Relevant bibliographies by topics / METAHEURISTIC OPTIMIZATION TECHNIQUES

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'METAHEURISTIC OPTIMIZATION TECHNIQUES'

Author: Grafiati
Published: 11 September 2023
Last updated: 26 July 2025

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Journal articles on the topic "METAHEURISTIC OPTIMIZATION TECHNIQUES"

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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 (2021): 2633. http://dx.doi.org/10.3390/math9202633.

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Combinatorial optimization problems are often considered NP-hard problems in the field of decision science and the industrial revolution. As a successful transformation to tackle complex dimensional problems, metaheuristic algorithms have been implemented in a wide area of combinatorial optimization problems. Metaheuristic algorithms have been evolved and modified with respect to the problem nature since it was recommended for the first time. As there is a growing interest in incorporating necessary methods to develop metaheuristics, there is a need to rediscover the recent advancement of meta
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Feitosa 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 (2018): 268. http://dx.doi.org/10.3390/info9110268.

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Metaheuristic algorithms have been applied to a wide range of global optimization problems. Basically, these techniques can be applied to problems in which a good solution must be found, providing imperfect or incomplete knowledge about the optimal solution. However, the concept of combining metaheuristics in an efficient way has emerged recently, in a field called hybridization of metaheuristics or, simply, hybrid metaheuristics. As a result of this, hybrid metaheuristics can be successfully applied in different optimization problems. In this paper, two hybrid metaheuristics, MAMH (Multiagent
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Misevičius, Alfonsas, Vytautas Bukšnaitis, and Jonas Blonskis. "Kombinatorinis optmizavimas ir metaeuristiniai metodai: teoriniai aspektai." Informacijos mokslai 42, no. 43 (2008): 213–19. http://dx.doi.org/10.15388/im.2008.0.3417.

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Straipsnyje aptariami kombinatorinio optimizavimo ir intelektualių optimizavimo priemonių, t. y. metaeuristinių metodų (metaeuristikų), teoriniai aspektai. Apibūdinami kombinatorinio optimizavimo uždaviniai, jų savybės, specifika. Pagrindinis dėmesys skiriamas metaeuristinių optimizavimo metodų charakterizavimui būtent kombinatorinio optimizavimo kontekste. Trumpai formuluojami metaeuristinių metodų tikslai, bendrosios nuostatos, taip pat akcentuojamas šių metodų savitumas, modernumas.Išsamiau apžvelgiami skiriamieji metaeuristikų bruožai, aprašomos svarbesnės teorinės metaeuristinių metodų ai
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Preeti Lakhani. "Harnessing Metaheuristics for Superior Intrusion Detection: Deep Learning on Benchmark Datasets." Journal of Information Systems Engineering and Management 10, no. 34s (2025): 588–602. https://doi.org/10.52783/jisem.v10i34s.5853.

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While intrusion detection systems (IDS) are crucial to safeguarding network environments from evolving cyber threats, traditional methods often struggle to optimize detection capabilities and balance computational efficiency. To enhance the performance of IDS, this study integrates metaheuristic optimization techniques with deep learning algorithms. Combining various metaheuristics, including Genetic Algorithms, Particle Swarm Optimization, and Harris Hawks Optimization, with deep learning models, such as Graph Neural Networks, Recurrent Neural Networks, and Convolutional Neural Networks, is a
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Wang, Chia-Hung, Kun Hu, Xiaojing Wu, and Yufeng Ou. "Rethinking Metaheuristics: Unveiling the Myth of “Novelty” in Metaheuristic Algorithms." Mathematics 13, no. 13 (2025): 2158. https://doi.org/10.3390/math13132158.

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In recent decades, the rapid development of metaheuristic algorithms has outpaced theoretical understanding, with experimental evaluations often overshadowing rigorous analysis. While nature-inspired optimization methods show promise for various applications, their effectiveness is often limited by metaphor-driven design, structural biases, and a lack of sufficient theoretical foundation. This paper systematically examines the challenges in developing robust, generalizable optimization techniques, advocating for a paradigm shift toward modular, transparent frameworks. A comprehensive review of
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Sahoo, Rashmi Rekha, and Mitrabinda Ray. "Metaheuristic Techniques for Test Case Generation." Journal of Information Technology Research 11, no. 1 (2018): 158–71. http://dx.doi.org/10.4018/jitr.2018010110.

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The primary objective of software testing is to locate bugs as many as possible in software by using an optimum set of test cases. Optimum set of test cases are obtained by selection procedure which can be viewed as an optimization problem. So metaheuristic optimizing (searching) techniques have been immensely used to automate software testing task. The application of metaheuristic searching techniques in software testing is termed as Search Based Testing. Non-redundant, reliable and optimized test cases can be generated by the search based testing with less effort and time. This article prese
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Funes 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 (2018): 172988141880113. http://dx.doi.org/10.1177/1729881418801132.

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Many robots are dedicated to replicate trajectories recorded manually; the recorded trajectories may contain singularities, which occur when positions and/or orientations are not achievable by the robot. The optimization of those trajectories is a complex issue and classical optimization methods present a deficient performance when solving them. Metaheuristics are well-known methodologies for solving hard engineering problems. In this case, they are applied to obtain alternative trajectories that pass as closely as possible to the original one, reorienting the end-effector and displacing its p
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Nandal, Deepak, and Om Prakash Sangwan. "A Survey Report on Various Software Estimation Techniques and Practices." International Journal of Control Theory and Applications 9, no. 22 (2016): 75–83. https://doi.org/10.5281/zenodo.163521.

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Paper offers thoroughly examine of software and project analysis methods established in industry and literature, its skills and flaws The Software Estimation is very important task for completing the project successfully. A successful software project development not only relies on the product efficiency but also the accurate estimation The estimation in software development depends on various factors especially on cost and effort factors for which further AI(Artificial Intelligence) and Algorithmic models have been put into usage. The low accuracy and non- reliable structures of the algorithm
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Augusto, 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 (2021): 1245–70. http://dx.doi.org/10.1007/s10270-020-00846-x.

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AbstractThe problem of automatically discovering business process models from event logs has been intensely investigated in the past two decades, leading to a wide range of approaches that strike various trade-offs between accuracy, model complexity, and execution time. A few studies have suggested that the accuracy of automated process discovery approaches can be enhanced by means of metaheuristic optimization techniques. However, these studies have remained at the level of proposals without validation on real-life datasets or they have only considered one metaheuristic in isolation. This art
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Radhika, Sajja, and Aparna Chaparala. "Optimization using evolutionary metaheuristic techniques: a brief review." Brazilian Journal of Operations & Production Management 15, no. 1 (2018): 44–53. http://dx.doi.org/10.14488/bjopm.2018.v15.n1.a17.

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Optimization is necessary for finding appropriate solutions to a range of real life problems. Evolutionary-approach-based meta-heuristics have gained prominence in recent years for solving Multi Objective Optimization Problems (MOOP). Multi Objective Evolutionary Approaches (MOEA) has substantial success across a variety of real-world engineering applications. The present paper attempts to provide a general overview of a few selected algorithms, including genetic algorithms, ant colony optimization, particle swarm optimization, and simulated annealing techniques. Additionally, the review is ex
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Dissertations / Theses on the topic "METAHEURISTIC OPTIMIZATION TECHNIQUES"

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Franz, Wayne. "Multi-population PSO-GA hybrid techniques: integration, topologies, and parallel composition." Springer, 2013. http://hdl.handle.net/1993/23842.

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Recent work in metaheuristic algorithms has shown that solution quality may be improved by composing algorithms with orthogonal characteristics. In this thesis, I study multi-population particle swarm optimization (MPSO) and genetic algorithm (GA) hybrid strategies. I begin by investigating the behaviour of MPSO with crossover, mutation, swapping, and all three, and show that the latter is able to solve the most difficult benchmark functions. Because GAs converge slowly and MPSO provides a large degree of parallelism, I also develop several parallel hybrid algorithms. A composite approach exec
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Mantilla, 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.

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Develop new strategies to design and operate the multilateration systems, used for air traffic control operations, in a more efficient way. The design strategies are based on the utilization of metaheuristic optimization techniques and they are intended to found the optimal spatial distribution of the system ground stations, taking into account the most relevant system operation parameters. The strategies to operate the systems are based on the development of new positioning methods which allow solving the problems of uncertainty position and poor accuracy that the current systems can present.
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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.

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This doctoral thesis investigates the application of simulation-based optimization (SBO) as an alternative to conventional optimization techniques when the inherent uncertainty and complex features of real manufacturing systems need to be considered. Inspired by a real-world production planning setting, we provide a general formulation of the situation as an extended knapsack problem. We proceed by proposing a solution approach based on single and multi-objective SBO models, which use simulation to capture the uncertainty and complexity of the manufacturing system and employ meta-heuristic opt
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Jaber, Ahmed. "Hybrid Algorithm for Multi-objective Mixed-integer Non-convex Mechanical Design Optimization Problems." Thesis, Troyes, 2021. http://www.theses.fr/2021TROY0034.

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Les problèmes d'optimisation sous contraintes non linéaires non convexes multi-objectifs en variables mixtes (discrètes et continues) apparaissent dans de nombreux domaines de l’ingénierie et notamment dans les applications de conception en mécaniques. Cette thèse vise à développer une nouvelle méthode pour résoudre ces problèmes d’optimisation. Notre proposition est une hybridation de l'algorithme multicritère « Branch-and-Bound » (MCBB) avec l’algorithme évolutionnaire de type NSGAII. L'approche proposée est en outre renforcée par de nouvelles stratégies de branchement conçues pour l’algorit
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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.

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La segmentation d'image est le processus de partitionnement d'une image numérique en régions, non chevauchées, homogènes vis-à-vis de certaines caractéristiques, telles que le niveau de gris, la texture, le mouvement, etc. Elle a des applications dans plusieurs domaines comme l'imagerie médicale, la détection d'objets, la biométrie, l'imagerie par satellite, la navigation de robot, la vidéosurveillance, etc. Le processus de segmentation représente une étape cruciale dans les systèmes de vision par ordinateur, car les caractéristiques et décisions sont extraites et prises à partir de son résult
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Rahmani, Younes. "The Multi-product Location-Routing Problem with Pickup and Delivery." Thesis, Université de Lorraine, 2015. http://www.theses.fr/2015LORR0331/document.

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Dans les problèmes de localisation-routage classiques (LRP), il s'agit de combiner des décisions stratégiques liées aux choix des sites à ouvrir (centres de traitement) avec des décisions tactiques et opérationnelles liées à l'affectation des clients aux sites sélectionnés et a la confection des tournées associées. Cette thèse propose de nouveaux modèles de localisation-routage permettant de résoudre des problématiques issues de réseaux logistiques, devenus aujourd'hui de plus en plus complexes vu la nécessité de mutualisation de ressources pour intégrer des contraintes de développement durabl
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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.

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Le chargement/déchargement des conteneurs et leurs stockages provisoires dans le port est la plus importante et complexe tâche dans les terminaux portuaires. Elle est fortement liée au routage des grues de quai et son coût augmente considérablement surtout en absence d’une gestion efficace du terminal. Dans ce travail, nous étudions le problème de stockage des conteneurs (PSC). Il appartient à la catégorie des problèmes NP-difficiles et NP-complets. PSC consiste à déterminer un plan d’arrangement des conteneurs destinés à l’import et à l’export dans le port qui minimise les remaniements ultéri
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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.

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La résolution satisfaisante d'un problème d'optimisation difficile, qui comporte un grand nombre de solutions sous-optimales, justifie souvent le recours à une métaheuristique puissante. La majorité des algorithmes utilisés pour résoudre ces problèmes d'optimisation sont les métaheuristiques à population. Parmi celles-ci, nous intéressons à l'Optimisation par Essaim Particulaire (OEP, ou PSO en anglais) qui est apparue en 1995. PSO s'inspire de la dynamique d'animaux se déplaçant en groupes compacts (essaims d'abeilles, vols groupés d'oiseaux, bancs de poissons). Les particules d'un même essai
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Rahmani, Younes. "The Multi-product Location-Routing Problem with Pickup and Delivery." Electronic Thesis or Diss., Université de Lorraine, 2015. http://www.theses.fr/2015LORR0331.

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Dans les problèmes de localisation-routage classiques (LRP), il s'agit de combiner des décisions stratégiques liées aux choix des sites à ouvrir (centres de traitement) avec des décisions tactiques et opérationnelles liées à l'affectation des clients aux sites sélectionnés et a la confection des tournées associées. Cette thèse propose de nouveaux modèles de localisation-routage permettant de résoudre des problématiques issues de réseaux logistiques, devenus aujourd'hui de plus en plus complexes vu la nécessité de mutualisation de ressources pour intégrer des contraintes de développement durabl
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Lepagnot, 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.

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Dans la pratique, beaucoup de problèmes d'optimisation sont dynamiques : leur fonction objectif (ou fonction de coût) évolue au cours du temps. L'approche principalement adoptée dans la littérature consiste à adapter des algorithmes d'optimisation statique à l'optimisation dynamique, en compensant leurs défauts intrinsèques. Plutôt que d'emprunter cette voie, déjà largement explorée, l'objectif principal de cette thèse est d'élaborer un algorithme entièrement pensé pour l'optimisation dynamique. La première partie de cette thèse est ainsi consacrée à la mise au point d'un algorithme, qui doit
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Books on the topic "METAHEURISTIC OPTIMIZATION TECHNIQUES"

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Ponce-Ortega, José María, and Luis Germán Hernández-Pérez. Optimization of Process Flowsheets through Metaheuristic Techniques. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-91722-1.

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Ponce-Ortega, José María, and Luis Germán Hernández-Pérez. Optimization of Process Flowsheets through Metaheuristic Techniques. Springer, 2018.

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Ponce-Ortega, José María, and Luis Germán Hernández-Pérez. Optimization of Process Flowsheets through Metaheuristic Techniques. Springer, 2019.

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Okokpujie, Imhade P. Modern Optimization Techniques for Advanced Machining: Heuristic and Metaheuristic Techniques. Springer, 2023.

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Swamy, M. N. S., and Ke-Lin Du. Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature. Birkhauser Verlag, 2016.

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Swamy, M. N. S., and Ke-Lin Du. Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature. Birkhäuser, 2018.

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Swamy, M. N. S., and Ke-Lin Du. Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature. Springer, 2016.

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Book chapters on the topic "METAHEURISTIC OPTIMIZATION TECHNIQUES"

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Ponce-Ortega, José María, and Luis Germán Hernández-Pérez. "Metaheuristic Optimization Programs." In Optimization of Process Flowsheets through Metaheuristic Techniques. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91722-1_3.

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Precup, Radu-Emil, Raul-Cristian Roman, Elena-Lorena Hedrea, Alexandra-Iulia Szedlak-Stinean, and Iuliu Alexandru Zamfirache. "Metaheuristic Optimization Algorithms." In Classical and Modern Optimization Techniques Applied to Control and Modeling. CRC Press, 2025. https://doi.org/10.1201/9781003488279-5.

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Rao, R. V., and S. Patel. "Optimization of Robot Path Planning Using Advanced Optimization Techniques." In Metaheuristic Algorithms in Industry 4.0. CRC Press, 2021. http://dx.doi.org/10.1201/9781003143505-5.

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Abdel-Basset, Mohamed, Ripon K. Chakrabortty, and Reda Mohamed. "Metaheuristic Algorithms for Healthcare." In Application of Advanced Optimization Techniques for Healthcare Analytics. CRC Press, 2023. http://dx.doi.org/10.1201/9781003325574-2.

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Deroussi, Laurent, Nathalie Grangeon, and Sylvie Norre. "Optimization of Logistics Systems Using Metaheuristic-Based Hybridization Techniques." In Metaheuristics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45403-0_14.

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Ponce-Ortega, José María, and Luis Germán Hernández-Pérez. "Optimization of Industrial Process 1." In Optimization of Process Flowsheets through Metaheuristic Techniques. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91722-1_6.

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Ponce-Ortega, José María, and Luis Germán Hernández-Pérez. "Optimization of Industrial Process 2." In Optimization of Process Flowsheets through Metaheuristic Techniques. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91722-1_7.

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He, 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. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22744-9_28.

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Ponce-Ortega, José María, and Luis Germán Hernández-Pérez. "Interlinking Between Process Simulators and Optimization Programs." In Optimization of Process Flowsheets through Metaheuristic Techniques. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91722-1_4.

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Karnik, Niharika, and Pankaj Dhatrak. "Optimization Techniques and Algorithms for Dental Implants – A Comprehensive Review." In Metaheuristic Algorithms in Industry 4.0. CRC Press, 2021. http://dx.doi.org/10.1201/9781003143505-12.

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Conference papers on the topic "METAHEURISTIC OPTIMIZATION TECHNIQUES"

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Vena, Bulumko, and Kumeshan Reddy. "Distribution Network Reconfiguration via Metaheuristic Optimization Techniques." In 2025 33rd Southern African Universities Power Engineering Conference (SAUPEC). IEEE, 2025. https://doi.org/10.1109/saupec65723.2025.10944418.

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Mundotiya, Prahlad, Subhash Shrimal, Ankush Koli, Gayatri Kansotia, Neetika Meena, and Harpal Tiwari. "Metaheuristic Optimization Techniques for Economic Dispatch in Power Generation: A Review." In 2024 International Conference on Computer, Electronics, Electrical Engineering & their Applications (IC2E3). IEEE, 2024. https://doi.org/10.1109/ic2e362166.2024.10827068.

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Birashk, Amin, Sadaf MD Halim, and Latifur Khan. "Handling Non-IID Data in Federated Learning using Metaheuristic Optimization Techniques." In 2024 IEEE International Conference on Data Mining (ICDM). IEEE, 2024. https://doi.org/10.1109/icdm59182.2024.00076.

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Omar, Bahmid, Kouloughli Sihem, and Ouis Khedim Amaria. "Scheduling Optimization in Automated Storage and Retrieval Systems Using Metaheuristic Techniques: A Review." In 2024 International Conference of the African Federation of Operational Research Societies (AFROS). IEEE, 2024. https://doi.org/10.1109/afros62115.2024.11037135.

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Menesy, Ahmed S., Hamdy M. Sultan, Mohamed R. Elkadeem, et al. "Precise Parameter Estimation for Polymer Electrolyte Membrane Fuel Cells Using Two Sophisticated Metaheuristic Optimization Techniques." In 2024 IEEE Sustainable Power and Energy Conference (iSPEC). IEEE, 2024. https://doi.org/10.1109/ispec59716.2024.10892373.

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Serrano Elena, Antonio. "METAHEURISTIC ANALYSIS IN REVERSE LOGISTICS OF WASTE." In CIT2016. Congreso de Ingeniería del Transporte. Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/cit2016.2016.3163.

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This paper focuses in the use of search metaheuristic techniques on a dynamic and deterministic model to analyze and solve cost optimization problems and location in reverse logistics, within the field of municipal waste management of Málaga (Spain). In this work we have selected two metaheuristic techniques having relevance in present research, to test the validity of the proposed approach: an important technique for its international presence as is the Genetic Algorithm (GA) and another interesting technique that works with swarm intelligence as is the Particles Swarm Optimization (PSO). The
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Loubna, 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.

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Singh, 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.

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Koudela, 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.

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Optimization procedures offer a possibility for time-effective determination of input parameters values for complex soil constitutive models. The following paper presents a combination of the metaheuristic Particle swarm optimization method (PSO) and commercially available solver based on the finite element method (FEM). After the brief theoretical description, different alternatives to the PSO method are reviewed and tested. An optimal alternative is chosen and further used. In the second part of the paper, the combination PSO – FEM is utilized for a fully automatic derivation of input parame
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Gunal, 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.

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