Relevant bibliographies by topics / Multi-Objective

Academic literature on the topic 'Multi-Objective'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Multi-Objective"

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Dimitrov, Dimitar, Pierre-Brice Wieber, and Adrien Escande. "Multi-Objective Control of Robots." Journal of the Robotics Society of Japan 32, no. 6 (2014): 512–18. http://dx.doi.org/10.7210/jrsj.32.512.

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Neralić, Luka, and Sanjo Zlobec. "LFS functions in multi-objective programming." Applications of Mathematics 41, no. 5 (1996): 347–66. http://dx.doi.org/10.21136/am.1996.134331.

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Zhang, Kai, Minshi Chen, Xin Xu, and Gary G. Yen. "Multi-objective evolution strategy for multimodal multi-objective optimization." Applied Soft Computing 101 (March 2021): 107004. http://dx.doi.org/10.1016/j.asoc.2020.107004.

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Tekin, Cem, and Eralp Turgay. "Multi-objective Contextual Multi-armed Bandit With a Dominant Objective." IEEE Transactions on Signal Processing 66, no. 14 (July 15, 2018): 3799–813. http://dx.doi.org/10.1109/tsp.2018.2841822.

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Guo, Weian, Ming Chen, Lei Wang, and Qidi Wu. "Hyper multi-objective evolutionary algorithm for multi-objective optimization problems." Soft Computing 21, no. 20 (May 24, 2016): 5883–91. http://dx.doi.org/10.1007/s00500-016-2163-5.

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Velea, Marian N., and Simona Lache. "Decision Making Process on Multi-Objective Optimization Results." International Journal of Materials, Mechanics and Manufacturing 4, no. 3 (2015): 213–17. http://dx.doi.org/10.7763/ijmmm.2016.v4.259.

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Brunello, Andrea, Enrico Marzano, Angelo Montanari, and Guido Sciavicco. "Decision Tree Pruning via Multi-Objective Evolutionary Computation." International Journal of Machine Learning and Computing 7, no. 6 (December 2017): 167–75. http://dx.doi.org/10.18178/ijmlc.2017.7.6.641.

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Lee, Chen Jian Ken, and Hirohisa Noguchi. "515 Multi-objective topology optimization involving 3D surfaces." Proceedings of The Computational Mechanics Conference 2008.21 (2008): 233–34. http://dx.doi.org/10.1299/jsmecmd.2008.21.233.

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M.Jayalakshmi, M. Jayalakshmi, and P. Pandian P.Pandian. "Solving Fully Fuzzy Multi-Objective Linear Programming Problems." International Journal of Scientific Research 3, no. 4 (June 1, 2012): 1–6. http://dx.doi.org/10.15373/22778179/apr2014/174.

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Guo, Yi Nan, Yuan Yuan Cao, and Dan Dan Liu. "Multi-Population Multi-Objective Cultural Algorithm." Advanced Materials Research 156-157 (October 2010): 52–55. http://dx.doi.org/10.4028/www.scientific.net/amr.156-157.52.

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In existing multi-population multi-objective cultural algorithms, information are exchanged among sub-populations by individuals. However, migrated individuals can not reflect the evolution information enough, which limits the evolution performance.In order to enhance the migration efficiency, a novel multi-population multi-objective cultural algorithm adopting knowledge migration is proposed. Implicit knowledge extracted from the evolution process of each sub-population directly reflects the information about dominant search space. By migrating the knowledge among sub-populations at the constant interval, the algorithm realizes more effective interaction with less communication cost. Taken benchmark functions as the examples, simulation results indicate that the algorithm can effectively obtain the Pareto-optimal sets of multi-objective optimization problems. The distribution performance is also improved.
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Dissertations / Theses on the topic "Multi-Objective"

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Maashi, Mashael. "An investigation of multi-objective hyper-heuristics for multi-objective optimisation." Thesis, University of Nottingham, 2014. http://eprints.nottingham.ac.uk/14171/.

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In this thesis, we investigate and develop a number of online learning selection choice function based hyper-heuristic methodologies that attempt to solve multi-objective unconstrained optimisation problems. For the first time, we introduce an online learning selection choice function based hyperheuristic framework for multi-objective optimisation. Our multi-objective hyper-heuristic controls and combines the strengths of three well-known multi-objective evolutionary algorithms (NSGAII, SPEA2, and MOGA), which are utilised as the low level heuristics. A choice function selection heuristic acts as a high level strategy which adaptively ranks the performance of those low-level heuristics according to feedback received during the search process, deciding which one to call at each decision point. Four performance measurements are integrated into a ranking scheme which acts as a feedback learning mechanism to provide knowledge of the problem domain to the high level strategy. To the best of our knowledge, for the first time, this thesis investigates the influence of the move acceptance component of selection hyper-heuristics for multi-objective optimisation. Three multi-objective choice function based hyper-heuristics, combined with different move acceptance strategies including All-Moves as a deterministic move acceptance and the Great Deluge Algorithm (GDA) and Late Acceptance (LA) as a nondeterministic move acceptance function. GDA and LA require a change in the value of a single objective at each step and so a well-known hypervolume metric, referred to as D metric, is proposed for their applicability to the multi-objective optimisation problems. D metric is used as a way of comparing two non-dominated sets with respect to the objective space. The performance of the proposed multi-objective selection choice function based hyper-heuristics is evaluated on the Walking Fish Group (WFG) test suite which is a common benchmark for multi-objective optimisation. Additionally, the proposed approaches are applied to the vehicle crashworthiness design problem, in order to test its effectiveness on a realworld multi-objective problem. The results of both benchmark test problems demonstrate the capability and potential of the multi-objective hyper-heuristic approaches in solving continuous multi-objective optimisation problems. The multi-objective choice function Great Deluge Hyper-Heuristic (HHMO_CF_GDA) turns out to be the best choice for solving these types of problems.
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Tezcaner, Diclehan. "Multi-objective Route Selection." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/2/12610767/index.pdf.

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In this thesis, we address the route selection problem for Unmanned Air Vehicles (UAV) under multiple objectives. We consider a general case for this problem where the UAV has to visit several targets and return to the base. For this case, there are multiple combinatorial problems to be considered. First, the paths to be followed between any pairs of targets should be determined. This part can be considered as a multi-objective shortest path problem. Additionally, we need to determine the order of the targets to be visited. This in turn, is a multi-objective traveling salesperson problem. The overall problem is a combination of these two combinatorial problems. The route selection for UAVs has been studied by several researchers, mainly in the military context. They considered a linear combination of the two objectives
minimizing distance traveled and minimizing radar detection threat
and proposed heuristics for the minimization of the composite single objective problem. We treat these two objectives separately. We develop an evolutionary algorithm to determine the efficient tours. We also consider an exact interactive approach to identify the best paths and tours of a decision maker. We tested the two solution approaches on both small-sized and large-sized problem instances.
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Chatterjee, H. K. "Multi-objective, interactive programming." Thesis, University of Manchester, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.376590.

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Lewis, Alyn Martyn. "Multi-objective bandit problems." Thesis, Keele University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283977.

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Jamil, Ramey. "Multi-objective control allocation." Thesis, Cranfield University, 2012. http://dspace.lib.cranfield.ac.uk/handle/1826/10735.

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Performance and redundancy requirements imposed on state-of-the-art unmmaned combat aerial vehicles often lead to over-actuated systems with a mix of conventional and novel moment generators. Consequently, control allocation schemes have become a crucial part of the flight control architecture and their design is now a growing problem. This thesis presents a four control allocation scheme designed to meet multiple objectives and resolve objective conflicts by finding the ‘Pareto’ optimal solution, namely; Weighted Control Allocation, Minimax Control Allocation, Canonical Control Allocation and Classical. This is defined as a solution to the multi-objective optimisation problem which is non-dominated for all objectives. The scheme is applied to a six degrees of freedom nonlinear simulation of an aircraft equipped with conventional control surfaces as well as fluidic thrust vectoring and circulation control. The results indicate a perfect allocation of the total control demand onto the actuator suite.
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Lidberg, Simon. "Evolving Cuckoo Search : From single-objective to multi-objective." Thesis, Högskolan i Skövde, Institutionen för teknik och samhälle, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-5309.

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This thesis aims to produce a novel multi-objective algorithm that is based on Cuckoo Search by Dr. Xin-She Yang. Cuckoo Search is a promising nature-inspired meta-heuristic optimization algorithm, which currently is only able to solve single-objective optimization problems. After an introduction, a number of theoretical points are presented as a basis for the decision of which algorithms to hybridize Cuckoo Search with. These are then reviewed in detail and verified against current benchmark algorithms to evaluate their efficiency. To test the proposed algorithm in a new setting, a real-world combinatorial problem is used. The proposed algorithm is then used as an optimization engine for a simulation-based system and compared against a current implementation.
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Dasgupta, Sumantra. "Multi-objective stochastic path planning." [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2755.

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Amouzgar, Kaveh. "Metamodel based multi-objective optimization." Licentiate thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH. Forskningsmiljö Produktutveckling - Simulering och optimering, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-28432.

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As a result of the increase in accessibility of computational resources and the increase in the power of the computers during the last two decades, designers are able to create computer models to simulate the behavior of a complex products. To address global competitiveness, companies are forced to optimize their designs and products. Optimizing the design needs several runs of computationally expensive simulation models. Therefore, using metamodels as an efficient and sufficiently accurate approximate of the simulation model is necessary. Radial basis functions (RBF) is one of the several metamodeling methods that can be found in the literature. The established approach is to add a bias to RBF in order to obtain a robust performance. The a posteriori bias is considered to be unknown at the beginning and it is defined by imposing extra orthogonality constraints. In this thesis, a new approach in constructing RBF with the bias to be set a priori by using the normal equation is proposed. The performance of the suggested approach is compared to the classic RBF with a posteriori bias. Another comprehensive comparison study by including several modeling criteria, such as problem dimension, sampling technique and size of samples is conducted. The studies demonstrate that the suggested approach with a priori bias is in general as good as the performance of RBF with a posteriori bias. Using the a priori RBF, it is clear that the global response is modeled with the bias and that the details are captured with radial basis functions. Multi-objective optimization and the approaches used in solving such problems are briefly described in this thesis. One of the methods that proved to be efficient in solving multi-objective optimization problems (MOOP) is the strength Pareto evolutionary algorithm (SPEA2). Multi-objective optimization of a disc brake system of a heavy truck by using SPEA2 and RBF with a priori bias is performed. As a result, the possibility to reduce the weight of the system without extensive compromise in other objectives is found. Multi-objective optimization of material model parameters of an adhesive layer with the aim of improving the results of a previous study is implemented. The result of the original study is improved and a clear insight into the nature of the problem is revealed.
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Roland, Julien. "Inverse multi-objective combinatorial optimization." Doctoral thesis, Universite Libre de Bruxelles, 2013. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209383.

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The initial question addressed in this thesis is how to take into account the multi-objective aspect of decision problems in inverse optimization. The most straightforward extension consists of finding a minimal adjustment of the objective functions coefficients such that a given feasible solution becomes efficient. However, there is not only a single question raised by inverse multi-objective optimization, because there is usually not a single efficient solution. The way we define inverse multi-objective

optimization takes into account this important aspect. This gives rise to many questions which are identified by a precise notation that highlights a large collection of inverse problems that could be investigated. In this thesis, a selection of inverse problems are presented and solved. This selection is motivated by their possible applications and the interesting theoretical questions they can rise in practice.
Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished

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Wang, Weijia. "Multi-objective sequential decision making." Phd thesis, Université Paris Sud - Paris XI, 2014. http://tel.archives-ouvertes.fr/tel-01057079.

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This thesis is concerned with multi-objective sequential decision making (MOSDM). The motivation is twofold. On the one hand, many decision problems in the domains of e.g., robotics, scheduling or games, involve the optimization of sequences of decisions. On the other hand, many real-world applications are most naturally formulated in terms of multi-objective optimization (MOO). The proposed approach extends the well-known Monte-Carlo tree search (MCTS) framework to the MOO setting, with the goal of discovering several optimal sequences of decisions through growing a single search tree. The main challenge is to propose a new reward, able to guide the exploration of the tree although the MOO setting does not enforce a total order among solutions. The main contribution of the thesis is to propose and experimentally study two such rewards, inspired from the MOO literature and assessing a solution with respect to the archive of previous solutions (Pareto archive): the hypervolume indicator and the Pareto dominance reward. The study shows the complementarity of these two criteria. The hypervolume indicator suffers from its known computational complexity; however the proposed extension thereof provides fine-grained information about the quality of solutions with respect to the current archive. Quite the contrary, the Pareto-dominance reward is linear but it provides increasingly rare information. Proofs of principle of the approach are given on artificial problems and challenges, and confirm the merits of the approach. In particular, MOMCTS is able to discover policies lying in non-convex regions of the Pareto front, contrasting with the state of the art: existing Multi-Objective Reinforcement Learning algorithms are based on linear scalarization and thus fail to sample such non-convex regions. Finally MOMCTS honorably competes with the state of the art on the 2013 MOPTSP competition.
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Books on the topic "Multi-Objective"

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Mandal, Jyotsna K., Somnath Mukhopadhyay, and Paramartha Dutta, eds. Multi-Objective Optimization. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1.

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C, Tan K., Goh Chi-Keong, Ong Yew Soon, and SpringerLink (Online service), eds. Multi-Objective Memetic Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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Pukkala, Timo, ed. Multi-objective Forest Planning. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-015-9906-1.

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Goh, Chi-Keong, Yew-Soon Ong, and Kay Chen Tan, eds. Multi-Objective Memetic Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-88051-6.

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Lobato, Fran Sérgio, and Valder Steffen. Multi-Objective Optimization Problems. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58565-9.

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Jin, Yaochu, ed. Multi-Objective Machine Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-33019-4.

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Dehuri, Satchidananda, Alok Kumar Jagadev, and Mrutyunjaya Panda, eds. Multi-objective Swarm Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46309-3.

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Jin, Yaochu, ed. Multi-Objective Machine Learning. Berlin/Heidelberg: Springer-Verlag, 2006. http://dx.doi.org/10.1007/11399346.

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1966-, Jin Yaochu, ed. Multi-objective machine learning. Berlin: Springer, 2006.

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Dey, Nilanjan, ed. Applied Multi-objective Optimization. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0353-1.

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Book chapters on the topic "Multi-Objective"

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Seada, Haitham, and Kalyanmoy Deb. "Non-dominated Sorting Based Multi/Many-Objective Optimization: Two Decades of Research and Application." In Multi-Objective Optimization, 1–24. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_1.

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Bhunia, Asoke Kumar, Amiya Biswas, and Ali Akbar Shaikh. "Extended Nondominated Sorting Genetic Algorithm (ENSGA-II) for Multi-Objective Optimization Problem in Interval Environment." In Multi-Objective Optimization, 215–41. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_10.

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Das, Asit Kumar, and Sunanda Das. "A Comparative Study on Different Versions of Multi-Objective Genetic Algorithm for Simultaneous Gene Selection and Sample Categorization." In Multi-Objective Optimization, 243–67. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_11.

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Datta, Niladri Sekhar, Himadri Sekhar Dutta, Koushik Majumder, Sumana Chatterjee, and Najir Abdul Wasim. "A Survey on the Application of Multi-Objective Optimization Methods in Image Segmentation." In Multi-Objective Optimization, 269–78. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_12.

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Das, Asit Kumar, and Soumen Kumar Pati. "Bi-objective Genetic Algorithm with Rough Set Theory for Important Gene Selection in Disease Diagnosis." In Multi-Objective Optimization, 279–98. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_13.

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Das, Amit Kumar, Debasish Das, and Dilip Kumar Pratihar. "Multi-Objective Optimization and Cluster-Wise Regression Analysis to Establish Input–Output Relationships of a Process." In Multi-Objective Optimization, 299–318. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_14.

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Majumder, Saibal, Samarjit Kar, and Tandra Pal. "Mean-Entropy Model of Uncertain Portfolio Selection Problem." In Multi-Objective Optimization, 25–54. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_2.

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Mukhopadhyay, Anirban. "Incorporating Gene Ontology Information in Gene Expression Data Clustering Using Multiobjective Evolutionary Optimization: Application in Yeast Cell Cycle Data." In Multi-Objective Optimization, 55–78. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_3.

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Pal, Bijay Baran. "Interval-Valued Goal Programming Method to Solve Patrol Manpower Planning Problem for Road Traffic Management Using Genetic Algorithm." In Multi-Objective Optimization, 79–113. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_4.

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Gunasekara, R. Chulaka, Chilukuri K. Mohan, and Kishan Mehrotra. "Multi-objective Optimization to Improve Robustness in Networks." In Multi-Objective Optimization, 115–39. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_5.

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Conference papers on the topic "Multi-Objective"

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Zheng, Yong, and David (Xuejun) Wang. "Multi-Objective Recommendations." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3470788.

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Shi, Chuan, Xiangnan Kong, Philip S. Yu, and Bai Wang. "Multi-Objective Multi-Label Classification." In Proceedings of the 2012 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2012. http://dx.doi.org/10.1137/1.9781611972825.31.

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Delle Fave, F. M., S. Canu, L. Iocchi, D. Nardi, and V. A. Ziparo. "Multi-objective multi-robot surveillance." In 2009 4th International Conference on Autonomous Robots and Agents. IEEE, 2009. http://dx.doi.org/10.1109/icara.2000.4804005.

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Zhang, Song, Hongfeng Wang, Di Yang, and Min Huang. "Hybrid multi-objective genetic algorithm for multi-objective optimization problems." In 2015 27th Chinese Control and Decision Conference (CCDC). IEEE, 2015. http://dx.doi.org/10.1109/ccdc.2015.7162243.

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Belaiche, Leyla, Laid Kahloul, Maroua Grid, Nedjma Abidallah, and Saber Benharzallah. "Parallel Multi-Objective Evolutionary Algorithm for Constrained Multi-Objective Optimization." In 2023 24th International Arab Conference on Information Technology (ACIT). IEEE, 2023. http://dx.doi.org/10.1109/acit58888.2023.10453727.

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Hedayatzadeh, Ramin, Bahareh Hasanizadeh, Reza Akbari, and Koorush Ziarati. "A multi-objective Artificial Bee Colony for optimizing multi-objective problems." In 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icacte.2010.5579761.

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Xiaoming, Wang, Yan Jubin, Huang Yan, Chen Hanlin, Zhang Xuexia, Zang Tianlei, and Yu Zixuan. "Multi-objective transmission network planning based on multi-objective optimization algorithms." In 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2017. http://dx.doi.org/10.1109/ei2.2017.8245710.

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Wei, Xin, and Shigeru Fujimura. "Multi-Objective Quantum Evolutionary Algorithm for Discrete Multi-Objective Combinational Problem." In 2010 International Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2010. http://dx.doi.org/10.1109/taai.2010.18.

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Peerlinck, Amy, and John Sheppard. "Multi-Objective Factored Evolutionary Optimization and the Multi-Objective Knapsack Problem." In 2022 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2022. http://dx.doi.org/10.1109/cec55065.2022.9870377.

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Nobahari, Hadi, and Ariyan Bighashdel. "MOCSA: A Multi-Objective Crow Search Algorithm for Multi-Objective optimization." In 2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC). IEEE, 2017. http://dx.doi.org/10.1109/csiec.2017.7940171.

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Reports on the topic "Multi-Objective"

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Costley, D., Luis De Jesús Díaz,, Sarah McComas, Christopher Simpson, James Johnson, and Mihan McKenna. Multi-objective source scaling experiment. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/40824.

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The U.S. Army Engineer Research and Development Center (ERDC) performed an experiment at a site near Vicksburg, MS, during May 2014. Explosive charges were detonated, and the shock and acoustic waves were detected with pressure and infrasound sensors stationed at various distances from the source, i.e., from 3 m to 14.5 km. One objective of the experiment was to investigate the evolution of the shock wave produced by the explosion to the acoustic wavefront detected several kilometers from the detonation site. Another objective was to compare the effectiveness of different wind filter strategies. Toward this end, several sensors were deployed near each other, approximately 8 km from the site of the explosion. These sensors used different types of wind filters, including the different lengths of porous hoses, a bag of rocks, a foam pillow, and no filter. In addition, seismic and acoustic waves produced by the explosions were recorded with seismometers located at various distances from the source. The suitability of these sensors for measuring low-frequency acoustic waves was investigated.
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Raji, David. Applied Multi-Objective Modelling & Optimization. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1888185.

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Kuprowicz, Nicholas J. The Integrated Multi-Objective Multi-Disciplinary Jet Engine Design Optimization Program. Fort Belvoir, VA: Defense Technical Information Center, January 1999. http://dx.doi.org/10.21236/ada372032.

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Waddell, Lucas, John Gauthier, Matthew Hoffman, Denise Padilla, Stephen Henry, Alexander Dessanti, and Adam Pierson. Estimating the Adequacy of a Multi-Objective Optimization . Office of Scientific and Technical Information (OSTI), November 2021. http://dx.doi.org/10.2172/1833178.

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Zvonek, J., and A. Gillette. MULTI-OBJECTIVE ADAPTIVE MESH REFINEMENT USING REINFORCEMENT LEARNING. Office of Scientific and Technical Information (OSTI), June 2023. http://dx.doi.org/10.2172/1989992.

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Green, Andre. Random Forest vs. Mahalanobis Ensemble and Multi-Objective LDA. Office of Scientific and Technical Information (OSTI), August 2021. http://dx.doi.org/10.2172/1818082.

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Sauser, Brian J., and Jose E. Ramirez-Marquez. Multi-Objective Optimization of System Capability Satisficing in Defense Acquisition. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada589350.

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Richie, David A., James A. Ross, Song J. Park, and Dale R. Shires. A Monte Carlo Method for Multi-Objective Correlated Geometric Optimization. Fort Belvoir, VA: Defense Technical Information Center, May 2014. http://dx.doi.org/10.21236/ada603830.

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9

Nenoff, Tina M., Sarah E. Moore, Sera Mirchandani, Vasiliki Karanikola, Robert G. Arnold, and Eduardo Saez. Multi-objective Optimization of Solar-driven Hollow-fiber Membrane Distillation Systems. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1395756.

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10

Tumer, Kagan. Evolving Robust and Reconfigurable Multi-objective Controllers for Advanced Power Systems. Office of Scientific and Technical Information (OSTI), April 2018. http://dx.doi.org/10.2172/1436584.

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