Добірка наукової літератури з теми "Multi-objective maximization"
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Статті в журналах з теми "Multi-objective maximization"
Nguyen, Duy Van. "Global maximization of UTA functions in multi-objective optimization." European Journal of Operational Research 228, no. 2 (July 2013): 397–404. http://dx.doi.org/10.1016/j.ejor.2012.06.022.
Повний текст джерелаFeng, Chao, and Chao Qian. "Multi-Objective Submodular Maximization by Regret Ratio Minimization with Theoretical Guarantee." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (May 18, 2021): 12302–10. http://dx.doi.org/10.1609/aaai.v35i14.17460.
Повний текст джерелаOsiadacz, Andrzej J., and Niccolo Isoli. "Multi-Objective Optimization of Gas Pipeline Networks." Energies 13, no. 19 (October 2, 2020): 5141. http://dx.doi.org/10.3390/en13195141.
Повний текст джерелаQiu, Jianfeng, Minghui Liu, Lei Zhang, Wei Li, and Fan Cheng. "A multi-level knee point based multi-objective evolutionary algorithm for AUC maximization." Memetic Computing 11, no. 3 (February 9, 2019): 285–96. http://dx.doi.org/10.1007/s12293-019-00280-7.
Повний текст джерелаAlshareef, Haya, and Mashael Maashi. "Application of Multi-Objective Hyper-Heuristics to Solve the Multi-Objective Software Module Clustering Problem." Applied Sciences 12, no. 11 (June 2, 2022): 5649. http://dx.doi.org/10.3390/app12115649.
Повний текст джерелаHashir, Syed Muhammad, Sabyasachi Gupta, Gavin Megson, Ehsan Aryafar, and Joseph Camp. "Rate Maximization in a UAV Based Full-Duplex Multi-User Communication Network Using Multi-Objective Optimization." Electronics 11, no. 3 (January 28, 2022): 401. http://dx.doi.org/10.3390/electronics11030401.
Повний текст джерелаEne, Seval, and Nursel Öztürk. "Multi-objective green supply chain network optimization." Global Journal of Business, Economics and Management: Current Issues 7, no. 1 (April 12, 2017): 15. http://dx.doi.org/10.18844/gjbem.v7i1.1391.
Повний текст джерелаEne, Seval, and Nursel Ozturk. "Multi-objective green supply chain network optimization." Global Journal of Business, Economics and Management: Current Issues 7, no. 1 (January 15, 2018): 15–24. http://dx.doi.org/10.18844/gjbem.v7i1.1875.
Повний текст джерелаKüçükoğlu, Ilker, and Nursel Öztürk. "Multi-objective green supply chain network optimization." Global Journal of Business, Economics and Management: Current Issues 7, no. 1 (October 20, 2017): 15. http://dx.doi.org/10.18844/gjbem.v7i1.2561.
Повний текст джерелаAdeyeye, Ademola David, and Festus Adekunle Oyawale. "Lexicographic Multi-Objective Optimization Approach for Welding Flux System Design." European Journal of Engineering Science and Technology 4, no. 1 (June 18, 2022): 1–14. http://dx.doi.org/10.33422/ejest.v4i1.593.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаIt 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
1985
Частини книг з теми "Multi-objective maximization"
Maity, Santi P., and Anal Paul. "On Joint Maximization in Energy and Spectral Efficiency in Cooperative Cognitive Radio Networks." In Multi-Objective Optimization, 141–57. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_6.
Повний текст джерелаDeist, Timo M., Monika Grewal, Frank J. W. M. Dankers, Tanja Alderliesten, and Peter A. N. Bosman. "Multi-objective Learning Using HV Maximization." In Lecture Notes in Computer Science, 103–17. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27250-9_8.
Повний текст джерелаBucur, Doina, Giovanni Iacca, Andrea Marcelli, Giovanni Squillero, and Alberto Tonda. "Improving Multi-objective Evolutionary Influence Maximization in Social Networks." In Applications of Evolutionary Computation, 117–24. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77538-8_9.
Повний текст джерелаBucur, Doina, Giovanni Iacca, Andrea Marcelli, Giovanni Squillero, and Alberto Tonda. "Multi-objective Evolutionary Algorithms for Influence Maximization in Social Networks." In Applications of Evolutionary Computation, 221–33. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55849-3_15.
Повний текст джерелаGuo, Jian-bin, Fu-zan Chen, and Min-qiang Li. "A Multi-objective Optimization Approach for Influence Maximization in Social Networks." In 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.
Повний текст джерелаDe, Sagar S., and Satchidananda Dehuri. "Multi-objective Biogeography-Based Optimization for Influence Maximization-Cost Minimization in Social Networks." In 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.
Повний текст джерелаHaas, I., and S. Bekhor. "Multi-objective network design problem considering system time minimization and road safety maximization." In Transport Infrastructure and Systems, 931–38. CRC Press, 2017. http://dx.doi.org/10.1201/9781315281896-120.
Повний текст джерелаYuce, Baris, and Ernesto Mastrocinque. "Supply Chain Network Design Using an Enhanced Hybrid Swarm-Based Optimization Algorithm." In 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.
Повний текст джерелаYuce, Baris, and Ernesto Mastrocinque. "Supply Chain Network Design Using an Enhanced Hybrid Swarm-Based Optimization Algorithm." In Supply Chain and Logistics Management, 266–83. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0945-6.ch013.
Повний текст джерелаShojai, Ali Zolghadr, Jamal Shahrabi, and Masoud Jenabi. "An Integrated Bi-Objective Reverse Logistics Network Design for Remanufacturing." In 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.
Повний текст джерелаТези доповідей конференцій з теми "Multi-objective maximization"
Kandhway, Kundan. "Multi-Objective Information Maximization in a Social Network." In 2023 17th International Conference on Ubiquitous Information Management and Communication (IMCOM). IEEE, 2023. http://dx.doi.org/10.1109/imcom56909.2023.10035644.
Повний текст джерелаIshibuchi, Hisao, Yuji Sakane, Noritaka Tsukamoto, and Yusuke Nojima. "Single-objective and multi-objective formulations of solution selection for hypervolume maximization." In the 11th Annual conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1569901.1570187.
Повний текст джерелаHong, Wenjing, Guanzhou Lu, Peng Yang, Yong Wang, and Ke Tang. "A new evolutionary multi-objective algorithm for convex hull maximization." In 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015. http://dx.doi.org/10.1109/cec.2015.7256990.
Повний текст джерелаFu, Xiaoyun, Rishabh Rajendra Bhatt, Samik Basu, and A. Pavan. "Multi-Objective Submodular Optimization with Approximate Oracles and Influence Maximization." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671756.
Повний текст джерелаTeh, Jiashen, Yeong Chin Koo, Ching-Ming Lai, and Yu-Huei Cheng. "Maximization of wind energy utilization through a multi-objective optimization framework." In TENCON 2017 - 2017 IEEE Region 10 Conference. IEEE, 2017. http://dx.doi.org/10.1109/tencon.2017.8227836.
Повний текст джерелаBucur, Doina, Giovanni Iacca, Andrea Marcelli, Giovanni Squillero, and Alberto Tonda. "Evaluating surrogate models for multi-objective influence maximization in social networks." In GECCO '18: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3205651.3208238.
Повний текст джерелаBelaiche, Leila, Laid Kahloul, Saber Benharzallah, and Yousra Hafidi. "Multi-Objective Optimization-Based Approach for Throughput Maximization in Reconfigurable Manufacturing Systems." In 2018 Fifth International Symposium on Innovation in Information and Communication Technology (ISIICT). IEEE, 2018. http://dx.doi.org/10.1109/isiict.2018.8613718.
Повний текст джерелаNaranjani, Yousef, and Jian-Qiao Sun. "Multi-Objective Optimal Airfoil Design for Cargo Aircrafts." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67930.
Повний текст джерелаNobile, Enrico, Francesco Pinto, and Gino Rizzetto. "Multi-Objective Shape Optimization of Convective Wavy Channels." In 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.
Повний текст джерелаSun, Jili, Zheng Chen, Hao Yu, Peng Qian, Dahai Zhang, and Yulin Si. "Multi-Objective Offshore Wind Farm Wake Redirection Optimization for Power Maximization and Load Reduction." In 2022 American Control Conference (ACC). IEEE, 2022. http://dx.doi.org/10.23919/acc53348.2022.9867822.
Повний текст джерела