Literatura académica sobre el tema "Multi-objective maximization"
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Artículos de revistas sobre el tema "Multi-objective maximization"
Nguyen, Duy Van. "Global maximization of UTA functions in multi-objective optimization". European Journal of Operational Research 228, n.º 2 (julio de 2013): 397–404. http://dx.doi.org/10.1016/j.ejor.2012.06.022.
Texto completoFeng, Chao y Chao Qian. "Multi-Objective Submodular Maximization by Regret Ratio Minimization with Theoretical Guarantee". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 14 (18 de mayo de 2021): 12302–10. http://dx.doi.org/10.1609/aaai.v35i14.17460.
Texto completoOsiadacz, Andrzej J. y Niccolo Isoli. "Multi-Objective Optimization of Gas Pipeline Networks". Energies 13, n.º 19 (2 de octubre de 2020): 5141. http://dx.doi.org/10.3390/en13195141.
Texto completoQiu, Jianfeng, Minghui Liu, Lei Zhang, Wei Li y Fan Cheng. "A multi-level knee point based multi-objective evolutionary algorithm for AUC maximization". Memetic Computing 11, n.º 3 (9 de febrero de 2019): 285–96. http://dx.doi.org/10.1007/s12293-019-00280-7.
Texto completoAlshareef, Haya y Mashael Maashi. "Application of Multi-Objective Hyper-Heuristics to Solve the Multi-Objective Software Module Clustering Problem". Applied Sciences 12, n.º 11 (2 de junio de 2022): 5649. http://dx.doi.org/10.3390/app12115649.
Texto completoHashir, Syed Muhammad, Sabyasachi Gupta, Gavin Megson, Ehsan Aryafar y Joseph Camp. "Rate Maximization in a UAV Based Full-Duplex Multi-User Communication Network Using Multi-Objective Optimization". Electronics 11, n.º 3 (28 de enero de 2022): 401. http://dx.doi.org/10.3390/electronics11030401.
Texto completoEne, Seval y Nursel Öztürk. "Multi-objective green supply chain network optimization". Global Journal of Business, Economics and Management: Current Issues 7, n.º 1 (12 de abril de 2017): 15. http://dx.doi.org/10.18844/gjbem.v7i1.1391.
Texto completoEne, Seval y Nursel Ozturk. "Multi-objective green supply chain network optimization". Global Journal of Business, Economics and Management: Current Issues 7, n.º 1 (15 de enero de 2018): 15–24. http://dx.doi.org/10.18844/gjbem.v7i1.1875.
Texto completoKüçükoğlu, Ilker y Nursel Öztürk. "Multi-objective green supply chain network optimization". Global Journal of Business, Economics and Management: Current Issues 7, n.º 1 (20 de octubre de 2017): 15. http://dx.doi.org/10.18844/gjbem.v7i1.2561.
Texto completoAdeyeye, Ademola David y Festus Adekunle Oyawale. "Lexicographic Multi-Objective Optimization Approach for Welding Flux System Design". European Journal of Engineering Science and Technology 4, n.º 1 (18 de junio de 2022): 1–14. http://dx.doi.org/10.33422/ejest.v4i1.593.
Texto completoTesis sobre el tema "Multi-objective maximization"
Dall'aglio, Giovanni. "PREFERENCE BASED APPROACH TO RISK SHARING". Doctoral thesis, Università degli studi di Trieste, 2015. http://hdl.handle.net/10077/11011.
Texto completoIt is well known that optimal risk sharing is an argument that deserves both theoretical and practical interest. It originally appears in the context of reinsurance problems, but now is widely used in a variety of financial and economical applications. The problem concerning the existence of individually rational Pareto optimal allocations, namely optimal solutions, is generally treated in the literature by considering the usual requirement of completeness over decision makers’ preferences. In this thesis we present several conditions for the existence of optimal solutions in a modern preference-based approach provided that agents’ preferences are expressed by not necessarily total preorders and by considering a topological context. We prove the equivalence between optimality and maximality with respect to a coalition preorder traducing the problem of finding optimal solutions to that of guaranteeing the existence of maximal elements for a not necessarily total preorder. In this framework a "folk theorem" is of help since it guarantees the existence of a maximal element for an upper semicontinuous preorder on a compact topological space. We study the functional approaches representing optimal risk sharing identified with the so called multi-objective maximization problem and the supconvolution problem, with the aim of incorporating functional representations of not necessarily total preorders, essentially expressed by order preserving functions and multi-utility representations. We use these two notions in order to guarantee the existence of optimal solutions, and to this aim we appropriately refer to well known results in mathematical utility theory (for example, Rader’s theorem). The case of individual preferences expressed by translation invariant total preorders is also considered, completing fundamental results from the literature also extended to the case of comonotone super-additive and positively homogeneous utility functions. When comonotone allocations are considered, we limit the research of maximal elements with respect to the coalition preorder to the set of comonotone allocations, provided that monotonicity conditions with respect to second order stochastic dominance are imposed to the individual preorders. In all our framework, we deal with risks belonging to some space of nonnegative random variables on a common probability space and, as a natural application of all our considerations, we consider the Choquet Integral when the topology L∞ is considered. Come noto, il problema di risk sharing è un argomento che interessa sia aspetti teorici che applicativi. Originariamente introdotto in contesti di riassicurazione, attualmente è ampiamente utilizzato in una varietà di applicazioni finanziarie ed economiche. Il problema legato all’esistenza di allocazioni Pareto ottimali ed individualmente razionali, definite soluzioni ottime, è generalmente trattato in letteratura considerando l’usuale assioma di completezza sulle preferenze degli agenti. In questa tesi presentiamo diverse condizioni per l'esistenza di soluzioni ottime in un moderno approccio di preferenza caratterizzato dall'espressione delle preferenze individuali per mezzo di preordini non necessariamente totali e considerando un contesto topologico. Viene dimostrata l’equivalenza tra ottimalità e massimalità rispetto ad un preordine di coalizione, traducendo così il problema di trovare soluzioni ottime nel garantire l’esistenza di elementi massimali per un preordine non necessariamente totale. In questo quadro di riferimento, un "folk theorem" è di aiuto in quanto garantisce l’esistenza di un elemento massimale per un preordine superiormente semicontinuo definito su uno spazio topologico compatto. Vengono studiati approcci funzionali legati al problema di risk sharing, identificati con il problema di massimizzazione multi-obiettivo ed il problema di sup-convoluzione, con l’obiettivo di incorporare rappresentazioni funzionali di preordini non necessariamente totali, essenzialmente definite da funzioni order preserving e rappresentazioni di multi-utilità. Queste due notazioni vengono utilizzate in modo da garantire l’esistenza di soluzioni ottime, e a questo scopo ci riferiamo in modo appropriato a ben noti risultati in teoria dell’utilità (ad esempio, il teorema di Rader). Il caso di preferenze individuali espresse da preordini totali invarianti per traslazioni è anche considerato, a completamento di fondamentali risultati presenti in letteratura ed estesi anche al caso di funzioni di utilità che soddisfino alle proprietà di comonotona super-additività e positiva omogeneità. Quando si considerano allocazioni comonotone, ci limitiamo alla ricerca di elementi massimali rispetto al preordine di coalizione nell’insieme delle allocazioni comonotone, purchè vengano imposte condizioni di monotonia sui preordini individuali rispetto alla dominanza stocastica di secondo ordine. In tutto il nostro contesto di riferimento affrontiamo il caso di rischi appartenenti a spazi di variabili aleatorie non-negative definite su un comune spazio di probabilità e come naturale applicazione consideriamo l’integrale di Choquet nel caso venga considerata la topologia L∞.
XXVII Ciclo
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Capítulos de libros sobre el tema "Multi-objective maximization"
Maity, Santi P. y Anal Paul. "On Joint Maximization in Energy and Spectral Efficiency in Cooperative Cognitive Radio Networks". En Multi-Objective Optimization, 141–57. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_6.
Texto completoDeist, Timo M., Monika Grewal, Frank J. W. M. Dankers, Tanja Alderliesten y Peter A. N. Bosman. "Multi-objective Learning Using HV Maximization". En Lecture Notes in Computer Science, 103–17. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27250-9_8.
Texto completoBucur, Doina, Giovanni Iacca, Andrea Marcelli, Giovanni Squillero y Alberto Tonda. "Improving Multi-objective Evolutionary Influence Maximization in Social Networks". En Applications of Evolutionary Computation, 117–24. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77538-8_9.
Texto completoBucur, Doina, Giovanni Iacca, Andrea Marcelli, Giovanni Squillero y Alberto Tonda. "Multi-objective Evolutionary Algorithms for Influence Maximization in Social Networks". En Applications of Evolutionary Computation, 221–33. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55849-3_15.
Texto completoGuo, Jian-bin, Fu-zan Chen y Min-qiang Li. "A Multi-objective Optimization Approach for Influence Maximization in Social Networks". En Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management 2018, 706–15. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3402-3_74.
Texto completoDe, Sagar S. y Satchidananda Dehuri. "Multi-objective Biogeography-Based Optimization for Influence Maximization-Cost Minimization in Social Networks". En Learning and Analytics in Intelligent Systems, 11–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39033-4_2.
Texto completoHaas, I. y S. Bekhor. "Multi-objective network design problem considering system time minimization and road safety maximization". En Transport Infrastructure and Systems, 931–38. CRC Press, 2017. http://dx.doi.org/10.1201/9781315281896-120.
Texto completoYuce, Baris y Ernesto Mastrocinque. "Supply Chain Network Design Using an Enhanced Hybrid Swarm-Based Optimization Algorithm". En Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics, 95–112. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9644-0.ch003.
Texto completoYuce, Baris y Ernesto Mastrocinque. "Supply Chain Network Design Using an Enhanced Hybrid Swarm-Based Optimization Algorithm". En Supply Chain and Logistics Management, 266–83. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0945-6.ch013.
Texto completoShojai, Ali Zolghadr, Jamal Shahrabi y Masoud Jenabi. "An Integrated Bi-Objective Reverse Logistics Network Design for Remanufacturing". En Exploring Innovative and Successful Applications of Soft Computing, 281–316. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4785-5.ch015.
Texto completoActas de conferencias sobre el tema "Multi-objective maximization"
Kandhway, Kundan. "Multi-Objective Information Maximization in a Social Network". En 2023 17th International Conference on Ubiquitous Information Management and Communication (IMCOM). IEEE, 2023. http://dx.doi.org/10.1109/imcom56909.2023.10035644.
Texto completoIshibuchi, Hisao, Yuji Sakane, Noritaka Tsukamoto y Yusuke Nojima. "Single-objective and multi-objective formulations of solution selection for hypervolume maximization". En the 11th Annual conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1569901.1570187.
Texto completoHong, Wenjing, Guanzhou Lu, Peng Yang, Yong Wang y Ke Tang. "A new evolutionary multi-objective algorithm for convex hull maximization". En 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015. http://dx.doi.org/10.1109/cec.2015.7256990.
Texto completoFu, Xiaoyun, Rishabh Rajendra Bhatt, Samik Basu y A. Pavan. "Multi-Objective Submodular Optimization with Approximate Oracles and Influence Maximization". En 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671756.
Texto completoTeh, Jiashen, Yeong Chin Koo, Ching-Ming Lai y Yu-Huei Cheng. "Maximization of wind energy utilization through a multi-objective optimization framework". En TENCON 2017 - 2017 IEEE Region 10 Conference. IEEE, 2017. http://dx.doi.org/10.1109/tencon.2017.8227836.
Texto completoBucur, Doina, Giovanni Iacca, Andrea Marcelli, Giovanni Squillero y Alberto Tonda. "Evaluating surrogate models for multi-objective influence maximization in social networks". En GECCO '18: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3205651.3208238.
Texto completoBelaiche, Leila, Laid Kahloul, Saber Benharzallah y Yousra Hafidi. "Multi-Objective Optimization-Based Approach for Throughput Maximization in Reconfigurable Manufacturing Systems". En 2018 Fifth International Symposium on Innovation in Information and Communication Technology (ISIICT). IEEE, 2018. http://dx.doi.org/10.1109/isiict.2018.8613718.
Texto completoNaranjani, Yousef y Jian-Qiao Sun. "Multi-Objective Optimal Airfoil Design for Cargo Aircrafts". En ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67930.
Texto completoNobile, Enrico, Francesco Pinto y Gino Rizzetto. "Multi-Objective Shape Optimization of Convective Wavy Channels". En ASME 2005 Summer Heat Transfer Conference collocated with the ASME 2005 Pacific Rim Technical Conference and Exhibition on Integration and Packaging of MEMS, NEMS, and Electronic Systems. ASMEDC, 2005. http://dx.doi.org/10.1115/ht2005-72635.
Texto completoSun, Jili, Zheng Chen, Hao Yu, Peng Qian, Dahai Zhang y Yulin Si. "Multi-Objective Offshore Wind Farm Wake Redirection Optimization for Power Maximization and Load Reduction". En 2022 American Control Conference (ACC). IEEE, 2022. http://dx.doi.org/10.23919/acc53348.2022.9867822.
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