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Gotowe bibliografie tematyczne / Constrained Multi-Objective Optimization

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Gotowa bibliografia na temat „Constrained Multi-Objective Optimization”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Constrained Multi-Objective Optimization"

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Adekoya, Adekunle Rotimi, and Mardé Helbig. "Decision-Maker’s Preference-Driven Dynamic Multi-Objective Optimization." Algorithms 16, no. 11 (2023): 504. http://dx.doi.org/10.3390/a16110504.

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DMOOP are optimization problems where elements of the problems, such as the objective functions and/or constraints, change with time. These problems are characterized by two or more objective functions, where at least two objective functions are in conflict with one another. When solving real-world problems, the incorporation of human DM’ preferences or expert knowledge into the optimization process and thereby restricting the search to a specific region of the POF may result in more preferred or suitable solutions. This study proposes approaches that enable DM to influence the search process

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Yang, Yufei, and Changsheng Zhang. "A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems." Biomimetics 8, no. 2 (2023): 136. http://dx.doi.org/10.3390/biomimetics8020136.

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Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Plant Algorithm. The algorithm employs the ϵ-constraint handling method, with the ϵ value adjusted according to different stages to meet the algorithm’s requirements. To improve the search efficiency, a cross-pollination is designed based on the trapping mechanism and pollination behavior of carnivorou

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Zhang, Kai, Siyuan Zhao, Hui Zeng, and Junming Chen. "Two-Stage Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization." Mathematics 13, no. 3 (2025): 470. https://doi.org/10.3390/math13030470.

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The core issue in handling constrained multi-objective optimization problems (CMOP) is how to maintain a balance between objectives and constraints. However, existing constrained multi-objective evolutionary algorithms (CMOEAs) often fail to achieve the desired performance when confronted with complex feasible regions. Building upon this theoretical foundation, a two-stage archive-based constrained multi-objective evolutionary algorithm (CMOEA-TA) based on genetic algorithms(GA) is proposed. In CMOEA-TA, First stage: The archive appropriately relaxes constraints based on the proportion of feas

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Wang, Qiuzhen, Zhibing Liang, Juan Zou, et al. "Dynamic Constrained Boundary Method for Constrained Multi-Objective Optimization." Mathematics 10, no. 23 (2022): 4459. http://dx.doi.org/10.3390/math10234459.

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When solving complex constrained problems, how to efficiently utilize promising infeasible solutions is an essential issue because these promising infeasible solutions can significantly improve the diversity of algorithms. However, most existing constrained multi-objective evolutionary algorithms (CMOEAs) do not fully exploit these promising infeasible solutions. In order to solve this problem, a constrained multi-objective optimization evolutionary algorithm based on the dynamic constraint boundary method is proposed (CDCBM). The proposed algorithm continuously searches for promising infeasib

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Zuo, Mingcheng, and Yuan Xue. "Population Feasibility State Guided Autonomous Constrained Multi-Objective Evolutionary Optimization." Mathematics 12, no. 6 (2024): 913. http://dx.doi.org/10.3390/math12060913.

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Many practical problems can be classified as constrained multi-objective optimization problems. Although various methods have been proposed for solving constrained multi-objective optimization problems, there is still a lack of research considering the integration of multiple constraint handling techniques. Given this, this paper combines the objective and constraint separation method with the multi-operator method, proposing a population feasibility state guided autonomous constrained evolutionary optimization method. This method first defines the feasibility state of the population based on

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Zhao, Tian Yang, Guo Bing Qiu, Ying Zhi Li, Wen Xia Liu, and Jian Hua Zhang. "Constrained Multi-Objective Differential Evolution for Security Constrained Economic/Environmental Dispatch." Applied Mechanics and Materials 291-294 (February 2013): 817–22. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.817.

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A newly constrained multi-objective differential evolution optimization technique (CMODE) for security constrained economic/environmental dispatch (EED) was proposed. The proposed CMODE evolved a constrained multi-objective version of differential evolution (DE) by employing the traditional multi-objective differential evolution (DEMO) and constrain handle technique to balance the search between feasible region and infeasible region. The proposed CMODE method had been applied to solve the security constrained EED problem. Experiments had been carried on a standard test system. The results demo

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Ramirez-Atencia, Cristian, and David Camacho. "Constrained multi-objective optimization for multi-UAV planning." Journal of Ambient Intelligence and Humanized Computing 10, no. 6 (2018): 2467–84. http://dx.doi.org/10.1007/s12652-018-0930-0.

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Ramírez, Atencia Cristian, and David Camacho. "Constrained multi-objective optimization for multi-UAV planning." Journal of Ambient Intelligence and Humanized Computing 10 (June 1, 2019): 2467–84. https://doi.org/10.1007/s12652-018-0930-0.

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Over the last decade, developments in unmanned aerial vehicles (UAVs) has greatly increased, and they are being used in many fields including surveillance, crisis management or automated mission planning. This last field implies the search of plans for missions with multiple tasks, UAVs and ground control stations; and the optimization of several objectives, including makespan, fuel consumption or cost, among others. In this work, this problem has been solved using a multi-objective evolutionary algorithm combined with a constraint satisfaction problem model, which is used in the fitness funct

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Hamdy, A., and A. A. Mohamed. "Greedy Binary Particle Swarm Optimization for multi-Objective Constrained Next Release Problem." International Journal of Machine Learning and Computing 9, no. 5 (2019): 561–68. http://dx.doi.org/10.18178/ijmlc.2019.9.5.840.

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Xie, Shumin, Zhenjia Zhu, and Hui Wang. "An Improved Coevolutionary Algorithm for Constrained Multi-Objective Optimization Problems." International Journal of Cognitive Informatics and Natural Intelligence 18, no. 1 (2024): 1–16. http://dx.doi.org/10.4018/ijcini.355766.

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Constrained multi-objective optimization problems are ubiquitous in engineering applications. In recent years, constrained multi-objective optimization algorithms based on the dual population coevolutionary framework have been widely studied due to their excellent performance. However, when facing optimization problems with complex constraints, the performance of existing algorithms still needs further improvement. This paper proposes an improved constrained multi-objective coevolutionary algorithm (iCMOCA). The algorithm mainly includes two populations: One population takes into account const

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Rozprawy doktorskie na temat "Constrained Multi-Objective Optimization"

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Hawe, Glenn. "Kriging methods for constrained multi-objective electromagnetic design optimization." Thesis, University of Southampton, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.444159.

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Lin, Maokai. "Multi-objective constrained optimization for decision making and optimization for system architectures." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/58188.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 171-174).<br>This thesis proposes new methods to solve three problems: 1) how to model and solve decision-making problems, 2) how to translate between a graphical representation of systems and a matrix representation of systems, and 3) how to cluster single and multiple Design Structure Matrices (DSM). To solve the first problem, the thesis provides an approach to model decisionmaking problems as multi-o

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Ebadi, Nasim. "Estimating Costs of Reducing Environmental Emissions From a Dairy Farm: Multi-objective epsilon-constraint Optimization Versus Single Objective Constrained Optimization." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99304.

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Agricultural production is an important source of environmental emissions. While water quality concerns related to animal agriculture have been studied extensively, air quality issues have become an increasing concern. Due to the transfer of nutrients between air, water, and soil, emissions to air can harm water quality. We conduct a multi-objective optimization analysis for a representative dairy farm with two different approaches: nonlinear programming (NLP) and ϵ-constraint optimization to evaluate trade-offs among reduction of multiple pollutants including nitrogen (N), phosphorus (P), gre

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Günther, Christian [Verfasser]. "On generalized-convex constrained multi-objective optimization and application in location theory / Christian Günther." Halle, 2018. http://d-nb.info/1175950602/34.

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Siegmund, Florian. "Dynamic Resampling for Preference-based Evolutionary Multi-objective Optimization of Stochastic Systems : Improving the efficiency of time-constrained optimization." Doctoral thesis, Högskolan i Skövde, Institutionen för ingenjörsvetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13088.

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In preference-based Evolutionary Multi-objective Optimization (EMO), the decision maker is looking for a diverse, but locally focused non-dominated front in a preferred area of the objective space, as close as possible to the true Pareto-front. Since solutions found outside the area of interest are considered less important or even irrelevant, the optimization can focus its efforts on the preferred area and find the solutions that the decision maker is looking for more quickly, i.e., with fewer simulation runs. This is particularly important if the available time for optimization is limited, a

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Fengler, Benedikt [Verfasser]. "Manufacturing-constrained multi-objective optimization of local patch reinforcements for discontinuous fiber reinforced composite parts / Benedikt Fengler." Karlsruhe : KIT-Bibliothek, 2019. http://d-nb.info/1176022628/34.

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Fengler, Benedikt [Verfasser]. "Manufacturing-constrained multi-objective optimization of local patch reinforcements for discontinuous fiber reinforced composite parts / Benedikt Fengler." Karlsruhe : KIT Scientific Publishing, 2021. http://d-nb.info/1229623698/34.

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Zini, Érico de Oliveira Costa. "Algoritmo genético especializado na resolução de problemas com variáveis contínuas e altamente restritos /." Ilha Solteira : [s.n.], 2009. http://hdl.handle.net/11449/87116.

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Resumo: Este trabalho apresenta uma metodologia composta de duas fases para resolver problemas de otimização com restrições usando uma estratégia multiobjetivo. Na primeira fase, o esforço concentra-se em encontrar, pelo menos, uma solução factível, descartando completamente a função objetivo. Na segunda fase, aborda-se o problema como biobjetivo, onde se busca a otimização da função objetivo original e maximizar o cumprimento das restrições. Na fase um propõe-se uma estratégia baseada na diminuição progressiva da tolerância de aceitação das restrições complexas para encontrar soluções factíve

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Zini, Érico de Oliveira Costa [UNESP]. "Algoritmo genético especializado na resolução de problemas com variáveis contínuas e altamente restritos." Universidade Estadual Paulista (UNESP), 2009. http://hdl.handle.net/11449/87116.

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Made available in DSpace on 2014-06-11T19:22:32Z (GMT). No. of bitstreams: 0 Previous issue date: 2009-02-20Bitstream added on 2014-06-13T19:28:05Z : No. of bitstreams: 1 zini_eoc_me_ilha.pdf: 1142984 bytes, checksum: 4ff93a7fe459a5a56e15da26b7a6dd45 (MD5)<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>Este trabalho apresenta uma metodologia composta de duas fases para resolver problemas de otimização com restrições usando uma estratégia multiobjetivo. Na primeira fase, o esforço concentra-se em encontrar, pelo menos, uma solução factível, descartando completamente

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Mrázková, Eva. "Approximations in Stochastic Optimization and Their Applications." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2010. http://www.nusl.cz/ntk/nusl-233932.

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Mnoho inženýrských úloh vede na optimalizační modely s~omezeními ve tvaru obyčejných (ODR) nebo parciálních (PDR) diferenciálních rovnic, přičemž jsou v praxi často některé parametry neurčité. V práci jsou uvažovány tři inženýrské problémy týkající se optimalizace vibrací a optimálního návrhu rozměrů nosníku. Neurčitost je v nich zahrnuta ve formě náhodného zatížení nebo náhodného Youngova modulu. Je zde ukázáno, že dvoustupňové stochastické programování nabízí slibný přístup k řešení úloh daného typu. Odpovídající matematické modely, zahrnující ODR nebo PDR omezení, neurčité parametry a více

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Książki na temat "Constrained Multi-Objective Optimization"

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Fengler, Benedikt. Manufacturing-constrained multi-objective optimization of local patch reinforcements for discontinuous fiber reinforced composite parts. KIT Scientific Publishing, 2021.

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Części książek na temat "Constrained Multi-Objective Optimization"

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Lourenço, Nuno, Ricardo Martins, and Nuno Horta. "Multi-objective Optimization Kernel." In Automatic Analog IC Sizing and Optimization Constrained with PVT Corners and Layout Effects. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42037-0_4.

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Mitra, Kishalay. "Chance Constrained Programming to Handle Uncertainty in Nonlinear Process Models." In Multi-Objective Optimization in Chemical Engineering. John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118341704.ch7.

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Koziel, Slawomir, and Anna Pietrenko-Dabrowska. "Constrained Modeling for Efficient Multi-objective Optimization." In Performance-Driven Surrogate Modeling of High-Frequency Structures. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38926-0_10.

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Gao, Yue-lin, and Min Qu. "Constrained Multi-objective Particle Swarm Optimization Algorithm." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31837-5_7.

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Ramu Naidu, Y., A. K. Ojha, and V. Susheela Devi. "Multi-objective Jaya Algorithm for Solving Constrained Multi-objective Optimization Problems." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31967-0_11.

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Sharma, Arun Kumar, Rituparna Datta, Maha Elarbi, Bishakh Bhattacharya, and Slim Bechikh. "Practical Applications in Constrained Evolutionary Multi-objective Optimization." In Recent Advances in Evolutionary Multi-objective Optimization. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42978-6_6.

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Deb, Kalyanmoy, Amrit Pratap, and T. Meyarivan. "Constrained Test Problems for Multi-objective Evolutionary Optimization." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44719-9_20.

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Feliot, Paul, Julien Bect, and Emmanuel Vazquez. "A Bayesian Approach to Constrained Multi-objective Optimization." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19084-6_24.

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Li, Yaohui, Yizhong Wu, Yuanmin Zhang, and Shuting Wang. "KMCGO: Kriging-Assisted Multi-objective Constrained Global Optimization." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21803-4_63.

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Fu, Jun, and Chi Zhang. "Multi-objective Dynamic Optimization of Path-Constrained Switched Systems." In Dynamic Optimization of Path-Constrained Switched Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-23428-6_5.

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Streszczenia konferencji na temat "Constrained Multi-Objective Optimization"

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Makropoulos, Athanasios, Dimitrios Gunopulos, Vana Kalogeraki, and Nikolaos Zygouras. "CONCERTO: Constrained Linear Multi-Objective Routing Path Optimization." In 2025 26th IEEE International Conference on Mobile Data Management (MDM). IEEE, 2025. https://doi.org/10.1109/mdm65600.2025.00051.

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Wu, Haofeng, Jinliang Ding, Qiqi Liu, and Yaochu Jin. "Model Management Agent for Expensive Constrained Multi-Objective Optimization." In 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS). IEEE, 2024. http://dx.doi.org/10.1109/docs63458.2024.10704384.

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Wang, Shunge, Yifeng Qiu, Wenji Li, et al. "Multi-stage Global and Local Cooperative Constrained Multi-objective Evolutionary Algorithm." In 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS). IEEE, 2024. http://dx.doi.org/10.1109/docs63458.2024.10704460.

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Zhao, Shulin, Xingxing Hao, Li Chen, and Yahui Feng. "Multi-Population Constrained Multi-Objective Evolutionary Algorithm Based on Knowledge Transfer." In 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS). IEEE, 2024. http://dx.doi.org/10.1109/docs63458.2024.10704519.

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Zhao, Shulin, Xingxing Hao, Li Chen, and Yongkang Qian. "Two-stage multi-population evolutionary algorithm for constrained multi-objective optimization." In 2024 IEEE 17th International Conference on Signal Processing (ICSP). IEEE, 2024. https://doi.org/10.1109/icsp62129.2024.10846109.

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García-Rojas, Néstor A., Miguel A. Jiménez-Domínguez, Saúl Zapotecas-Martínez, Raquel Díaz-Hernández, Leopoldo Altamirano-Robles, and Bilel Derbel. "Constrained Multi-Objective Optimization with dMOPSO: An Adaptive Penalty Approach." In 2025 IEEE Conference on Artificial Intelligence (CAI). IEEE, 2025. https://doi.org/10.1109/cai64502.2025.00223.

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Zhao, Lei. "ACMO-Diet: Accelerated Constrained Multi-Objective Optimization for Diet Recommendation." In 2025 IEEE 7th International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2025. https://doi.org/10.1109/cisce65916.2025.11065817.

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Ma, Siyang, and Jie Li. "A data-driven hybrid multi-objective optimization framework for pressure swing adsorption systems." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.123607.

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Pressure swing adsorption (PSA) is an energy-efficient technology for gas separation, while the multi-objective optimization of PSA is a challenging task. To tackle this, we propose a hybrid optimization framework, which integrates three steps. In the first step, we establish surrogate models for the constraints using Gaussian processes (GPs) and employ multi-objective Bayesian optimization to search for feasible points that satisfy the constraints. In the second step, we establish surrogate models for the objective function and constraints using GPs and utilize constrained multi-objective Bay

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Chen, Jie, Kai Zhang, Ni Wu, and Ling Zhang. "Two-Stage Dynamic Cooperative Evolution Algorithm for Constrained Multi-objective Optimization." In 2024 25th International Arab Conference on Information Technology (ACIT). IEEE, 2024. https://doi.org/10.1109/acit62805.2024.10877061.

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Wang, Zhaojun, Jiachun Huang, Wenji Li, et al. "Masked Genetic Operators with Causal Grouping for Constrained Multi-Objective Optimization." In 2025 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2025. https://doi.org/10.1109/cec65147.2025.11043011.

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Raporty organizacyjne na temat "Constrained Multi-Objective Optimization"

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Wenren, Yonghu, Joon Lim, Luke Allen, Robert Haehnel, and Ian Dettwiler. Helicopter rotor blade planform optimization using parametric design and multi-objective genetic algorithm. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/46261.

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In this paper, an automated framework is presented to perform helicopter rotor blade planform optimization. This framework contains three elements, Dakota, ParBlade, and RCAS. These elements are integrated into an environment control tool, Galaxy Simulation Builder, which is used to carry out the optimization. The main objective of this work is to conduct rotor performance design optimizations for forward flight and hover. The blade design variables manipulated by ParBlade are twist, sweep, and anhedral. The multi-objective genetic algorithm method is used in this study to search for the optim

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti

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An Optimization Model for Die Sets Allocation to Minimize Supply Chain Cost. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-5057.

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In this paper, a novel mixed-integer programming model is developed to optimally assign the die sets to candidate plants to minimize the total costs. The total costs include freight shipping stamped parts to assembly plants, die set movement, outsourcing, and utilization. Therefore, the objective function is weighted multi-criteria and it takes into consideration some of the key constraints in the real-world condition including “must-move die sets”. An optimization tool has been developed that takes several inputs and feeds them as the input to the mathematical model and generates the optimal

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