzzi, and Marco Sciandrone. "A concave optimization-based approach for sparse multiobjective programming." Optimization Letters 14, no. 3 (2019): 535–56. http://dx.doi.org/10.1007/s11590-019-01506-w.

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Yue, Caitong, Jing Liang, Boyang Qu, Yuhong Han, Yongsheng Zhu, and Oscar D. Crisalle. "A novel multiobjective optimization algorithm for sparse signal reconstruction." Signal Processing 167 (February 2020): 107292. http://dx.doi.org/10.1016/j.sigpro.2019.107292.

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Wu, Yu, Yongshan Zhang, Xiaobo Liu, Zhihua Cai, and Yaoming Cai. "A multiobjective optimization-based sparse extreme learning machine algorithm." Neurocomputing 317 (November 2018): 88–100. http://dx.doi.org/10.1016/j.neucom.2018.07.060.

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Li, Hui, Qingfu Zhang, Jingda Deng, and Zong-Ben Xu. "A Preference-Based Multiobjective Evolutionary Approach for Sparse Optimization." IEEE Transactions on Neural Networks and Learning Systems 29, no. 5 (2018): 1716–31. http://dx.doi.org/10.1109/tnnls.2017.2677973.

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Gebken, Bennet, and Sebastian Peitz. "An Efficient Descent Method for Locally Lipschitz Multiobjective Optimization Problems." Journal of Optimization Theory and Applications 188, no. 3 (2021): 696–723. http://dx.doi.org/10.1007/s10957-020-01803-w.

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AbstractWe present an efficient descent method for unconstrained, locally Lipschitz multiobjective optimization problems. The method is realized by combining a theoretical result regarding the computation of descent directions for nonsmooth multiobjective optimization problems with a practical method to approximate the subdifferentials of the objective functions. We show convergence to points which satisfy a necessary condition for Pareto optimality. Using a set of test problems, we compare our method with the multiobjective proximal bundle method by Mäkelä. The results indicate that our metho
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Fang, Xiaoping, Yaoming Cai, Zhihua Cai, Xinwei Jiang, and Zhikun Chen. "Sparse Feature Learning of Hyperspectral Imagery via Multiobjective-Based Extreme Learning Machine." Sensors 20, no. 5 (2020): 1262. http://dx.doi.org/10.3390/s20051262.

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Hyperspectral image (HSI) consists of hundreds of narrow spectral band components with rich spectral and spatial information. Extreme Learning Machine (ELM) has been widely used for HSI analysis. However, the classical ELM is difficult to use for sparse feature leaning due to its randomly generated hidden layer. In this paper, we propose a novel unsupervised sparse feature learning approach, called Evolutionary Multiobjective-based ELM (EMO-ELM), and apply it to HSI feature extraction. Specifically, we represent the task of constructing the ELM Autoencoder (ELM-AE) as a multiobjective optimiza
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Wang, Zhao, Jinxin Wei, Jianzhao Li, Peng Li, and Fei Xie. "Evolutionary Multiobjective Optimization with Endmember Priori Strategy for Large-Scale Hyperspectral Sparse Unmixing." Electronics 10, no. 17 (2021): 2079. http://dx.doi.org/10.3390/electronics10172079.

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Mixed pixels inevitably appear in the hyperspectral image due to the low resolution of the sensor and the mixing of ground objects. Sparse unmixing, as an emerging method to solve the problem of mixed pixels, has received extensive attention in recent years due to its robustness and high efficiency. In theory, sparse unmixing is essentially a multiobjective optimization problem. The sparse endmember term and the reconstruction error term can be regarded as two objectives to optimize simultaneously, and a series of nondominated solutions can be obtained as the final solution. However, the large
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Tian, Ye, Xingyi Zhang, Chao Wang, and Yaochu Jin. "An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems." IEEE Transactions on Evolutionary Computation 24, no. 2 (2020): 380–93. http://dx.doi.org/10.1109/tevc.2019.2918140.

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Tesis sobre el tema "Multiobjective sparse optimization"

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Levato, Tommaso. "Algorithms for ell_0-norm Optimization Problems." Doctoral thesis, 2020. http://hdl.handle.net/2158/1188438.

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Spalke, Tobias [Verfasser]. "Application of multiobjective optimization concepts in inverse radiotherapy planning / put forward by Tobias Spalke." 2009. http://d-nb.info/995810087/34.

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Libros sobre el tema "Multiobjective sparse optimization"

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United States. National Aeronautics and Space Administration., ed. Multiobjective optimization of hybrid regenerative life support technologies, (topic D, technology assessment): NASA interim progress report. National Aeronautics and Space Administration, 1995.

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Multiobjective optimization of hybrid regenerative life support technologies, (topic D, technology assessment): NASA interim progress report. National Aeronautics and Space Administration, 1995.

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Capítulos de libros sobre el tema "Multiobjective sparse optimization"

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Li, Yangyang, Xiaoyu Bai, Xiaoxu Liang, and Licheng Jiao. "Sparse Restricted Boltzmann Machine Based on Multiobjective Optimization." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68759-9_73.

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Huang, Minchao, Jianjun Wu, Jian Li, and Yuqiang Cheng. "Integrated Design of Solar Thermal Propulsion and Task Optimization." In Solar Thermal Thruster. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-7490-6_8.

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AbstractIn previous chapters, the secondary concentrator, absorption cavity, heat exchanger core, and nozzle of the solar thermal propulsion (STP) system are integratedly designed, and regenerative cooling and laminated heat exchange technologies are used to effectively combine these components. This chapter analyzes the overall efficiency of the STP system and uses quantum genetic algorithms to perform a multiobjective optimization for space missions.
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Witt, Nicolas, Mark Deutel, Jakob Schubert, Christopher Sobel, and Philipp Woller. "Energy-Efficient AI on the Edge." In Unlocking Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64832-8_19.

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AbstractThis chapter shows methods for the resource-optimized design of AI functionality for edge devices powered by microprocessors or microcontrollers. The goal is to identify Pareto-optimal solutions that satisfy both resource restrictions (energy and memory) and AI performance. To accelerate the design of energyefficient classical machine learning pipelines, an AutoML tool based on evolutionary algorithms is presented, which uses an energy prediction model from assembly instructions (prediction accuracy 3.1%) to integrate the energy demand into a multiobjective optimization approach. For t
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Tenente, Marcos, Carla Henriques, Álvaro Gomes, Patrícia Pereira da Silva, and António Trigo. "Multiple Impacts of Energy Efficiency Technologies in Portugal." In Springer Proceedings in Political Science and International Relations. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18161-0_9.

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AbstractPortuguese programs aimed at fostering Energy Efficiency (EE) measures often rely on cost–benefit approaches only considering the use phase and neglecting other potential impacts generated. Therefore, this work suggests a novel methodological framework by combining Hybrid Input–Output Lifecycle Analysis (HIO-LCA) with the Portuguese seasonal method for computing the households’ energy needs. A holistic assessment of the energy, economic, environmental, and social impacts connected with the adoption of EE solutions is conducted aimed at supporting decision-makers (DMs) in the design of
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Ganesan, T., I. Elamvazuthi, and P. Vasant. "Swarm Intelligence for Multiobjective Optimization of Extraction Process." In Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9644-0.ch020.

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Multi objective (MO) optimization is an emerging field which is increasingly being implemented in many industries globally. In this work, the MO optimization of the extraction process of bioactive compounds from the Gardenia Jasminoides Ellis fruit was solved. Three swarm-based algorithms have been applied in conjunction with normal-boundary intersection (NBI) method to solve this MO problem. The gravitational search algorithm (GSA) and the particle swarm optimization (PSO) technique were implemented in this work. In addition, a novel Hopfield-enhanced particle swarm optimization was developed
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Ohsaki, M., T. Ogawa, and R. Tateishi. "Multiobjective Shape Optimization of Shells Considering Roundness and Elastic Stiffness." In Space Structures 5. Thomas Telford Publishing, 2002. http://dx.doi.org/10.1680/ss5v1.31739.0041.

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Manne, Janga Reddy. "Multiobjective Optimization in Water and Environmental Systems Management- MODE Approach." In Advances in Computer and Electrical Engineering. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9479-8.ch004.

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Many real world problems are characterized by multiple goals, often conflicting in nature and compete with one another. Multi-objective optimization problems (MOOPs) require the simultaneous optimization of several non-commensurable and conflicting objectives. In the past, several studies have used conventional approaches to solve the MOOPs by adopting weighted approach or constrained approach, which may face difficulties while generating Pareto optimal solutions, if optimal solution lies on non-convex or disconnected regions of the objective function space. An effective algorithm should have
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Benini Ernesto, Venturelli Giovanni, and Łaniewski-Wołłk Łukasz. "Comparison between Pure and Surrogate-assisted Evolutionary Algorithms for Multiobjective Optimization." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2016. https://doi.org/10.3233/978-1-61499-619-4-229.

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In this paper, a comparison between a “pure” genetic algorithm (GeDEA-II) and a surrogate-assisted algorithm (ASEMOO) is carried out using up-to-date multiobjective and multidimensional test functions. The experimental results show that the use of surrogates greatly improves convergence when both two- and three-objective test cases are dealt with. However, its convergence capabilities depend on how the surrogate can have an accurate picture of the fitness function landscape and seem to decrease as the number of the objective increases from two to three. On the other hand, a
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Kuyucu, Tüze, Ivan Tanev, and Katsunori Shimohara. "Efficient Evolution of Modular Robot Control via Genetic Programming." In Engineering Creative Design in Robotics and Mechatronics. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-4225-6.ch005.

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In Genetic Programming (GP), most often the search space grows in a greater than linear fashion as the number of tasks required to be accomplished increases. This is a cause for one of the greatest problems in Evolutionary Computation (EC): scalability. The aim of the work presented here is to facilitate the evolution of control systems for complex robotic systems. The authors use a combination of mechanisms specifically designed to facilitate the fast evolution of systems with multiple objectives. These mechanisms are: a genetic transposition inspired seeding, a strongly-typed crossover, and
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Actas de conferencias sobre el tema "Multiobjective sparse optimization"

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Wang, Huoyuan, and Jing Jiang. "A Two-Stage Optimization Framework for Sparse Large-Scale Multiobjective Optimization." In 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2024. http://dx.doi.org/10.1109/cec60901.2024.10611882.

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Udomvorakulchai, Veerawat, Miguel Pineda, and Eric S. Fraga. "Introducing Competition in a Multi-Agent System for Hybrid Optimization." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.132182.

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Process systems engineering optimization problems may be challenging. These problems often exhibit nonlinearity, non-convexity, discontinuity, and uncertainty, and often only the values of objective and constraint functions are accessible. Additionally, some problems may be computationally expensive. In such scenarios, black-box optimization methods may be appropriate to tackle such problems. A general-purpose multi-agent framework for optimization has been developed to automate the configuration and use of hybrid optimization, allowing for multiple optimization solvers, including different in
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3

Lee, Yong Hoon, R. E. Corman, Randy H. Ewoldt, and James T. Allison. "A Multiobjective Adaptive Surrogate Modeling-Based Optimization (MO-ASMO) Framework Using Efficient Sampling Strategies." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67541.

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A novel multiobjective adaptive surrogate modeling-based optimization (MO-ASMO) framework is proposed to utilize a minimal number of training samples efficiently for sequential model updates. All the sample points are enforced to be feasible, and to provide coverage of sparsely explored sparse design regions using a new optimization subproblem. The MO-ASMO method only evaluates high-fidelity functions at feasible sample points. During an exploitation sample phase, samples are selected to enhance solution accuracy rather than the global exploration. Sampling tasks are especially challenging for
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Rangavajhala, Sirisha, Anoop A. Mullur, and Achille Messac. "Equality Constraints in Multiobjective Robust Design Optimization: Implications and Tradeoffs." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-79961.

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In the present paper, we explore issues in handling equality constraints in multiobjective robust design optimization (RDO) problems. Satisfying an equality constraint exactly under uncertainty can be a challenging task. The challenge of handling equality constraints is compounded in multiobjective RDO problems. Modeling the tradeoffs between the mean of the performance and the variation of the performance for each design objective in a multiobjective RDO problem is a complex task by itself. Equality constraints add to this complexity because of the additional tradeoffs that are introduced bet
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Mattson, Christopher A., Vicky Lofthouse, and Tracy Bhamra. "Exploring Decision Tradeoffs in Sustainable Design." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47295.

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Sustainable design involves three essential areas: economic sustainability, environmental sustainability, and social sustainability. For even the simplest of products, the complexities of these three areas and their tradeoffs cause decision-making transparency to be lost in most practical situations. The existing field of multiobjective optimization offers a natural framework to explore the tradeoffs in the sustainability space (defined by economic, environmental, and social sustainability issues), thus offering both the designer and the decision makers a means of understanding the sustainabil
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Simov, Peter, and Scott Ferguson. "Investigating the Significance of “One-to-Many” Mappings in Multiobjective Optimization." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28689.

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Significant research has focused on multiobjective design optimization and negotiating trade-offs between conflicting objectives. Many times, this research has referred to the possibility of attaining similar performance from multiple, unique design combinations. While such occurrences may allow for greater design freedom, their significance has yet to be quantified for trade-off decisions made in the design space (DS). In this paper, we computationally explore which regions of the performance space (PS) exhibit “one-to-many” mappings back to the DS, and examine the behavior and validity of th
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Curtis, Shane K., Braden J. Hancock, and Christopher A. Mattson. "Use Scenarios for Design Space Exploration With a Dynamic Multiobjective Optimization Formulation." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71039.

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In a recent publication, we presented a new strategy for engineering design and optimization, which we termed formulation space exploration. The formulation space for an optimization problem is the union of all variable and design objective spaces identified by the designer as being valid and pragmatic problem formulations. By extending a computational search into this new space, the solution to any optimization problem is no longer predefined by the optimization problem formulation. This method allows a designer to both diverge the design space during conceptual design and converge onto a sol
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Rangavajhala, Sirisha, and Achille Messac. "Decision Making and Constraint Tradeoff Visualization for Design Under Uncertainty." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35385.

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In design optimization problems under uncertainty, two conflicting issues are generally of interest to the designer: feasibility and optimality. In this research, we adopt the philosophy that design, especially under uncertainty, is a decision making process, where the associated tradeoffs can be conveniently understood using multiobjective optimization. The importance of constraint feasibility and the associated tradeoffs, especially in the presence of equality constraints, is examined in this paper. We propose a three-step decision making framework that facilitates effective decision making
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Lautenschlager, Uwe, Hans A. Eschenauer, and Farrokh Mistree. "Multiobjective Flywheel Design: A DOE-Based Concept Exploration Task." In ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/dac-3961.

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Abstract In structural design, expensive function evaluations can be replaced by accurate function approximations to facilitate the effective solution of multiobjective problems. In this paper we address the question: How can we solve multiobjective shape optimization problems effectively using a Design-of-Experiments (DOE) -based approach? To answer this question we address issues of creating non-orthogonal experimental designs, when dependencies among the parameters that represent shape functions are present. A screening strategy is used to gain knowledge about the structural behavior within
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Ferguson, Scott, Ashwin Gurnani, Joseph Donndelinger, and Kemper Lewis. "A Study of Convergence and Mapping in Multiobjective Optimization Problems." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84852.

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In this paper, we investigate the issue of convergence in multiobjective optimization problems when using a Multi-Objective Genetic Algorithm (MOGA) to determine the set of Pareto optimal solutions. Additionally, given a Pareto set for a multi-objective problem, the mapping between the performance and design space is studied to determine design variable configurations for a given set of performance specifications. The advantage of this study is that the design variable information is obtained without having to repeat system analyses. The tools developed in this paper have been applied to devel
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Informes sobre el tema "Multiobjective sparse optimization"

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Allen, J. C., and D. Acero. Multiobjective Optimization on Function Spaces: A Kolmogorov Approach. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada439625.

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