Literatura académica sobre el tema "Multi-Task Optimisation"
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Artículos de revistas sobre el tema "Multi-Task Optimisation"
Pearce, Michael y Juergen Branke. "Continuous multi-task Bayesian Optimisation with correlation". European Journal of Operational Research 270, n.º 3 (noviembre de 2018): 1074–85. http://dx.doi.org/10.1016/j.ejor.2018.03.017.
Texto completoLi, Feng, Lin Zhang, T. W. Liao y Yongkui Liu. "Multi-objective optimisation of multi-task scheduling in cloud manufacturing". International Journal of Production Research 57, n.º 12 (8 de noviembre de 2018): 3847–63. http://dx.doi.org/10.1080/00207543.2018.1538579.
Texto completoPanchu K., Padmanabhan, M. Rajmohan, R. Sundar y R. Baskaran. "Multi-objective Optimisation of Multi-robot Task Allocation with Precedence Constraints". Defence Science Journal 68, n.º 2 (13 de marzo de 2018): 175. http://dx.doi.org/10.14429/dsj.68.11187.
Texto completoBellotti, Renato, Romana Boiger y Andreas Adelmann. "Fast, Efficient and Flexible Particle Accelerator Optimisation Using Densely Connected and Invertible Neural Networks". Information 12, n.º 9 (28 de agosto de 2021): 351. http://dx.doi.org/10.3390/info12090351.
Texto completoCvetkovski, Goga y Lidija Petkovska. "Design Improvement of Permanent Magnet Motor Using Single- and Multi-Objective Approaches". Power Electronics and Drives 9, n.º 1 (1 de enero de 2024): 34–49. http://dx.doi.org/10.2478/pead-2024-0003.
Texto completoTrianni, Vito y Manuel López-Ibáñez. "Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics". PLOS ONE 10, n.º 8 (21 de agosto de 2015): e0136406. http://dx.doi.org/10.1371/journal.pone.0136406.
Texto completoRamachandram, S. y Prashant Balkrishna Jawade. "Task scheduling in multi-cloud environment via improved optimisation theory". International Journal of Wireless and Mobile Computing 27, n.º 1 (2024): 64–77. http://dx.doi.org/10.1504/ijwmc.2024.10064647.
Texto completoJawade, Prashant Balkrishna y S. Ramachandram. "Task scheduling in multi-cloud environment via improved optimisation theory". International Journal of Wireless and Mobile Computing 27, n.º 1 (2024): 64–77. http://dx.doi.org/10.1504/ijwmc.2024.139671.
Texto completoLisowski, Józef. "Multi-Criteria Optimisation of Multi-Stage Positional Game of Vessels". Polish Maritime Research 27, n.º 1 (1 de marzo de 2020): 46–52. http://dx.doi.org/10.2478/pomr-2020-0005.
Texto completoGoddanti, N. S. S. L. Venkata Jwala, Pooja Ponakampalli, Shiny Sharon Neela, Reashma Sulthana Shaik y V. Suresh Chintalapudi. "An OptiAssign-PSO based optimisation for multi-objective multi-level multi-task scheduling in cloud computing environment". i-manager’s Journal on Cloud Computing 11, n.º 1 (2024): 1. http://dx.doi.org/10.26634/jcc.11.1.20484.
Texto completoTesis sobre el tema "Multi-Task Optimisation"
Turner, Joanna. "Distributed task allocation optimisation techniques in multi-agent systems". Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/36202.
Texto completoPascal, Lucas. "Optimization of deep multi-task networks". Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS535.
Texto completoMulti-task learning (MTL) is a learning paradigm involving the joint optimization of parameters with respect to multiple tasks. By learning multiple related tasks, a learner receives more complete and complementary information on the input domain from which the tasks are issued. This allows to gain better understanding of the domain by building a more accurate set of assumptions of it. However, in practice, the broader use of MTL is hindered by the lack of consistent performance gains observed by deep multi-task networks. It is often the case that deep MTL networks suffer from performance degradation caused by task interference. This thesis addresses the problem of task interference in Multi-Task learning, in order to improve the generalization capabilities of deep neural networks
Anne, Timothée. "L'optimisation multi-tâche et ses applications à la robotique : d'abord résoudre, ensuite généraliser". Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0045.
Texto completoGranting artificial agents, such as robots, the capability to learn how to solve complex tasks and adapt is a central quest in artificial intelligence research. Today, reinforcement learning is favored, but it is neither straightforward to implement nor consistently effective. In this thesis, we explore an alternative policy learning concept divided into two stages: solving a set of sub-problems and generalization. More formally, the first stage reformulates the general problem into a multi-task problem to obtain a dataset of high-performing solutions. The second stage applies supervised learning to this dataset to train a general policy. We first evaluate the viability of this concept in learning fall avoidance reflexes with a real humanoid robot. It enables learning behaviors in simulation that prevent falling in more than 75% % of cases, and these behaviors are robust enough to function on the real robot. We then develop a multi-task multi-behavior quality-diversity algorithm, Multi-Task Multi-Behavior MAP-Elites, to improve the sample efficiency of the first resolution stage. We demonstrate its application in fall avoidance reflex learning, where it performs better than a deep reinforcement learning algorithm and also enables generalization to more realistic environments. Finally, we propose to go from a discrete resolution stage to a continuous resolution stage. To do so, we reformulate the multi-task black-box optimization problem as a parametric optimization problem and propose a method to solve it: Parametric-Task MAP-Elites. Parametric-Task MAP-Elites solves a new task at each iteration, asymptotically covering the task space. After consuming its evaluation budget, Parametric-Task MAP-Elites distills the solutions found into a policy to generalize to the entire continuous space. Multi-task optimization is an underexploited method that has demonstrated in this thesis its ability to solve some robotics problems more straightforwardly and effectively than deep reinforcement learning
Rommel, Cédric. "Exploration de données pour l'optimisation de trajectoires aériennes". Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLX066/document.
Texto completoThis thesis deals with the use of flight data for the optimization of climb trajectories with relation to fuel consumption.We first focus on methods for identifying the aircraft dynamics, in order to plug it in the trajectory optimization problem. We suggest a static formulation of the identification problem, which we interpret as a structured multi-task regression problem. In this framework, we propose parametric models and use different maximum likelihood approaches to learn the unknown parameters.Furthermore, polynomial models are considered and an extension to the structured multi-task setting of the bootstrap Lasso is used to make a consistent selection of the monomials despite the high correlations among them.Next, we consider the problem of assessing the optimized trajectories relatively to the validity region of the identified models. For this, we propose a probabilistic criterion for quantifying the closeness between an arbitrary curve and a set of trajectories sampled from the same stochastic process. We propose a class of estimators of this quantity and prove their consistency in some sense. A nonparemetric implementation based on kernel density estimators, as well as a parametric implementation based on Gaussian mixtures are presented. We introduce the later as a penalty term in the trajectory optimization problem, which allows us to control the trade-off between trajectory acceptability and consumption reduction
Koung, Daravuth. "Cooperative navigation of a fleet of mobile robots". Electronic Thesis or Diss., Ecole centrale de Nantes, 2022. http://www.theses.fr/2022ECDN0044.
Texto completoThe interest in integrating multirobot systems (MRS) into real-world applications is increasing more and more, especially for performing complex tasks. For loadcarrying tasks, various load-handling strategies have been proposed such as: pushingonly, caging, and grasping. In this thesis, we aim to use a simple handling strategy: placing the carrying object on top of a group of wheeled mobile robots. Thus, it requires a rigid formation control. A consensus algorithm is one of the two formation controllers we apply to the system. We adapt a dynamic flocking controller to be used in the singleintegrator system, and we propose an obstacle avoidance that can prevent splitting while evading the obstacles. The second formation control is based on hierarchical quadratic programming (HQP). The problem is decomposed into multiple task objectives: formation, navigation, obstacle avoidance, velocity limits. These tasks are represented by equality and inequality constraints with different levels of priority, which are solved sequentially by the HQP. Lastly, a study on task allocation algorithms (Contract Net Protocol and Tabu Search) is carried out in order to determine an appropriate solution for allocating tasks in the industrial environment
Gou, Changjiang. "Task Mapping and Load-balancing for Performance, Memory, Reliability and Energy". Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEN047.
Texto completoThis thesis focuses on multi-objective optimization problems arising when running scientific applications on high performance computing platforms and streaming applications on embedded systems. These optimization problems are all proven to be NP-complete, hence our efforts are mainly on designing efficient heuristics for general cases, and proposing optimal solutions for special cases.Some scientific applications are commonly modeled as rooted trees. Due to the size of temporary data, processing such a tree may exceed the local memory capacity. A practical solution on a multiprocessor system is to partition the tree into many subtrees, and run each on a processor, which is equipped with a local memory. We studied how to partition the tree into several subtrees such that each subtree fits in local memory and the makespan is minimized, when communication costs between processors are accounted for.Then, a practical work of tree scheduling arising in parallel sparse matrix solver is examined. The objective is to minimize the factorization time by exhibiting good data locality and load balancing. The proportional mapping technique is a widely used approach to solve this resource-allocation problem. It achieves good data locality by assigning the same processors to large parts of the task tree. However, it may limit load balancing in some cases. Based on proportional mapping, a dynamic scheduling algorithm is proposed. It relaxes the data locality criterion to improve load balancing. The performance of our approach has been validated by extensive experiments with the parallel sparse matrix direct solver PaStiX.Streaming applications often appear in video and audio domains. They are characterized by a series of operations on streaming data, and a high throughput. Multi-Processor System on Chip (MPSoC) is a multi/many-core embedded system that integrates many specific cores through a high speed interconnect on a single die. Such systems are widely used for multimedia applications. Lots of MPSoCs are batteries-operated. Such a tight energy budget intrinsically calls for an efficient schedule to meet the intensive computation demands. Dynamic Voltage and Frequency Scaling (DVFS) can save energy by decreasing the frequency and voltage at the price of increasing failure rates. Another technique to reduce the energy cost and meet the reliability target consists in running multiple copies of tasks. We first model applications as linear chains and study how to minimize the energy consumption under throughput and reliability constraints, using DVFS and duplication technique on MPSoC platforms.Then, in a following study, with the same optimization goal, we model streaming applications as series-parallel graphs, which are more complex than simple chains and more realistic. The target platform has a hierarchical communication system with two levels, which is common in embedded systems and high performance computing platforms. The reliability is guaranteed through either running tasks at the maximum speed or triplication of tasks. Several efficient heuristics are proposed to tackle this NP-complete optimization problem
Touzani, Hicham. "Planification Multi-Robot du Problème de Répartition de Tâches avec Évitement Automatique de Collisions et Optimisation du Temps de Cycle : Application à la Chaîne de Production Automobile". Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST079.
Texto completoIn the automotive industry, several robots are required to simultaneously carry out welding sequences on the same vehicle. Assigning and coordinating welding tasks between robots is a manual and challenging phase that must be optimized using automatic tools. The cycle time of the cell strongly depends on different robotic factors such as the task allocation among the robots, the configuration solutions, and obstacle avoidance. Moreover, a key aspect, often neglected in the state-ofthe- art, is to define a strategy to solve the robotic task sequencing with an effective robot-robot collision avoidance integration. This thesis is motivated by solving this industrial problem and seeks to raise different research challenges. It begins by presenting the current state-of-the-art solutions regarding robotic planning. An in-depth investigation is carried out on the related existing academic/industrial solutions to solve the robotic task sequencing problem, particularly for multi-robot systems. This investigation helps identify the challenges when integrating several robotic factors into the optimization process. An efficient iterative algorithm that generates a high-quality solution for the Multi-Robotic Task Sequencing Problem is presented. This algorithm manages not only the mentioned robotic factors but also aspects related to accessibility constraints and mutual collision avoidance. In addition, a home-developed planner (RoboTSPlanner) handling six-axis robots has been validated in a real case scenario. In order to ensure the completeness of the proposed methodology, we perform optimization in the task, configuration, and coordination space in a synergistic way. Compared to the existing approaches, both simulation and real experiments reveal positive results in terms of cycle time and show the ability of this method to be interfaced with both industrial simulation software and ROS-I tools
Vitolo, Ferdinando. "Multi-Attribute Task Sequencing Optimisation with Neighbourhoods for Robotic Systems". Tesi di dottorato, 2017. http://www.fedoa.unina.it/11509/1/PhD-Thesis_Vitolo.pdf.
Texto completoCapítulos de libros sobre el tema "Multi-Task Optimisation"
Ramachandran, Anil, Sunil Gupta, Santu Rana y Svetha Venkatesh. "Information-Theoretic Multi-task Learning Framework for Bayesian Optimisation". En AI 2019: Advances in Artificial Intelligence, 497–509. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35288-2_40.
Texto completoLin, Jiabin, Qi Chen, Bing Xue y Mengjie Zhang. "AMTEA-Based Multi-task Optimisation for Multi-objective Feature Selection in Classification". En Applications of Evolutionary Computation, 623–39. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30229-9_40.
Texto completoS., Nandhini y Jeen Marseline K. S. "Intelligent Routing Scheme for FANET Using Bio-Inspired Optimisation". En Intelligent Decision Making Through Bio-Inspired Optimization, 218–26. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2073-0.ch012.
Texto completoXu, Xun. "Key Enabling Technologies". En Integrating Advanced Computer-Aided Design, Manufacturing, and Numerical Control, 354–93. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-714-0.ch017.
Texto completoActas de conferencias sobre el tema "Multi-Task Optimisation"
Lin, Jiabin, Qi Chen, Bing Xue y Mengjie Zhang. "Multi-task optimisation for multi-objective feature selection in classification". En GECCO '22: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3520304.3528903.
Texto completoKnerr, Bastian, Martin Holzer y Markus Rupp. "Task sheduling for power optimisation of multi frequency synchronous data flow graphs". En the 18th annual symposium. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1081081.1081100.
Texto completoKnerr, Bastian, Martin Holzer y Markus Rupp. "Task Scheduling for Power Optimisation of Multi Frequency Synchronous Data Flow Graphs". En 2005 18th Symposium on Integrated Circuits and Systems Design. IEEE, 2005. http://dx.doi.org/10.1109/sbcci.2005.4286831.
Texto completoYue, Zhengjun, Heidi Christensen y Jon Barker. "Autoencoder Bottleneck Features with Multi-Task Optimisation for Improved Continuous Dysarthric Speech Recognition". En Interspeech 2020. ISCA: ISCA, 2020. http://dx.doi.org/10.21437/interspeech.2020-2746.
Texto completoXue, Y., B. Jiang y Y. Huang. "Optimisation strategy for multi-AGV multi-task assignment scheduling based on improved particle swarm genetic algorithm". En 5th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM 2023). Institution of Engineering and Technology, 2023. http://dx.doi.org/10.1049/icp.2023.2928.
Texto completoBerends, J. P. T. J., M. J. L. Tooren y D. N. V. Belo. "A Distributed Multi-Disciplinary Optimisation of a Blended Wing Body UAV Using a Multi-Agent Task Environment". En 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
14th AIAA/ASME/AHS Adaptive Structures Conference
7th. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-1610.
Berends, J. P. T. J. y M. J. L. Van Tooren. "Design of a Multi Agent Task Environment Framework to Support Multidisciplinary Design and Optimisation". En 45th AIAA Aerospace Sciences Meeting and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2007. http://dx.doi.org/10.2514/6.2007-969.
Texto completoSaadatmand, Samad y Salil S. Kanhere. "ACOMTA: An Ant Colony Optimisation based Multi-Task Assignment Algorithm for Reverse Auction based Mobile Crowdsensing". En 2020 IEEE 45th Conference on Local Computer Networks (LCN). IEEE, 2020. http://dx.doi.org/10.1109/lcn48667.2020.9314813.
Texto completoBaert, Lieven, Paul Beaucaire, Michaël Leborgne, Caroline Sainvitu y Ingrid Lepot. "Tackling Highly Constrained Design Problems: Efficient Optimisation of a Highly Loaded Transonic Compressor". En ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-64610.
Texto completoRecalde, Luis, Hong Yue, William Leithead, Olimpo Anaya-Lara, Hongda Liu y Jiang You. "Hybrid Renewable Energy Systems Sizing for Offshore Multi-Purpose Platforms". En ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-96017.
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