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Статті в журналах з теми "METAHEURISTIC OPTIMIZATION TECHNIQUES"
Rahman, Md Ashikur, Rajalingam Sokkalingam, Mahmod Othman, Kallol Biswas, Lazim Abdullah, and Evizal Abdul Kadir. "Nature-Inspired Metaheuristic Techniques for Combinatorial Optimization Problems: Overview and Recent Advances." Mathematics 9, no. 20 (October 19, 2021): 2633. http://dx.doi.org/10.3390/math9202633.
Повний текст джерелаFeitosa Neto, Antonino, Anne Canuto, and João Xavier-Junior. "Hybrid Metaheuristics to the Automatic Selection of Features and Members of Classifier Ensembles." Information 9, no. 11 (October 26, 2018): 268. http://dx.doi.org/10.3390/info9110268.
Повний текст джерелаMisevičius, Alfonsas, Vytautas Bukšnaitis, and Jonas Blonskis. "Kombinatorinis optmizavimas ir metaeuristiniai metodai: teoriniai aspektai." Informacijos mokslai 42, no. 43 (January 1, 2008): 213–19. http://dx.doi.org/10.15388/im.2008.0.3417.
Повний текст джерелаSahoo, Rashmi Rekha, and Mitrabinda Ray. "Metaheuristic Techniques for Test Case Generation." Journal of Information Technology Research 11, no. 1 (January 2018): 158–71. http://dx.doi.org/10.4018/jitr.2018010110.
Повний текст джерелаFunes Lora, Miguel Angel, Edgar Alfredo Portilla-Flores, Raul Rivera Blas, Emmanuel Alejandro Merchán Cruz, and Manuel Faraón Carbajal Romero. "Metaheuristic techniques comparison to optimize robotic end-effector behavior and its workspace." International Journal of Advanced Robotic Systems 15, no. 5 (September 1, 2018): 172988141880113. http://dx.doi.org/10.1177/1729881418801132.
Повний текст джерелаRadhika, Sajja, and Aparna Chaparala. "Optimization using evolutionary metaheuristic techniques: a brief review." Brazilian Journal of Operations & Production Management 15, no. 1 (May 10, 2018): 44–53. http://dx.doi.org/10.14488/bjopm.2018.v15.n1.a17.
Повний текст джерелаAugusto, Adriano, Marlon Dumas, Marcello La Rosa, Sander J. J. Leemans, and Seppe K. L. M. vanden Broucke. "Optimization framework for DFG-based automated process discovery approaches." Software and Systems Modeling 20, no. 4 (February 27, 2021): 1245–70. http://dx.doi.org/10.1007/s10270-020-00846-x.
Повний текст джерелаNavarro-Acosta, Jesús Alejandro, Irma D. García-Calvillo, Vanesa Avalos-Gaytán, and Edgar O. Reséndiz-Flores. "Metaheuristics and Support Vector Data Description for Fault Detection in Industrial Processes." Applied Sciences 10, no. 24 (December 21, 2020): 9145. http://dx.doi.org/10.3390/app10249145.
Повний текст джерелаFidanova, Stefka Stoyanova, and Olympia Nikolaeva Roeva. "Metaheuristic Techniques for Optimization of anE. ColiCultivation Model." Biotechnology & Biotechnological Equipment 27, no. 3 (January 2013): 3870–76. http://dx.doi.org/10.5504/bbeq.2012.0136.
Повний текст джерелаTahami, Hesamoddin, and Hengameh Fakhravar. "A Literature Review on Combining Heuristics and Exact Algorithms in Combinatorial Optimization." European Journal of Information Technologies and Computer Science 2, no. 2 (April 29, 2022): 6–12. http://dx.doi.org/10.24018/compute.2022.2.2.50.
Повний текст джерелаДисертації з теми "METAHEURISTIC OPTIMIZATION TECHNIQUES"
Franz, Wayne. "Multi-population PSO-GA hybrid techniques: integration, topologies, and parallel composition." Springer, 2013. http://hdl.handle.net/1993/23842.
Повний текст джерелаMantilla, Gaviria Iván Antonio. "New Strategies to Improve Multilateration Systems in the Air Traffic Control." Doctoral thesis, Editorial Universitat Politècnica de València, 2013. http://hdl.handle.net/10251/29688.
Повний текст джерелаMantilla Gaviria, IA. (2013). New Strategies to Improve Multilateration Systems in the Air Traffic Control [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/29688
Alfresco
Diaz, Leiva Juan Esteban. "Simulation-based optimization for production planning : integrating meta-heuristics, simulation and exact techniques to address the uncertainty and complexity of manufacturing systems." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/simulationbased-optimization-for-production-planning-integrating-metaheuristics-simulation-and-exact-techniques-to-address-the-uncertainty-and-complexity-of-manufacturing-systems(9ef8cb33-99ba-4eb7-aa06-67c9271a50d0).html.
Повний текст джерелаJaber, Ahmed. "Hybrid Algorithm for Multi-objective Mixed-integer Non-convex Mechanical Design Optimization Problems." Thesis, Troyes, 2021. http://www.theses.fr/2021TROY0034.
Повний текст джерелаMulti-objective mixed-integer non-convex non-linear constrained optimization problems that appears in several fields especially in mechanical applications. This thesis aims to develop a new method to solve such problems. Our proposal is a hybridization of the Multi-Criteria Branch-and-Bound (MCBB) algorithm with the Non-dominated Sorting Genetic Algorithm 2 (NSGAII). The proposed approach is furthermore enhanced by new branching strategies designed for MCBB. The constraints are handled using a new proposed constraint handling technique for evolutionary algorithms. Numerical experiments based on statistical assessment are done in this thesis to examine the performance of the new proposed approach. Results show the competitive performance of our algorithm among NSGAII. We propose two applications of our proposed approach: "Search Feasibility" and "Seek Optimality" applications. Both are applied on a real-world state of art 3 stages reducer problem which is formulated in this thesis to a bi-objective problem to meet the requirement of ISO standards on calculation of load capacity of gears
Benaichouche, Ahmed Nasreddine. "Conception de métaheuristiques d'optimisation pour la segmentation d'images : application aux images IRM du cerveau et aux images de tomographie par émission de positons." Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1106/document.
Повний текст джерелаImage segmentation is the process of partitioning a digital image into homogeneous non-overlapped regions with respect to some characteristics, such as gray value, motion, texture, etc. It is used in various applications like medical imaging, objects detection, biometric system, remote sensing, robot navigation, video surveillance, etc. The success of the machine vision system depends heavily on its performance, because characteristics and decisions are extracted and taken from its result. The first image segmentation algorithms were introduced in the 70's. Since then, various techniques and methods were experimented to improve the results. Nevertheless, up till now, no method produces a perfect result for a wide variety of images. Metaheuristics are a high level procedure designed to solve hard optimization problems. These problems are in general characterized by their incomplete, uncertain or noised data, or faced to low computing capacity. Metaheuristics have been extremely successful in a wide variety of fields and demonstrate significant results. This is due to the fact that they can applied to solve any problem which can be formulated as an optimization problem. These methods are, mainly, inspired from physics (simulated annealing), biology (evolutionary algorithms), or ethology (particle swarm optimization, ant colony optimization).In recent years, metaheuristics are starting to be exploited to solve segmentation problems with varying degrees of success and allow to consider the problem with different perspectives. Bearing this in mind, we propose in this work three segmentation and post-segmentation approaches based on mono or multiobjective optimization metaheuristics. The proposed methods were evaluated on databases containing synthetic images, simulated MRI images, real MRI images and PET images. The obtained results show the efficiency of the proposed ideas
Rahmani, Younes. "The Multi-product Location-Routing Problem with Pickup and Delivery." Thesis, Université de Lorraine, 2015. http://www.theses.fr/2015LORR0331/document.
Повний текст джерелаIn the framework of Location-Routing Problem (LRP), the main idea is to combine strategic decisions related to the choice of processing centers with tactical and operational decisions related to the allocation of customers to selected processing centers and computing the associated routes. This thesis proposes a new location-routing model to solve problems which are coming from logistics networks, that became nowadays increasingly complex due to the need of resources sharing, in order to integrate the constraints of sustainable development and fuels price, which is increasing irreversibly. More precisely, three aspects have been integrated to generalize the classical LRP models already existed in the literature: 1) pickup and delivery aspect, 2) multi-product aspect, and 3) the possibility to use the processing centers as intermediate facilities in routes. We studied two logistics schemes gives us two new location-routing models: (i) MPLRP-PD (Multi-product LRP with pickup and delivery), which can be viewed as an extension of the vehicle routing problem with pick-up and delivery, including a tactical decision related to the location of processing centers (node with pick-up and delivery), and (ii) 2E-MPLRP-PD (Two-echelon multi-product LRP with pickup and delivery), which is a generalization of the two-echelon LRP. Both models were formalized by mixed integer linear programming (MIP). Solving techniques, based on heuristic methods, clustering approach and meta-heuristic techniques have been proposed to solve the MPLRP-PD and the 2E-MPLRP-PD. The benchmarks from the literature were generalized to test and to validate the proposed algorithms
Ayachi, Hajjem Imen. "Techniques avancées d'optimisation pour la résolution du problème de stockage de conteneurs dans un port." Thesis, Ecole centrale de Lille, 2012. http://www.theses.fr/2012ECLI0003/document.
Повний текст джерелаThe loading and unloading of containers and their temporary storage in the container terminal are the most important and complex operation in seaport terminals. It is highly inter-related with the routing of yard crane and truck and their costs increased significantly especially without an efficient terminal management. To improve this process, an efficiency decision for the container storage space allocation must be taken.In this thesis, we studied the container storage problem (CSP). It falls into the category of NP hard and NP complete problems. CSP consists on finding the most suitable storage location for incoming containers that minimizes rehandling operations of containers during their transfer to the ship, truck or train. In fact, the wait time of customer trucks, the transfer time of yard crane and the Ship turnaround time are advantageously reduced.Generally, this problem is studied considering a single container type. However, this does not stand the problem under its real-life statement as there are multiple container types that should be considered, (refrigerated, open side, empty, dry, open top and tank). Often, containers arrive at the port dynamically over time and have an uncertain departure date (ship delayed, a ship down, delayed arrival of customer trucks…). Indeed, CSP must be studied in dynamic aspectThe objective of this thesis is to study Static CSP for a single and various container type and dynamic CSP for ONE and several container types and to propose solutions for each of them. Genetic algorithm and Harmony Search algorithm are used to solve these problems and we compare the results of each approach with the LIFO algorithm
El, Dor Abbas. "Perfectionnement des algorithmes d'optimisation par essaim particulaire : applications en segmentation d'images et en électronique." Phd thesis, Université Paris-Est, 2012. http://tel.archives-ouvertes.fr/tel-00788961.
Повний текст джерелаLepagnot, Julien. "Conception de métaheuristiques pour l'optimisation dynamique : application à l'analyse de séquences d'images IRM." Phd thesis, Université Paris-Est, 2011. http://tel.archives-ouvertes.fr/tel-00674754.
Повний текст джерелаCooren, Yann. "Perfectionnement d'un algorithme adaptatif d'Optimisation par Essaim Particulaire : application en génie médical et en électronique." Phd thesis, Université Paris-Est, 2008. http://tel.archives-ouvertes.fr/tel-00462106.
Повний текст джерелаКниги з теми "METAHEURISTIC OPTIMIZATION TECHNIQUES"
Ponce-Ortega, José María, and Luis Germán Hernández-Pérez. Optimization of Process Flowsheets through Metaheuristic Techniques. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-91722-1.
Повний текст джерелаPonce-Ortega, José María, and Luis Germán Hernández-Pérez. Optimization of Process Flowsheets through Metaheuristic Techniques. Springer, 2018.
Знайти повний текст джерелаPonce-Ortega, José María, and Luis Germán Hernández-Pérez. Optimization of Process Flowsheets through Metaheuristic Techniques. Springer, 2019.
Знайти повний текст джерелаSwamy, M. N. S., and Ke-Lin Du. Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature. Birkhauser Verlag, 2016.
Знайти повний текст джерелаSwamy, M. N. S., and Ke-Lin Du. Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature. Birkhäuser, 2018.
Знайти повний текст джерелаSwamy, M. N. S., and Ke-Lin Du. Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature. Springer, 2016.
Знайти повний текст джерелаЧастини книг з теми "METAHEURISTIC OPTIMIZATION TECHNIQUES"
Ponce-Ortega, José María, and Luis Germán Hernández-Pérez. "Metaheuristic Optimization Programs." In Optimization of Process Flowsheets through Metaheuristic Techniques, 27–51. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91722-1_3.
Повний текст джерелаRao, R. V., and S. Patel. "Optimization of Robot Path Planning Using Advanced Optimization Techniques." In Metaheuristic Algorithms in Industry 4.0, 83–126. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003143505-5.
Повний текст джерелаAbdel-Basset, Mohamed, Ripon K. Chakrabortty, and Reda Mohamed. "Metaheuristic Algorithms for Healthcare." In Application of Advanced Optimization Techniques for Healthcare Analytics, 25–36. New York: CRC Press, 2023. http://dx.doi.org/10.1201/9781003325574-2.
Повний текст джерелаDeroussi, Laurent, Nathalie Grangeon, and Sylvie Norre. "Optimization of Logistics Systems Using Metaheuristic-Based Hybridization Techniques." In Metaheuristics, 381–405. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45403-0_14.
Повний текст джерелаPonce-Ortega, José María, and Luis Germán Hernández-Pérez. "Optimization of Industrial Process 1." In Optimization of Process Flowsheets through Metaheuristic Techniques, 79–90. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91722-1_6.
Повний текст джерелаPonce-Ortega, José María, and Luis Germán Hernández-Pérez. "Optimization of Industrial Process 2." In Optimization of Process Flowsheets through Metaheuristic Techniques, 91–97. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91722-1_7.
Повний текст джерелаHe, Xing-Shi, Qin-Wei Fan, Mehmet Karamanoglu, and Xin-She Yang. "Comparison of Constraint-Handling Techniques for Metaheuristic Optimization." In Lecture Notes in Computer Science, 357–66. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22744-9_28.
Повний текст джерелаKarnik, Niharika, and Pankaj Dhatrak. "Optimization Techniques and Algorithms for Dental Implants – A Comprehensive Review." In Metaheuristic Algorithms in Industry 4.0, 261–82. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003143505-12.
Повний текст джерелаPonce-Ortega, José María, and Luis Germán Hernández-Pérez. "Interlinking Between Process Simulators and Optimization Programs." In Optimization of Process Flowsheets through Metaheuristic Techniques, 53–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91722-1_4.
Повний текст джерелаAbdel-Basset, Mohamed, Ripon K. Chakrabortty, and Reda Mohamed. "Contribution of Metaheuristic Approaches for Feature Selection Techniques." In Application of Advanced Optimization Techniques for Healthcare Analytics, 103–24. New York: CRC Press, 2023. http://dx.doi.org/10.1201/9781003325574-6.
Повний текст джерелаТези доповідей конференцій з теми "METAHEURISTIC OPTIMIZATION TECHNIQUES"
Serrano Elena, Antonio. "METAHEURISTIC ANALYSIS IN REVERSE LOGISTICS OF WASTE." In CIT2016. Congreso de Ingeniería del Transporte. Valencia: Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/cit2016.2016.3163.
Повний текст джерелаLoubna, Kritele, El Beqal Asmae, Zorkani Izeddine, and Benhala Bachir. "Metaheuristic-based Optimization Techniques for Optimal Analog Filter Sizing." In 2018 International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS). IEEE, 2018. http://dx.doi.org/10.1109/icecocs.2018.8610525.
Повний текст джерелаSingh, Rajendra Bahadur, Anurag Singh Baghel, and Ayush Agarwal. "A review on VLSI floorplanning optimization using metaheuristic algorithms." In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE, 2016. http://dx.doi.org/10.1109/iceeot.2016.7755508.
Повний текст джерелаKoudela, Pavel, and Juraj Chalmovský. "Automation of calibration process adopting metaheuristic optimization method." In The 13th international scientific conference “Modern Building Materials, Structures and Techniques”. Vilnius Gediminas Technical University, 2019. http://dx.doi.org/10.3846/mbmst.2019.142.
Повний текст джерелаGunal, Ozen, Mustafa Akpinar, and Kevser Ovaz Akpinar. "Performance Evaluation of Metaheuristic Optimization Techniques in Insulation Problem." In 2022 8th International Conference on Information Technology Trends (ITT). IEEE, 2022. http://dx.doi.org/10.1109/itt56123.2022.9863959.
Повний текст джерелаLeite da Silva, Armando M., Joao Guilherme de C. Costa, Kascilene G. Machado, and Carlos H. V. Moraes. "Spare transformers optimization using Monte Carlo simulation and metaheuristic techniques." In 2015 18th International Conference on Intelligent System Application to Power Systems (ISAP). IEEE, 2015. http://dx.doi.org/10.1109/isap.2015.7325578.
Повний текст джерелаSarapan, Waranyu, Nonthakorn Boonrakchat, Ashok Paudel, Terapong Boonraksa, Promphak Boonraksa, and Boonruang Marungsri. "Optimal Energy Management in Smart House using Metaheuristic Optimization Techniques." In 2022 International Conference on Power, Energy and Innovations (ICPEI). IEEE, 2022. http://dx.doi.org/10.1109/icpei55293.2022.9986889.
Повний текст джерелаWadi, Mohammed, Wisam Elmasry, Abdulfetah Shobole, Mehmet Rida Tur, Ramazan Bayindir, and Hossein Shahinzadeh. "Wind Energy Potential Approximation with Various Metaheuristic Optimization Techniques Deployment." In 2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS). IEEE, 2021. http://dx.doi.org/10.1109/icspis54653.2021.9729389.
Повний текст джерелаBoualem, Nasri, Mostefai Lotfi, Guessoum Abderrezak, Benikhlef Abdelhak, Zemalache Meguenni Kadda, and Tahar Mohammed. "Metaheuristic optimization techniques of a sliding-mode controller gains applied to quadrotor." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGY AND MULTIDISCIPLINE (ICATAM) 2021: “Advanced Technology and Multidisciplinary Prospective Towards Bright Future” Faculty of Advanced Technology and Multidiscipline. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0118909.
Повний текст джерелаKavaliauskas, Donatas, and Leonidas Sakalauskas. "Conceptual model of productivity bot for smart construction planning." In The 13th international scientific conference “Modern Building Materials, Structures and Techniques”. Vilnius Gediminas Technical University, 2019. http://dx.doi.org/10.3846/mbmst.2019.003.
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