Gotowe bibliografie tematyczne / Multi-Task Optimisation

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Gotowa bibliografia na temat „Multi-Task Optimisation”

Autor: Grafiati
Data publikacji: 31 sierpnia 2024
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Multi-Task Optimisation"

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Pearce, Michael, and Juergen Branke. "Continuous multi-task Bayesian Optimisation with correlation." European Journal of Operational Research 270, no. 3 (2018): 1074–85. http://dx.doi.org/10.1016/j.ejor.2018.03.017.

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Li, Feng, Lin Zhang, T. W. Liao, and Yongkui Liu. "Multi-objective optimisation of multi-task scheduling in cloud manufacturing." International Journal of Production Research 57, no. 12 (2018): 3847–63. http://dx.doi.org/10.1080/00207543.2018.1538579.

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Panchu K., Padmanabhan, M. Rajmohan, R. Sundar, and R. Baskaran. "Multi-objective Optimisation of Multi-robot Task Allocation with Precedence Constraints." Defence Science Journal 68, no. 2 (2018): 175. http://dx.doi.org/10.14429/dsj.68.11187.

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Efficacy of the multi-robot systems depends on proper sequencing and optimal allocation of robots to the tasks. Focuses on deciding the optimal allocation of set-of-robots to a set-of-tasks with precedence constraints considering multiple objectives. Taguchi’s design of experiments based parameter tuned genetic algorithm (GA) is developed for generalised task allocation of single-task robots to multi-robot tasks. The developed methodology is tested for 16 scenarios by varying the number of robots and number of tasks. The scenarios were tested in a simulated environment with a maximum of 20 rob
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Bellotti, Renato, Romana Boiger, and Andreas Adelmann. "Fast, Efficient and Flexible Particle Accelerator Optimisation Using Densely Connected and Invertible Neural Networks." Information 12, no. 9 (2021): 351. http://dx.doi.org/10.3390/info12090351.

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Particle accelerators are enabling tools for scientific exploration and discovery in various disciplines. However, finding optimised operation points for these complex machines is a challenging task due to the large number of parameters involved and the underlying non-linear dynamics. Here, we introduce two families of data-driven surrogate models, based on deep and invertible neural networks, that can replace the expensive physics computer models. These models are employed in multi-objective optimisations to find Pareto optimal operation points for two fundamentally different types of particl
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Cvetkovski, Goga, and Lidija Petkovska. "Design Improvement of Permanent Magnet Motor Using Single- and Multi-Objective Approaches." Power Electronics and Drives 9, no. 1 (2024): 34–49. http://dx.doi.org/10.2478/pead-2024-0003.

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Abstract Optimisation, or optimal design, has become a fundamental aspect of engineering across various domains, including power devices, power systems, and industrial systems. Engineers and academics have been actively involved in optimising these systems to achieve better performance, efficiency, and cost-effectiveness. Optimising electrical machines, including permanent magnet motors, is a complex task. It often involves solving intricate problems with various parameters and constraints. Engineers use different optimisation methods to tackle these challenges. Depending on the specific requi
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Jawade, Prashant Balkrishna, and S. Ramachandram. "Task scheduling in multi-cloud environment via improved optimisation theory." International Journal of Wireless and Mobile Computing 27, no. 1 (2024): 64–77. http://dx.doi.org/10.1504/ijwmc.2024.139671.

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Trianni, Vito, and Manuel López-Ibáñez. "Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics." PLOS ONE 10, no. 8 (2015): e0136406. http://dx.doi.org/10.1371/journal.pone.0136406.

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Ramachandram, S., and Prashant Balkrishna Jawade. "Task scheduling in multi-cloud environment via improved optimisation theory." International Journal of Wireless and Mobile Computing 27, no. 1 (2024): 64–77. http://dx.doi.org/10.1504/ijwmc.2024.10064647.

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Lisowski, Józef. "Multi-Criteria Optimisation of Multi-Stage Positional Game of Vessels." Polish Maritime Research 27, no. 1 (2020): 46–52. http://dx.doi.org/10.2478/pomr-2020-0005.

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AbstractThe paper presents a mathematical model of a positional game of the safe control of a vessel in collision situations at sea, containing a description of control, state variables and state constraints as well as sets of acceptable ship strategies, as a multi-criteria optimisation task. The three possible tasks of multi-criteria optimisation were formulated in the form of non-cooperative and cooperative multi-stage positional games as well as optimal non-game controls. The multi-criteria control algorithms corresponding to these tasks were subjected to computer simulation in Matlab/Simul
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Goddanti, N. S. S. L. Venkata Jwala, Pooja Ponakampalli, Shiny Sharon Neela, Reashma Sulthana Shaik, and 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, no. 1 (2024): 1. http://dx.doi.org/10.26634/jcc.11.1.20484.

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Cloud computing is a prominent and evolving distributed computing paradigm that provides users with on-demand services through a network of diverse autonomous systems with flexible computational structures. The significance of task scheduling becomes evident, serving as a vital component to elevating cloud computing's overall performance. Streamlining cost-effective execution and optimizing resource utilization is a key objective, given the NP-hard nature of the task scheduling problem. Although numerous meta-heuristic techniques have been explored to address task allocation challenges, ample
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Rozprawy doktorskie na temat "Multi-Task Optimisation"

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Turner, Joanna. "Distributed task allocation optimisation techniques in multi-agent systems." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/36202.

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A multi-agent system consists of a number of agents, which may include software agents, robots, or even humans, in some application environment. Multi-robot systems are increasingly being employed to complete jobs and missions in various fields including search and rescue, space and underwater exploration, support in healthcare facilities, surveillance and target tracking, product manufacturing, pick-up and delivery, and logistics. Multi-agent task allocation is a complex problem compounded by various constraints such as deadlines, agent capabilities, and communication delays. In high-stake re
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Pascal, Lucas. "Optimization of deep multi-task networks." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS535.

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L'apprentissage multi-tâches est un paradigme d'apprentissage impliquant l’optimisation de paramètres par rapport à plusieurs tâches simultanément. En apprenant plusieurs tâches liées, un modèle d'apprentissage dispose d'un ensemble d'informations plus complet concernant le domaine dont les tâches sont issues, lui permettant ainsi de construire un meilleur ensemble d’hypothèse sur ce domaine. Cependant, en pratique, les gains de performance obtenus par les réseaux multi-tâches sont loin d'être systématiques. Il arrive au contraire que ces réseaux subissent une perte de performance liée à des p
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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.

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Doter des agents artificiels, tels que des robots, d'une capacité à apprendre à réaliser des tâches complexes et à s'adapter est une quête centrale de la recherche en intelligence artificielle. L'apprentissage par renforcement profond en est aujourd'hui une des méthodes privilégiées, mais n'est ni toujours simple à mettre en œuvre, ni toujours la plus performante. Dans cette thèse, nous étudions un autre concept d'apprentissage de politique qui se divise en deux étapes : une étape de résolution d'un ensemble de sous-problèmes puis une étape de généralisation. Plus formellement, la première éta
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Rommel, Cédric. "Exploration de données pour l'optimisation de trajectoires aériennes." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLX066/document.

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Cette thèse porte sur l'utilisation de données de vols pour l'optimisation de trajectoires de montée vis-à-vis de la consommation de carburant.Dans un premier temps nous nous sommes intéressé au problème d'identification de modèles de la dynamique de l'avion dans le but de les utiliser pour poser le problème d'optimisation de trajectoire à résoudre. Nous commençont par proposer une formulation statique du problème d'identification de la dynamique. Nous l'interpretons comme un problème de régression multi-tâche à structure latente, pour lequel nous proposons un modèle paramétrique. L'estimation
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Koung, Daravuth. "Cooperative navigation of a fleet of mobile robots." Electronic Thesis or Diss., Ecole centrale de Nantes, 2022. http://www.theses.fr/2022ECDN0044.

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L’intérêt pour l’intégration des systèmes multi-robots (MRS) dans les applications du monde réel augmente de plus en plus, notamment pour l’exécution de tâches complexes. Pour les tâches de transport de charges, différentes stratégies de manutention de charges ont été proposées telles que : la poussée seule, la mise en cage et la préhension. Dans cette thèse, nous souhaitons utiliser une stratégie de manipulation simple : placer l’objet à transporter au sommet d’un groupe de robots mobiles. Ainsi, cela nécessite un contrôle de formation rigide. Nous proposons deux algorithmes de formation. L’a
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Gou, Changjiang. "Task Mapping and Load-balancing for Performance, Memory, Reliability and Energy." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEN047.

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Cette thèse se concentre sur les problèmes d'optimisation multi-objectifs survenant lors de l'exécution d'applications scientifiques sur des plates-formes de calcul haute performance et des applications de streaming sur des systèmes embarqués. Ces problèmes d'optimisation se sont tous avérés NP-complets, c'est pourquoi nos efforts portent principalement sur la conception d'heuristiques efficaces pour des cas généraux et sur la proposition de solutions optimales pour des cas particuliers.Certaines applications scientifiques sont généralement modélisées comme des arbres enracinés. En raison de l
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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.

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Dans l’industrie automobile, plusieurs robots sont nécessaires pour réaliser simultanément des séquences de soudage sur un même véhicule. L’attribution et la coordination des tâches de soudage entre les robots est une phase manuelle et exigeante qui doit être optimisée à l’aide d’outils automatiques. Le temps de cycle de la cellule dépend fortement de différents facteurs robotiques tels que la répartition des tâches entre les robots, les solutions de configuration et l’évitement d’obstacles. De plus, un aspect clé, souvent négligé dans l’état de l’art, est de définir une stratégie pour résoudr
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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.

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Modern manufacturing processes have to be continuously updated to catch up with fast-evolving requirements, as dictated my competitive and dynamic markets, which demand high product variety. Indeed, in the era of smart factories and cyber-physical production systems (CPPS) we are experiencing a fast transition from mass production to mass customisation. Key Enabling Technologies (KETs) are then necessary to hinge business and market needs on digital solutions which enable the rapid delivery of new and innovative products. If on one side mass customisation imposes high level of product variety,
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Części książek na temat "Multi-Task Optimisation"

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Ramachandran, Anil, Sunil Gupta, Santu Rana, and Svetha Venkatesh. "Information-Theoretic Multi-task Learning Framework for Bayesian Optimisation." In AI 2019: Advances in Artificial Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35288-2_40.

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Lin, Jiabin, Qi Chen, Bing Xue, and Mengjie Zhang. "AMTEA-Based Multi-task Optimisation for Multi-objective Feature Selection in Classification." In Applications of Evolutionary Computation. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30229-9_40.

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Stone, Christopher, Quentin Renau, Ian Miguel, and Emma Hart. "An Evaluation of Domain-Agnostic Representations to Enable Multi-task Learning in Combinatorial Optimisation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-75623-8_31.

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Sivakumar R, John Alexis S, Manickavasagam A, and Sivakumar Rajagopal. "Enhancing Collaborative Robots Performance Through Bat Algorithm Based Multi-Objective Optimization." In Advances in Transdisciplinary Engineering. IOS Press, 2024. https://doi.org/10.3233/atde241249.

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Collaborative robots (cobots) are progressively becoming essential in industrial settings, where they operate in conjunction with human workers to execute intricate tasks. Optimising the operational efficiency of cobots entails various obstacles stemming from conflicting aims, including the minimisation of task completion time, reduction of energy consumption, and assurance of safety. This work presents a hybrid optimisation method that integrates the advantages of the Tabu Search algorithm and the Bat Algorithm to tackle these multi-objective difficulties. The Tabu Search is proficient in loc
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S., Nandhini, and Jeen Marseline K. S. "Intelligent Routing Scheme for FANET Using Bio-Inspired Optimisation." In Intelligent Decision Making Through Bio-Inspired Optimization. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2073-0.ch012.

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An unmanned aerial vehicle (UAV) is an aircraft without a human pilot that is operated remotely. When multiple UAVs are connected together for performing specific task, the arrangement is called a flying adhoc network (FANET). In a multi-UAV system, communication and coordination among the flying nodes are essential to carry out the mission properly. As the flying nodes are highly dynamic in nature, an efficient routing strategy is important. The intelligent routing decisions in this scenario can be taken by applying bio-inspired optimisation algorithms. This chapter focuses on bio-inspired op
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Bezemer M. M., Groothuis M. A., and Broenink J. F. "Analysing gCSP Models Using Runtime and Model Analysis Algorithms." In Concurrent Systems Engineering Series. IOS Press, 2009. https://doi.org/10.3233/978-1-60750-065-0-67.

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This paper presents two algorithms for analysing gCSP models in order to improve their execution performance. Designers tend to create many small separate processes for each task, which results in many (resource intensive) context switches. The research challenge is to convert the model created from a design point of view to models which have better performance during execution, without limiting the designers in their ways of working. The first algorithm analyses the model during run-time execution in order to find static sequential execution traces that allow for optimisation. The second algo
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Jullian Nathalie, Jourdan Nathalie, and Afshar Mohammad. "Hypothesis Generation for Scientific Discovery. Examples from the Use of KEM®, a Rule-Based Method for Multi-Objective Analysis and Optimization." In Solvay Pharmaceuticals Conferences. IOS Press, 2008. https://doi.org/10.3233/978-1-58603-949-3-75.

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Hypothesis generation is an essential step in scientific discovery. It involves an analysis of existing evidence, the generation of a “theory” (or model) which leads to a hypothesis (about the unknown) tested by new experiments. The design of new drugs follows a similar scheme starting with an effective mining of a huge amount of collected experimental in vitro and in vivo data. These data often come from many different areas such as chemistry, biology, pharmacology, toxicology etc. and in various formats. Extracting the critical information is a challenging task that is pe
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Xu, Xun. "Key Enabling Technologies." In Integrating Advanced Computer-Aided Design, Manufacturing, and Numerical Control. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-714-0.ch017.

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While computers have proven to be instrumental in the advancement of product design and manufacturing processes, the role that various technologies have played over the years can never be over-estimated. Because of the intimate involvement of computers in the product development chain, technologies that have severed as enablers are in many cases all software- oriented. There are a number of issues that a technology needs to address in better support of CAD, CAPP, CAM, CNC, PDM, PLM, and so forth. Knowledge acquisition and utilization is one of the top priorities and very often the first step o
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Streszczenia konferencji na temat "Multi-Task Optimisation"

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Pujari, NagaSree Keerthi, A. K. Qin, and Kishalay Mitra. "Multi-Task Optimisation-Based Yaw Control For Wind Farm Power Generation." In 2025 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2025. https://doi.org/10.1109/cec65147.2025.11043055.

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Zhang, Lifeng, Beno�t Chachuat, and Claire S. Adjiman. "Accelerating Solvent Design Optimisation with Group-Contribution Machine Learning Surrogate Classifiers." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.166568.

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Asserting the phase stability of multi-component mixtures is an important task in computer-aided mixture/blend design (CAMbD), but it is often hindered by the lack of reliable and tractable models. In this paper, we propose a group-contribution machine-learning (GC-ML) method to predict phase coexistence for a large set of ternary mixtures consisting of two solvents and one (fixed) solute. Each solvent is represented by a vector of functional group numbers, encoded by integer values. The solvent vectors are combined with mixture composition and temperature to form the input features to a GC-ML
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Lin, Jiabin, Qi Chen, Bing Xue, and Mengjie Zhang. "Multi-task optimisation for multi-objective feature selection in classification." In GECCO '22: Genetic and Evolutionary Computation Conference. ACM, 2022. http://dx.doi.org/10.1145/3520304.3528903.

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Knerr, Bastian, Martin Holzer, and Markus Rupp. "Task sheduling for power optimisation of multi frequency synchronous data flow graphs." In the 18th annual symposium. ACM Press, 2005. http://dx.doi.org/10.1145/1081081.1081100.

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Knerr, Bastian, Martin Holzer, and Markus Rupp. "Task Scheduling for Power Optimisation of Multi Frequency Synchronous Data Flow Graphs." In 2005 18th Symposium on Integrated Circuits and Systems Design. IEEE, 2005. http://dx.doi.org/10.1109/sbcci.2005.4286831.

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Yue, Zhengjun, Heidi Christensen, and Jon Barker. "Autoencoder Bottleneck Features with Multi-Task Optimisation for Improved Continuous Dysarthric Speech Recognition." In Interspeech 2020. ISCA, 2020. http://dx.doi.org/10.21437/interspeech.2020-2746.

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Xue, Y., B. Jiang, and Y. Huang. "Optimisation strategy for multi-AGV multi-task assignment scheduling based on improved particle swarm genetic algorithm." In 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.

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Berends, J. P. T. J., M. J. L. Tooren, and D. N. V. Belo. "A Distributed Multi-Disciplinary Optimisation of a Blended Wing Body UAV Using a Multi-Agent Task Environment." In 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
14th AIAA/ASME/AHS Adaptive Structures Conference
7th. American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-1610.

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Berends, J. P. T. J., and M. J. L. Van Tooren. "Design of a Multi Agent Task Environment Framework to Support Multidisciplinary Design and Optimisation." In 45th AIAA Aerospace Sciences Meeting and Exhibit. American Institute of Aeronautics and Astronautics, 2007. http://dx.doi.org/10.2514/6.2007-969.

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Saadatmand, Samad, and Salil S. Kanhere. "ACOMTA: An Ant Colony Optimisation based Multi-Task Assignment Algorithm for Reverse Auction based Mobile Crowdsensing." In 2020 IEEE 45th Conference on Local Computer Networks (LCN). IEEE, 2020. http://dx.doi.org/10.1109/lcn48667.2020.9314813.

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