Relevant bibliographies by topics / METAHEURISTIC OPTIMIZATION TECHNIQUES

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Academic literature on the topic 'METAHEURISTIC OPTIMIZATION TECHNIQUES'

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Published: 11 September 2023

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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 (October 19, 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 metaheuristics in combinatorial optimization. From the authors’ point of view, there is still a lack of comprehensive surveys on current research directions. Therefore, a substantial part of this paper is devoted to analyzing and discussing the modern age metaheuristic algorithms that gained popular use in mostly cited combinatorial optimization problems such as vehicle routing problems, traveling salesman problems, and supply chain network design problems. A survey of seven different metaheuristic algorithms (which are proposed after 2000) for combinatorial optimization problems is carried out in this study, apart from conventional metaheuristics like simulated annealing, particle swarm optimization, and tabu search. These metaheuristics have been filtered through some key factors like easy parameter handling, the scope of hybridization as well as performance efficiency. In this study, a concise description of the framework of the selected algorithm is included. Finally, a technical analysis of the recent trends of implementation is discussed, along with the impacts of algorithm modification on performance, constraint handling strategy, the handling of multi-objective situations using hybridization, and future research opportunities.
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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 (October 26, 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 Metaheuristic Hybridization) and MAGMA (Multiagent Metaheuristic Architecture), are adapted to be applied in the automatic design of ensemble systems, in both mono- and multi-objective versions. To validate the feasibility of these hybrid techniques, we conducted an empirical investigation, performing a comparative analysis between them and traditional metaheuristics as well as existing existing ensemble generation methods. Our findings demonstrate a competitive performance of both techniques, in which a hybrid technique provided the lowest error rate for most of the analyzed objective functions.
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Misevičius, 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.

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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 aiškinimo kryptys. Pabaigoje pateikiamos apibendrinamosios pastabos.Combinatorial optimization and metaheuristic methods: theoretical aspectsAlfonsas Misevičius, Vytautas Bukšnaitis, Jonas Blonskis SummaryIn this paper, theoretical aspects of combinatorial optimization (CO) and intelligent optimization techniques, i. e. metaheuristic methods (metaheuristics) are discussed. The combinatorial optimization problems and their basic properties are shortly introduced. Much of our attention is paid to the characterization of the metaheuristic methods, in particular for solving CO problems. We formulate the main goals of the metaheuristic methods, also focusing on the special theoretical issues and features of these methods. The most important interpretations of the metaheuristic methods are described in more details. The paper is completed with the concluding remarks.
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Sahoo, 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.

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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 presents a systematic review on several meta heuristic techniques like Genetic Algorithms, Particle Swarm optimization, Ant Colony Optimization, Bee Colony optimization, Cuckoo Searches, Tabu Searches and some modified version of these algorithms used for test case generation. The authors also provide one framework, showing the advantages, limitations and future scope or gap of these research works which will help in further research on these works.
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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 (September 1, 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 position to avoid the singularities caused by limitations of inverse kinematics equations, the task, and the workspace. In this article, alternative solutions for an ill-posed problem concerning the behavior of the robotic end-effector position and orientation are proposed using metaheuristic algorithms such as cuckoo search, differential evolution, and modified artificial bee colony. The case study for this work considers a three-revolute robot (3R), whose trajectories can contain or not singularities, and an optimization problem is defined to minimize the objective function that represents the error of the alternative trajectories. A tournament in combination with a constant of proportionality allows the metaheuristics to modify the gradual orientation and position of the robot when a singularity is present. Consequently, the procedure selects from all the possible elbow-configurations the best that fits the trajectory. A classical numerical technique, Newton’s method, is used to compare the results of the applied metaheuristics, to evaluate their quality. The results of this implementation indicate that metaheuristic algorithms are an efficient tool to solve the problem of optimizing the end-effector behavior, because of the quality of the alternative trajectory produced.
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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 (May 10, 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 extended to present differential evolution and teaching-learning-based optimization. Few applications of the said algorithms are also presented. This review intends to serve as a reference for further work in this domain.
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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 (February 27, 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 article presents a metaheuristic optimization framework for automated process discovery. The key idea of the framework is to construct a directly-follows graph (DFG) from the event log, to perturb this DFG so as to generate new candidate solutions, and to apply a DFG-based automated process discovery approach in order to derive a process model from each DFG. The framework can be instantiated by linking it to an automated process discovery approach, an optimization metaheuristic, and the quality measure to be optimized (e.g., fitness, precision, F-score). The article considers several instantiations of the framework corresponding to four optimization metaheuristics, three automated process discovery approaches (Inductive Miner—directly-follows, Fodina, and Split Miner), and one accuracy measure (Markovian F-score). These framework instances are compared using a set of 20 real-life event logs. The evaluation shows that metaheuristic optimization consistently yields visible improvements in F-score for all the three automated process discovery approaches, at the cost of execution times in the order of minutes, versus seconds for the baseline approaches.
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Navarro-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.

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In this study, a system for faults detection using a combination of Support Vector Data Description (SVDD) with metaheuristic algorithms is presented. The presented approach is applied to a real industrial process where the set of measured faults is scarce. The original contribution in this work is the industrial context of application and the comparison of swarm intelligence algorithms to optimize the SVDD hyper-parameters. Four recent metaheuristics are compared hereby to solve the corresponding optimization problem in an efficient manner. These optimization techniques are then implemented for fault detection in a multivariate industrial process with non-balanced data. The obtained numerical results seem to be promising when the considered optimization techniques are combined with SVDD. In particular, the Spotted Hyena algorithm outperforms other metaheuristics reaching values of F1 score near 100% in fault detection.
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Fidanova, 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.

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Tahami, 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.

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There are several approaches for solving hard optimization problems. Mathematical programming techniques such as (integer) linear programming-based methods and metaheuristic approaches are two extremely effective streams for combinatorial problems. Different research streams, more or less in isolation from one another, created these two. Only several years ago, many scholars noticed the advantages and enormous potential of building hybrids of combining mathematical programming methodologies and metaheuristics. In reality, many problems can be solved much better by exploiting synergies between these approaches than by “pure” classical algorithms. The key question is how to integrate mathematical programming methods and metaheuristics to achieve such benefits. This paper reviews existing techniques for such combinations and provides examples of using them for vehicle routing problems.
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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 executes PSO and GAs simultaneously in different swarms, and shows advantages when arranged in a star topology, particularly with a central GA. A static scheme executes in series, with a GA performing the exploration followed by MPSO for exploitation. Finally, the last approach dynamically alternates between algorithms. Hybrid algorithms are well-suited for parallelization, but exhibit tradeoffs between performance and solution quality.
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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. The new strategies can be applied to design, deploy and operate the multilateration systems for airport surface surveillance as well as takeoff-landing, approach and enroute control. An important advance in the current knowledge of air traffic control is expected from the development of these strategies, because they solve several deficiencies that have been made clear, by the international scientific community, in the last years.
Mantilla 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
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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 optimizers to search for near-optimal solutions. Moreover, we consider the design of matheuristic approaches that combine the advantages of population-based meta-heuristics with mathematical programming techniques. More specifically, we consider the integration of mathematical programming techniques during the initialization stage of the single and multi-objective approaches as well as during the actual search process. Using data collected from a manufacturing company, we provide evidence for the advantages of our approaches over conventional methods (integer linear programming and chance-constrained programming) and highlight the synergies resulting from the combination of simulation, meta-heuristics and mathematical programming methods. In the context of the same real-world problem, we also analyse different single and multi-objective SBO models for robust optimization. We demonstrate that the choice of robustness measure and the sample size used during fitness evaluation are crucial considerations in designing an effective multi-objective model.
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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’algorithme MCBB. Les contraintes du problème d’optimisation sont gérées à l'aide d'une nouvelle technique dédiée aux algorithmes évolutionnaires. Les performances de cette nouvelle approche sont évaluées et comparées à l’existant par une étude statistique sur un ensemble de problèmes tests. Les résultats montrent que les performances de notre algorithme sont compétitives face à l’algorithme NSGAII seul. Nous proposons deux applications de notre algorithme : les applications "Recherche de solutions faisables" et "Recherche de solutions optimales". Celles-ci sont appliquées sur un problème industriel réel d’un réducteur à engrenages à 3 étages formulé comme un problème bi-objectif. Dans ce problème des contraintes sont incluses pour satisfaire aux exigences de normes ISO sur le calcul de la capacité de charge des engrenages
Multi-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
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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 résultat. Les premiers algorithmes de segmentation d'image ont vu le jour dans les années 1970. Depuis, de nombreuses techniques et méthodes de segmentation ont été expérimentées pour essayer d'améliorer les résultats. Néanmoins, jusqu'à nos jours, aucun algorithme de segmentation d'image n'arrive à fournir des résultats parfaits sur une large variété d'images. Les "métaheuristiques" sont des procédures conçues pour résoudre des problèmes d'optimisation dits difficiles. Ce sont en général des problèmes aux données incomplètes, incertaines, bruitées ou confrontés à une capacité de calcul limitée. Les métaheuristiques ont connu un succès dans une large variété de domaines. Cela découle du fait qu'elles peuvent être appliquées à tout problème pouvant être exprimé sous la forme d'un problème d'optimisation de critère(s). Ces méthodes sont, pour la plupart, inspirées de la physique (recuit simulé), de la biologie (algorithmes évolutionnaires) ou de l'éthologie (essaims particulaires, colonies de fourmis).Ces dernières années, l'introduction des métaheuristiques dans le domaine du traitement d'images a permis d'étudier la segmentation sous un angle différent, avec des résultats plus ou moins réussis. Dans le but d'apporter notre contribution et d'améliorer davantage les performances des méthodes de segmentation, nous avons proposé des algorithmes basés régions, contours et hybrides, mettant en œuvre des métaheuristiques d'optimisation dans des approches mono et multiobjectif. Les méthodes proposées ont été évaluées sur des bases de données expérimentales composées d'images synthétiques, d'images IRM simulées et d'images IRM réelles ainsi que des images de tomographie par émission de positons (TEP). Les résultats obtenus sont significatifs et prouvent l'efficacité des idées proposées
Image 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
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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 durable et des prix de carburants qui semblent augmenter de manière irrémédiable. Plus précisément, trois aspects ont été intégrés pour généraliser les modèles LRP classiques de la littérature : 1) l'aspect pickup and delivery, 2) l'aspect multi-produits, et 3) la possibilité de visiter un ou plusieurs centres de traitement dans une tournée donnée. Nous avons étudié deux schémas logistiques, qui ont donné lieu à deux nouveaux modèles de localisation et de routage, le MPLRP-PD (LRP with multi-product and pickup and delivery), qui peut être vu comme une extension des problèmes de tournées de véhicules avec collecte et livraison, intégrant une décision tactique liée à la localisation des centres de traitement (noeud avec collecte et livraison) dans un réseau de distribution à un seul échelon, et le 2E-MPLRP-PD (Two-echelon LRP with multi-product and pickup and delivery) qui est une généralisation du LRP à deux échelons avec les contraintes citées plus-haut. Ces deux modèles ont été formalisés par des programmes linéaires en variables mixtes (MIP). Des techniques de résolution, basées sur des méthodes de type heuristique, clustering, métaheuristique, ont été proposées pour résoudre le MPLRP-PD et le 2E-MPLRP-PD. Les jeux d'essais de la littérature ont été généralisés pour tester et valider les algorithmes proposés
In 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
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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 ultérieurs lors de leur transfert vers le bateau, camion ou train. En effet, le temps d'attente des camions des clients, le temps de transfert des grues de quai et le temps nécessaire au chargement/déchargement du navire sont avantageusement réduits. PSC est généralement étudié en considérant un seul type de conteneur. Cependant, plusieurs types de conteneurs sont utilisés dans les ports maritimes (dry, réfrigérés, toit ouvert,...). En outre, le problème de stockage de conteneurs peut être traité de façon statique ou dynamique (date d’arrivée et de départ des conteneurs incertains).L’objectif de cette thèse est de résoudre le PSC statique et le PSC dynamique pour un seul et plusieurs types de conteneurs en utilisant deux métaheuristiques : l’algorithme génétique, la recherche harmoniquePour vérifier la performance de chacune des approches proposées, une étude comparative des résultats générés par chaque méthode ainsi que celle de l’algorithme LIFO est établie
The 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
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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 essaim communiquent entre elles tout au long de la recherche pour construire une solution au problème posé, et ce en s'appuyant sur leur expérience collective. L'algorithme PSO, qui est simple à comprendre, à programmer et à utiliser, se révèle particulièrement efficace pour les problèmes d'optimisation à variables continues. Cependant, comme toutes les métaheuristiques, PSO possède des inconvénients, qui rebutent encore certains utilisateurs. Le problème de convergence prématurée, qui peut conduire les algorithmes de ce type à stagner dans un optimum local, est un de ces inconvénients. L'objectif de cette thèse est de proposer des mécanismes, incorporables à PSO, qui permettent de remédier à cet inconvénient et d'améliorer les performances et l'efficacité de PSO. Nous proposons dans cette thèse deux algorithmes, nommés PSO-2S et DEPSO-2S, pour remédier au problème de la convergence prématurée. Ces algorithmes utilisent des idées innovantes et se caractérisent par de nouvelles stratégies d'initialisation dans plusieurs zones, afin d'assurer une bonne couverture de l'espace de recherche par les particules. Toujours dans le cadre de l'amélioration de PSO, nous avons élaboré une nouvelle topologie de voisinage, nommée Dcluster, qui organise le réseau de communication entre les particules. Les résultats obtenus sur un jeu de fonctions de test montrent l'efficacité des stratégies mises en oeuvre par les différents algorithmes proposés. Enfin, PSO-2S est appliqué à des problèmes pratiques, en segmentation d'images et en électronique
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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 non seulement se démarquer des algorithmes concurrents par son originalité, mais également être plus performant. Dans ce contexte, il s'agit de développer une métaheuristique d'optimisation dynamique. Deux algorithmes à base d'agents, MADO (MultiAgent algorithm for Dynamic Optimization) et MLSDO (Multiple Local Search algorithm for Dynamic Optimization), sont proposés et validés sur les deux principaux jeux de tests existant dans la littérature en optimisation dynamique : MPB (Moving Peaks Benchmark) et GDBG (Generalized Dynamic Benchmark Generator). Les résultats obtenus sur ces jeux de tests montrent l'efficacité des stratégies mises en oeuvre par ces algorithmes, en particulier : MLSDO est classé premier sur sept algorithmes évalués sur GDBG, et deuxième sur seize algorithmes évalués sur MPB. Ensuite, ces algorithmes sont appliqués à des problèmes pratiques en traitement de séquences d'images médicales (segmentation et recalage de séquences ciné-IRM cérébrales). A notre connaissance, ce travail est innovant, en ce sens que l'approche de l'optimisation dynamique n'avait jamais été explorée pour ces problèmes. Les gains de performance obtenus montrent l'intérêt d'utiliser les algorithmes d'optimisation dynamique proposés pour ce type d'applications
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Cooren, 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.

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Les métaheuristiques sont une famille d'algorithmes stochastiques destinés à résoudre des problèmes d 'optimisation difficile . Utilisées dans de nombreux domaines, ces méthodes présentent l'avantage d'être généralement efficaces, sans pour autant que l'utilisateur ait à modifier la structure de base de l'algorithme qu'il utilise. Parmi celles-ci, l'Optimisation par Essaim Particulaire (OEP) est une nouvelle classe d'algorithmes proposée pour résoudre les problèmes à variables continues. Les algorithmes d'OEP s'inspirent du comportement social des animaux évoluant en essaim, tels que les oiseaux migrateurs ou les poissons. Les particules d'un même essaim communiquent de manière directe entre elles tout au long de la recherche pour construire une solution au problème posé, en s'appuyant sur leur expérience collective. Reconnues depuis de nombreuses années pour leur efficacité, les métaheuristiques présentent des défauts qui rebutent encore certains utilisateurs. Le réglage des paramètres des algorithmes est un de ceux-ci. Il est important, pour chaque problème posé, de trouver le jeu de paramètres qui conduise à des performances optimales de l'algorithme. Cependant, cette tâche est fastidieuse et coûteuse en temps, surtout pour les utilisateurs novices. Pour s'affranchir de ce type de réglage, des recherches ont été menées pour proposer des algorithmes dits adaptatifs . Avec ces algorithmes, les valeurs des paramètres ne sont plus figées, mais sont modifiées, en fonction des résultats collectés durant le processus de recherche. Dans cette optique-là, Maurice Clerc a proposé TRIBES, qui est un algorithme d'OEP mono-objectif sans aucun paramètre de contrôle. Cet algorithme fonctionne comme une boîte noire , pour laquelle l'utilisateur n'a qu'à définir le problème à traiter et le critère d'arrêt de l'algorithme. Nous proposons dans cette thèse une étude comportementale de TRIBES, qui permet d'en dégager les principales qualités et les principaux défauts. Afin de corriger certains de ces défauts, deux modules ont été ajoutés à TRIBES. Une phase d'initialisation régulière est insérée, afin d'assurer, dès le départ de l'algorithme, une bonne couverture de l'espace de recherche par les particules. Une nouvelle stratégie de déplacement, basée sur une hybridation avec un algorithme à estimation de distribution, est aussi définie, afin de maintenir la diversité au sein de l'essaim, tout au long du traitement. Le besoin croissant de méthodes de résolution de problèmes multiobjectifs a conduit les concepteurs à adapter leurs méthodes pour résoudre ce type de problème. La complexité de cette opération provient du fait que les objectifs à optimiser sont souvent contradictoires. Nous avons élaboré une version multiobjectif de TRIBES, dénommée MO-TRIBES. Nos algorithmes ont été enfin appliqués à la résolution de problèmes de seuillage d'images médicales et au problème de dimensionnement de composants de circuits analogiques
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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. Cham: 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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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, 27–51. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91722-1_3.

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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, 83–126. Boca Raton: 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, 25–36. New York: 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, 381–405. Cham: 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, 79–90. Cham: 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, 91–97. Cham: 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, 357–66. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22744-9_28.

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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, 261–82. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003143505-12.

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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, 53–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91722-1_4.

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Abdel-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.

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Conference papers on the topic "METAHEURISTIC OPTIMIZATION TECHNIQUES"

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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.

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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). These metaheuristic techniques will be used to solve cost optimization problems and location of MSW recovery facilities (transfer centers and treatment plants).DOI: http://dx.doi.org/10.4995/CIT2016.2016.3163
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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 parameters values for the Hardening small strain model from pressuremeter tests. Predicted pressurevolume curves from the axisymmetric FE model gradually converge towards the measured curve until the accuracy criterion is reached.
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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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Leite 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.

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Sarapan, 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.

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Wadi, 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.

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Boualem, 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.

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Kavaliauskas, 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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One of the most important tasks in modern construction is to build a building according to the desired time schedule. This requires a timetable for the construction stages processes. The schedule is also easy to adapt and to plan the renovation of a building or maintenance works. Without a good schedule site manager cannot effectively handle construction area processes. The solution to this problem is the proposed productivity bot concept based on metaheuristic algorithms. Metaheuristic algorithms allow to improve the construction planning process schedules compared with conventional planning methods and equipment. The results of testing with construction planning data has shown that the metaheuristic algorithm achieved the main improvements during the first planning optimization stages. The proposed concept is expected to present results that are close to the optimal timetable and surpass classical methods for scheduling. Productivity bots act as a software engine complementing with the organization of construction or automation functionality. Productivity bot is designed to manage building processes. It is intended for automated planning of construction stages schedules according to construction technologies.
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