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Artículos de revistas 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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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Liang, Lei, Yachao Jiang, Jialing Liu, Hailin Li, and Jianjiang Zhou. "Pattern Synthesis of Time-Modulated Sparse Array by an OPM-CVX Algorithm." Mathematical Problems in Engineering 2020 (April 14, 2020): 1–15. http://dx.doi.org/10.1155/2020/5491921.

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This paper addresses the constrained multiobjective optimization problem of time-modulated sparse arrays. The synthesis objective is to find an optimal element arrangement and associated excitation strategy of sparse arrays, which realize the balance of radiation power and sideband suppression performance with minimum number of elements, and suppress side lobe level simultaneously. A novel hybrid algorithm based on orthogonal perturbation method and convex optimization (OPM-CVX) for the synthesis of time-modulated sparse antenna array is presented in this paper. In order to satisfy the main lo

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12

Xu, Xinlin, Zhongbo Hu, Qinghua Su, and Zenggang Xiong. "Multiobjective Collective Decision Optimization Algorithm for Economic Emission Dispatch Problem." Complexity 2018 (November 13, 2018): 1–20. http://dx.doi.org/10.1155/2018/1027193.

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The collective decision optimization algorithm (CDOA) is a new stochastic population-based evolutionary algorithm which simulates the decision behavior of human. In this paper, a multiobjective collective decision optimization algorithm (MOCDOA) is first proposed to solve the environmental/economic dispatch (EED) problem. MOCDOA uses three novel learning strategies, that is, a leader-updating strategy based on the maximum distance of each solution in an external archive, a wise random perturbation strategy based on the sparse mark around a leader, and a geometric center-updating strategy based

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13

Feng, Dan, Mingyang Zhang, and Shanfeng Wang. "Multipopulation Particle Swarm Optimization for Evolutionary Multitasking Sparse Unmixing." Electronics 10, no. 23 (2021): 3034. http://dx.doi.org/10.3390/electronics10233034.

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Recently, the multiobjective evolutionary algorithms (MOEAs) have been designed to cope with the sparse unmixing problem. Due to the excellent performance of MOEAs in solving the NP hard optimization problems, they have also achieved good results for the sparse unmixing problems. However, most of these MOEA-based methods only deal with a single pixel for unmixing and are subjected to low efficiency and are time-consuming. In fact, sparse unmixing can naturally be seen as a multitasking problem when the hyperspectral imagery is clustered into several homogeneous regions, so that evolutionary mu

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14

Hu, Peng, Xiaobo Liu, Yaoming Cai, and Zhihua Cai. "Band Selection of Hyperspectral Images Using Multiobjective Optimization-Based Sparse Self-Representation." IEEE Geoscience and Remote Sensing Letters 16, no. 3 (2019): 452–56. http://dx.doi.org/10.1109/lgrs.2018.2872540.

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15

Haan, Sebastian, Fabio Ramos, and R. Dietmar Müller. "Multiobjective Bayesian optimization and joint inversion for active sensor fusion." GEOPHYSICS 86, no. 1 (2021): ID1—ID17. http://dx.doi.org/10.1190/geo2019-0460.1.

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A critical decision process in data acquisition for mineral and energy resource exploration is how to efficiently combine a variety of sensor types and how to minimize the total cost. We have developed a probabilistic framework for multiobjective optimization and inverse problems given an expensive cost function for allocating new measurements. This new method is devised to jointly solve multilinear forward models of 2D sensor data and 3D geophysical properties using sparse Gaussian process kernels while taking into account the cross-variances of different parameters. Multiple optimization str

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16

Zhang, Panpan, Ru Zhang, Ye Tian, Kay Chen Tan, and Xingyi Zhang. "A dual model-based evolutionary framework for dynamic large-scale sparse multiobjective optimization." Swarm and Evolutionary Computation 97 (August 2025): 102011. https://doi.org/10.1016/j.swevo.2025.102011.

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17

Jiang, Jing, Huoyuan Wang, Pingping Tong, et al. "A heterogeneous sparsity knowledge guided evolutionary algorithm for sparse large-scale multiobjective optimization." Swarm and Evolutionary Computation 96 (July 2025): 102000. https://doi.org/10.1016/j.swevo.2025.102000.

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18

Jiao, Shengxi, Lu Wen, and Haitao Guo. "Incomplete angle reconstruction algorithm with the sparse optimization and the image optimal criterions." International Journal of Advanced Robotic Systems 17, no. 3 (2020): 172988142091697. http://dx.doi.org/10.1177/1729881420916974.

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To solve the problem of artifact and image degradation caused by incomplete angle projection, this article presents an incomplete angle reconstruction algorithm based on sparse optimization and image optimization criterion (SO-IOC). Firstly, the joint objective function model is established based on the projection sparsity and the natural features of images. Secondly, by means of the idea of alternating direction method of multipliers, the augmented Lagrange method is used to decompose the reconstruction model into simple subproblems and the modified genetic algorithm is used for solving those

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19

WEI, JINGXUAN, and YUPING WANG. "AN INFEASIBLE ELITIST BASED PARTICLE SWARM OPTIMIZATION FOR CONSTRAINED MULTIOBJECTIVE OPTIMIZATION AND ITS CONVERGENCE." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 03 (2010): 381–400. http://dx.doi.org/10.1142/s021800141000797x.

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In this paper, an infeasible elitist based particle swarm optimization is proposed for solving constrained optimization problems. Firstly, an infeasible elitist preservation strategy is proposed, which keeps some infeasible solutions with smaller rank values at the early stage of evolution regardless of how large the constraint violations are, and keep some infeasible solutions with smaller constraint violations and rank values at the later stage of evolution. In this manner, the true Pareto front will be found easier. Secondly, in order to find a set of diversity and uniformly distributed Par

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20

Chen, Yu, Dong Chen, and Xiufen Zou. "Inference of Biochemical S-Systems via Mixed-Variable Multiobjective Evolutionary Optimization." Computational and Mathematical Methods in Medicine 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/3020326.

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Inference of the biochemical systems (BSs) via experimental data is important for understanding how biochemical components in vivo interact with each other. However, it is not a trivial task because BSs usually function with complex and nonlinear dynamics. As a popular ordinary equation (ODE) model, the S-System describes the dynamical properties of BSs by incorporating the power rule of biochemical reactions but behaves as a challenge because it has a lot of parameters to be confirmed. This work is dedicated to proposing a general method for inference of S-Systems by experimental data, using

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21

Kabganian, Masoud, Seyed M. Hashemi, and Jafar Roshanian. "Multidisciplinary Design Optimization of a Re-Entry Spacecraft via Radau Pseudospectral Method." Applied Mechanics 3, no. 4 (2022): 1176–89. http://dx.doi.org/10.3390/applmech3040067.

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The design and optimization of re-entry spacecraft or its subsystems is a multidisciplinary or multiobjective optimization problem by nature. Multidisciplinary design optimization (MDO) focuses on using numerical optimization in designing systems with several subsystems or disciplines that have interactions and independent actions. In the present paper, the system-level optimizer, trajectory, geometry and shape, aerodynamics, and aerothermodynamics differential equations, are converted to algebraic equations using the Radau pseudospectral method (RPM) since a spacecraft is a nonlinear, extensi

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22

Wang, Li, and Wei Wang. "Hyperspectral Image Reconstruction Based on Reference Point Nondominated Sorting Genetic Algorithm." Mobile Information Systems 2022 (April 5, 2022): 1–24. http://dx.doi.org/10.1155/2022/8455150.

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Spatial and spectral features of hyperspectral imagery reconstruction have gained increasing attention in the latest years. Based on the study of orthogonal matching pursuit (OMP) idea, a hyperspectral image reconstruction algorithm based on reference point nondominated sorting genetic algorithm (NSGA) is proposed. Instead of directly reconstructing the entire hyperspectral data as a traditional OMP reconstruction algorithm, the proposed algorithm explores the idea of the evolution process in the reconstruction. The Gabor redundancy dictionary is established as the sparse basis of hyperspectra

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23

Zhu, Jianjian, and Yanlong Xue. "Construction of a Mental Health Education Model for College Students Based on Fine-Grained Parallel Computing Programming." Mathematical Problems in Engineering 2022 (April 22, 2022): 1–13. http://dx.doi.org/10.1155/2022/4206714.

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Mental health and mental health problems of college students are becoming more and more obvious, and there is more and more negative news caused by psychological problems, and society from all walks of life has given high attention to this problem. Given the new situations and new problems, how to keep up with the times and reform and innovate in the content, method, and path of psychological education in colleges and universities is an important work of ideological and political education in colleges and universities. Because fine-grained category information can provide rich semantic clues,

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24

Xu, Meiling. "The Combination of Internet of Things Technology Based on Probability Model Network and Mass Education." Wireless Communications and Mobile Computing 2022 (May 12, 2022): 1–8. http://dx.doi.org/10.1155/2022/2782473.

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With the continuous development of the Internet of things, educational informatization has become a hot spot in the application of education. Internet of things technology, combined with various subject fields of education, can better achieve subject teaching objectives and teaching assistance. In the multimode teaching optimization model, weight distribution is a complex multiobjective decision-making problem. In this paper, a Bayesian network based on probability model is proposed, which is combined with the large entropy criterion to determine the comprehensive weight. The combination of Ba

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25

Chen, Shuo, Peng Cui, and Hongyuan Mei. "A Sustainable Design Strategy Based on Building Morphology to Improve the Microclimate of University Campuses in Cold Regions of China Using an Optimization Algorithm." Mathematical Problems in Engineering 2021 (June 8, 2021): 1–16. http://dx.doi.org/10.1155/2021/2304796.

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The microclimate affects the quality and efficiency of outdoor spaces of campuses, especially in the cold regions of China. In this paper, we propose a multiobjective optimization method to improve the thermal comfort of the outdoor environment of university campuses in severe cold regions. We used morphology data from 41 universities in the cold region of China to create a layout prototype of a campus cluster. Multiobjective optimization was used, and the effects of sunlight, solar radiation, and wind on the outdoor thermal comfort in winter were considered. A parameterized platform was estab

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26

Pleumpirom, Yuttapong, and Sataporn Amornsawadwatana. "Multiobjective Optimization of Aircraft Maintenance in Thailand Using Goal Programming: A Decision-Support Model." Advances in Decision Sciences 2012 (August 30, 2012): 1–17. http://dx.doi.org/10.1155/2012/128346.

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The purpose of this paper is to develop the multiobjective optimization model in order to evaluate suppliers for aircraft maintenance tasks, using goal programming. The authors have developed a two-step process. The model will firstly be used as a decision-support tool for managing demand, by using aircraft and flight schedules to evaluate and generate aircraft-maintenance requirements, including spare-part lists. Secondly, they develop a multiobjective optimization model by minimizing cost, minimizing lead time, and maximizing the quality under various constraints in the model. Finally, the m

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27

Yang, Yuzhen, and Xingsheng Gu. "Cultural-Based Genetic Tabu Algorithm for Multiobjective Job Shop Scheduling." Mathematical Problems in Engineering 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/230719.

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The job shop scheduling problem, which has been dealt with by various traditional optimization methods over the decades, has proved to be an NP-hard problem and difficult in solving, especially in the multiobjective field. In this paper, we have proposed a novel quadspace cultural genetic tabu algorithm (QSCGTA) to solve such problem. This algorithm provides a different structure from the original cultural algorithm in containing double brief spaces and population spaces. These spaces deal with different levels of populations globally and locally by applying genetic and tabu searches separatel

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28

Shi, Feng, and Jingna Lin. "Virtual Machine Resource Allocation Optimization in Cloud Computing Based on Multiobjective Genetic Algorithm." Computational Intelligence and Neuroscience 2022 (March 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/7873131.

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Cloud computing is an important milestone in the development of distributed computing as a commercial implementation, and it has good prospects. Infrastructure as a service (IaaS) is an important service mode in cloud computing. It combines massive resources scattered in different spaces into a unified resource pool by means of virtualization technology, facilitating the unified management and use of resources. In IaaS mode, all resources are provided in the form of virtual machines (VM). To achieve efficient resource utilization, reduce users’ costs, and save users’ computing time, VM allocat

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29

Zheng, X. Y., X. M. Yang, and K. L. Teo. "Sharp Minima for Multiobjective Optimization in Banach Spaces." Set-Valued Analysis 14, no. 4 (2006): 327–45. http://dx.doi.org/10.1007/s11228-006-0023-7.

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30

Cobos-Sánchez, Clemente, José Antonio Vilchez-Membrilla, Almudena Campos-Jiménez, and Francisco Javier García-Pacheco. "Pareto Optimality for Multioptimization of Continuous Linear Operators." Symmetry 13, no. 4 (2021): 661. http://dx.doi.org/10.3390/sym13040661.

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This manuscript determines the set of Pareto optimal solutions of certain multiobjective-optimization problems involving continuous linear operators defined on Banach spaces and Hilbert spaces. These multioptimization problems typically arise in engineering. In order to accomplish our goals, we first characterize, in an abstract setting, the set of Pareto optimal solutions of any multiobjective optimization problem. We then provide sufficient topological conditions to ensure the existence of Pareto optimal solutions. Next, we determine the Pareto optimal solutions of convex max–min problems in

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31

Luo, Naili, Wu Lin, Peizhi Huang, and Jianyong Chen. "An Evolutionary Algorithm with Clustering-Based Assisted Selection Strategy for Multimodal Multiobjective Optimization." Complexity 2021 (January 12, 2021): 1–13. http://dx.doi.org/10.1155/2021/4393818.

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In multimodal multiobjective optimization problems (MMOPs), multiple Pareto optimal sets, even some good local Pareto optimal sets, should be reserved, which can provide more choices for decision-makers. To solve MMOPs, this paper proposes an evolutionary algorithm with clustering-based assisted selection strategy for multimodal multiobjective optimization, in which the addition operator and deletion operator are proposed to comprehensively consider the diversity in both decision and objective spaces. Specifically, in decision space, the union population is partitioned into multiple clusters b

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32

Solatikia, Farnaz, Erdem Kiliç, and Gerhard Wilhelm Weber. "Fuzzy optimization for portfolio selection based on Embedding Theorem in Fuzzy Normed Linear Spaces." Organizacija 47, no. 2 (2014): 90–97. http://dx.doi.org/10.2478/orga-2014-0010.

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Abstract Background: This paper generalizes the results of Embedding problem of Fuzzy Number Space and its extension into a Fuzzy Banach Space C(Ω) × C(Ω), where C(Ω) is the set of all real-valued continuous functions on an open set Ω. Objectives: The main idea behind our approach consists of taking advantage of interplays between fuzzy normed spaces and normed spaces in a way to get an equivalent stochastic program. This helps avoiding pitfalls due to severe oversimplification of the reality. Method: The embedding theorem shows that the set of all fuzzy numbers can be embedded into a Fuzzy Ba

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33

Dai, Cai, and Yuping Wang. "A New Multiobjective Evolutionary Algorithm Based on Decomposition of the Objective Space for Multiobjective Optimization." Journal of Applied Mathematics 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/906147.

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In order to well maintain the diversity of obtained solutions, a new multiobjective evolutionary algorithm based on decomposition of the objective space for multiobjective optimization problems (MOPs) is designed. In order to achieve the goal, the objective space of a MOP is decomposed into a set of subobjective spaces by a set of direction vectors. In the evolutionary process, each subobjective space has a solution, even if it is not a Pareto optimal solution. In such a way, the diversity of obtained solutions can be maintained, which is critical for solving some MOPs. In addition, if a solut

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34

Qiu, Qijie, Lingjie Li, Zhijiao Xiao, Yuhong Feng, Qiuzhen Lin, and Zhong Ming. "Joint UAV Deployment and Task Offloading in Large-Cale UAV-Assisted MEC: A Multiobjective Evolutionary Algorithm." Mathematics 12, no. 13 (2024): 1966. http://dx.doi.org/10.3390/math12131966.

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With the development of digital economy technologies, mobile edge computing (MEC) has emerged as a promising computing paradigm that provides mobile devices with closer edge computing resources. Because of high mobility, unmanned aerial vehicles (UAVs) have been extensively utilized to augment MEC to improve scalability and adaptability. However, with more UAVs or mobile devices, the search space grows exponentially, leading to the curse of dimensionality. This paper focus on the combined challenges of the deployment of UAVs and the task of offloading mobile devices in a large-scale UAV-assist

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35

OBAYASHI, Shigeru, Daisuke SASAKI, and Akira OYAMA. "Finding Tradeoffs by Using Multiobjective Optimization Algorithms." TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 47, no. 155 (2004): 51–58. http://dx.doi.org/10.2322/tjsass.47.51.

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Zheng, Xi Yin, and Xiao Qi Yang. "Weak sharp minima for piecewise linear multiobjective optimization in normed spaces." Nonlinear Analysis: Theory, Methods & Applications 68, no. 12 (2008): 3771–79. http://dx.doi.org/10.1016/j.na.2007.04.018.

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37

Shimada, Yoshiyuki, Daichi Akutsu, Shinnosuke Kodama, et al. "Decision making support for designers at the early design stage regarding narrowing down the range values of design variables." Proceedings of the Design Society 4 (May 2024): 765–74. http://dx.doi.org/10.1017/pds.2024.79.

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AbstractThis study presents a search method for a solution space that aligns with a designer's design intent. The proposed method uses multiobjective optimization to determine the size of the narrowed solution space and the weakness of the constraint relationships between the design variables. The suitability of the proposed method is tested by applying it to the design problem of an electric motor for an EV, aiming to provide designers with solution spaces that offer a high degree of freedom in the later design stages and that have weaker constraint relationships among the design variables.

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38

Huynh, Diem Thi Hong. "Approximations of Variational Problems in Terms of Variational Convergence." Science and Technology Development Journal 20, K2 (2017): 107–16. http://dx.doi.org/10.32508/stdj.v20ik2.456.

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We show first the definition of variational convergence of unifunctions and their basic variational properties. In the next section, we extend this variational convergence definition in case the functions which are defined on product two sets (bifunctions or bicomponent functions). We present the definition of variational convergence of bifunctions, icluding epi/hypo convergence, minsuplop convergnece and maxinf-lop convergence, defined on metric spaces. Its variational properties are also considered. In this paper, we concern on the properties of epi/hypo convergence to apply these results on

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39

Dai, Cai, and Xiujuan Lei. "A Decomposition-Based Multiobjective Evolutionary Algorithm with Adaptive Weight Adjustment." Complexity 2018 (September 12, 2018): 1–20. http://dx.doi.org/10.1155/2018/1753071.

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Recently, decomposition-based multiobjective evolutionary algorithms have good performances in the field of multiobjective optimization problems (MOPs) and have been paid attention by many scholars. Generally, a MOP is decomposed into a number of subproblems through a set of weight vectors with good uniformly and aggregate functions. The main role of weight vectors is to ensure the diversity and convergence of obtained solutions. However, these algorithms with uniformity of weight vectors cannot obtain a set of solutions with good diversity on some MOPs with complex Pareto optimal fronts (PFs)

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40

Looye, Gertjan, and Hans-Dieter Joos. "Design of Autoland Controller Functions with Multiobjective Optimization." Journal of Guidance, Control, and Dynamics 29, no. 2 (2006): 475–84. http://dx.doi.org/10.2514/1.8797.

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41

Cheng, Franklin Y., and Dan Li. "Multiobjective optimization of structures with and without control." Journal of Guidance, Control, and Dynamics 19, no. 2 (1996): 392–97. http://dx.doi.org/10.2514/3.21631.

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42

Oyama, Akira, and Meng-Sing Liou. "Multiobjective Optimization of Rocket Engine Pumps Using Evolutionary Algorithm." Journal of Propulsion and Power 18, no. 3 (2002): 528–35. http://dx.doi.org/10.2514/2.5993.

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43

Liu, Zhi-Zhong, and Yong Wang. "Handling Constrained Multiobjective Optimization Problems With Constraints in Both the Decision and Objective Spaces." IEEE Transactions on Evolutionary Computation 23, no. 5 (2019): 870–84. http://dx.doi.org/10.1109/tevc.2019.2894743.

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44

Singh, Nagendra, and Yogendra Kumar. "Multiobjective Economic Load Dispatch Problem Solved by New PSO." Advances in Electrical Engineering 2015 (February 19, 2015): 1–6. http://dx.doi.org/10.1155/2015/536040.

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Proposed in this paper is a new particle swarm optimization technique for the solution of economic load dispatch as well as environmental emission of the thermal power plant with power balance and generation limit constraints. Economic load dispatch is an online problem to minimize the total generating cost of the thermal power plant and satisfy the equality and inequality constraints. Thermal power plants use fossil fuels for the generation of power; fossil fuel emits many toxic gases which pollute the environment. This paper not only considers the economic load dispatch problem to reduce the

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Chai, Runqi, Al Savvaris, Antonios Tsourdos, Senchun Chai, and Yuanqing Xia. "Unified Multiobjective Optimization Scheme for Aeroassisted Vehicle Trajectory Planning." Journal of Guidance, Control, and Dynamics 41, no. 7 (2018): 1521–30. http://dx.doi.org/10.2514/1.g003189.

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Suzuki, Shinji, and Takeshi Yoshizawa. "Multiobjective trajectory optimization by goal programming with fuzzy decisions." Journal of Guidance, Control, and Dynamics 17, no. 2 (1994): 297–303. http://dx.doi.org/10.2514/3.21197.

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47

Sai, Aditya, Carolina Vivas-Valencia, Thomas F. Imperiale, and Nan Kong. "Multiobjective Calibration of Disease Simulation Models Using Gaussian Processes." Medical Decision Making 39, no. 5 (2019): 540–52. http://dx.doi.org/10.1177/0272989x19862560.

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Background. Developing efficient procedures of model calibration, which entails matching model predictions to observed outcomes, has gained increasing attention. With faithful but complex simulation models established for cancer diseases, key parameters of cancer natural history can be investigated for possible fits, which can subsequently inform optimal prevention and treatment strategies. When multiple calibration targets exist, one approach to identifying optimal parameters relies on the Pareto frontier. However, computational burdens associated with higher-dimensional parameter spaces requ

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48

Kramer, Oliver. "A Review of Constraint-Handling Techniques for Evolution Strategies." Applied Computational Intelligence and Soft Computing 2010 (2010): 1–11. http://dx.doi.org/10.1155/2010/185063.

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Evolution strategies are successful global optimization methods. In many practical numerical problems constraints are not explicitly given. Evolution strategies have to incorporate techniques to optimize in restricted solution spaces. Famous constraint-handling techniques are penalty and multiobjective approaches. Past work has shown that in particular an ill-conditioned alignment between the coordinate system of Gaussian mutation and the constraint boundaries leads to premature convergence. Covariance matrix adaptation evolution strategies offer a solution to this alignment problem. Last, met

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Zheng, XiYin, and XiaoQi Yang. "The structure of weak Pareto solution sets in piecewise linear multiobjective optimization in normed spaces." Science in China Series A: Mathematics 51, no. 7 (2008): 1243–56. http://dx.doi.org/10.1007/s11425-008-0021-3.

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Kim, Jin-Hyuk, Kwang-Jin Choi, Afzal Husain, and Kwang-Yong Kim. "Multiobjective Optimization of Circumferential Casing Grooves for a Transonic Axial Compressor." Journal of Propulsion and Power 27, no. 3 (2011): 730–33. http://dx.doi.org/10.2514/1.50563.

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