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Добірка наукової літератури з теми "METAHEURISTIC APPROACH"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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Статті в журналах з теми "METAHEURISTIC APPROACH"

Laudis, Lalin L., and Amit Kumar Sinha. "Metaheuristic Approach for VLSI 3D-Floorplanning." International Journal of Scientific Research 2, no. 12 (June 1, 2012): 202–3. http://dx.doi.org/10.15373/22778179/dec2013/62.

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LEE, YOUNG CHOON, JAVID TAHERI, and ALBERT Y. ZOMAYA. "A PARALLEL METAHEURISTIC FRAMEWORK BASED ON HARMONY SEARCH FOR SCHEDULING IN DISTRIBUTED COMPUTING SYSTEMS." International Journal of Foundations of Computer Science 23, no. 02 (February 2012): 445–64. http://dx.doi.org/10.1142/s0129054112400229.

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Анотація:

A large number of optimization problems have been identified as computationally challenging and/or intractable to solve within a reasonable amount of time. Due to the NP-hard nature of these problems, in practice, heuristics account for the majority of existing algorithms. Metaheuristics are one very popular type of heuristics used for many of these optimization problems. In this paper, we present a novel parallel-metaheuristic framework, which effectively enables to devise parallel metaheuristics, particularly with heterogeneous metaheuristics. The core component of the proposed framework is its harmony-search-based coordinator. Harmony search is a recent breed of metaheuristic that mimics the improvisation process of musicians. The coordinator facilitates heterogeneous metaheuristics (forming a parallel metaheuristic) to escape local optima. Specifically, best solutions generated by these worker metaheuristics are maintained in the harmony memory of the coordinator, and they are used to form new-possibly better-harmonies (solutions) before actual solution sharing between workers occurs; hence, their solutions are harmonized with each other. For the applicability validation and the performance evaluation, we have implemented a parallel hybrid metaheuristic using the framework for the task scheduling problem on multiprocessor computing systems (e.g., computer clusters). Experimental results verify that the proposed framework is a compelling approach to parallelize heterogeneous metaheuristics.

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Bajenaru, Victor, Steven Lavoie, Brett Benyo, Christopher Riker, Mitchell Colby, and James Vaccaro. "Recommender System Metaheuristic for Optimizing Decision-Making Computation." Electronics 12, no. 12 (June 14, 2023): 2661. http://dx.doi.org/10.3390/electronics12122661.

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We implement a novel recommender system (RS) metaheuristic framework within a nonlinear NP-hard decision-making problem, for reducing the solution search space before high-burden computational steps are performed. Our RS-based metaheuristic supports consideration of comprehensive evaluation criteria, including estimations of the potential solution set’s optimality, diversity, and feedback/preference of the end-user, while also being fully compatible with additional established RS evaluation metrics. Compared to prior Operations Research metaheuristics, our RS-based metaheuristic allows for (1) achieving near-optimal solution scores through comprehensive deep learning training, (2) fast metaheuristic parameter inference during solution instantiation trials, and (3) the ability to reuse this trained RS module for traditional RS ranking of final solution options for the end-user. When implementing this RS metaheuristic within an experimental high-dimensionality simulation environment, we see an average 91.7% reduction in computation time against a baseline approach, and solution scores within 9.1% of theoretical optimal scores. A simplified RS metaheuristic technique was also developed in a more realistic decision-making environment dealing with multidomain command and control scenarios, where a significant computation time reduction of 87.5% is also achieved compared with a baseline approach, while maintaining solution scores within 9.5% of theoretical optimal scores.

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Rosłon, Jerzy Hubert, and Janusz Edward Kulejewski. "A hybrid approach for solving multi-mode resource-constrained project scheduling problem in construction." Open Engineering 9, no. 1 (January 31, 2019): 7–13. http://dx.doi.org/10.1515/eng-2019-0006.

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Анотація:

AbstractPractical problems in construction can be easily qualified as NP-hard (non-deterministic, polynomial-time hard) problems. The time needed for solving these problems grows exponentially with the increase of the problem’s size – this is why mathematical and heuristic methods do not enable finding solutions to complicated construction problems within an acceptable period of time. In the view of many authors, metaheuristic algorithms seem to be the most appropriate measures for scheduling and task sequencing. However even metaheuristic approach does not guarantee finding the optimal solution and algorithms tend to get stuck around local optima of objective functions. This is why authors considered improving the metaheuristic approach by the use of neural networks. In the article, authors analyse possible benefits of using a hybrid approach with the use of metaheuristics and neural networks for solving the multi-mode, resource-constrained, project-scheduling problem (MRCPSP). The suggested approach is described and tested on a model construction project schedule. The results are promising for construction practitioners, the hybrid approach improved results in 87% of tests. Based on the research outcomes, authors suggest future research ideas.

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Correia, Sérgio D., Marko Beko, Luis A. Da Silva Cruz, and Slavisa Tomic. "Elephant Herding Optimization for Energy-Based Localization." Sensors 18, no. 9 (August 29, 2018): 2849. http://dx.doi.org/10.3390/s18092849.

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This work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations, we approach it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics are applied to this type of problem. More specifically, an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as a performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.

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Talatahari, Babak, Mahdi Azizi, Siamak Talatahari, Mohamad Tolouei, and Pooya Sareh. "Crystal structure optimization approach to problem solving in mechanical engineering design." Multidiscipline Modeling in Materials and Structures 18, no. 1 (March 1, 2022): 1–23. http://dx.doi.org/10.1108/mmms-10-2021-0174.

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PurposeIn this paper, the authors aim to examine and comparatively evaluate a recently-developed metaheuristic called crystal structure algorithm (CryStAl) – which is inspired by the symmetries in the internal structure of crystalline solids – in solving engineering mechanics and design problems.Design/methodology/approachA total number of 20 benchmark mathematical functions are employed as test functions to evaluate the overall performance of the proposed method in handling various functions. Moreover, different classical and modern metaheuristic algorithms are selected from the optimization literature for a comparative evaluation of the performance of the proposed approach. Furthermore, five well-known mechanical design examples are utilized to examine the capability of the proposed method in dealing with challenging optimization problems.FindingsThe results of this study indicated that, in most cases, CryStAl produced more accurate outputs when compared to the other metaheuristics examined as competitors.Research limitations/implicationsThis paper can provide motivation and justification for the application of CryStAl to solve more complex problems in engineering design and mechanics, as well as in other branches of engineering.Originality/valueCryStAl is one of the newest metaheuristic algorithms, the mathematical details of which were recently introduced and published. This is the first time that this algorithm is applied to solving engineering mechanics and design problems.

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Cruz-Duarte, Jorge M., José C. Ortiz-Bayliss, Iván Amaya, Yong Shi, Hugo Terashima-Marín, and Nelishia Pillay. "Towards a Generalised Metaheuristic Model for Continuous Optimisation Problems." Mathematics 8, no. 11 (November 17, 2020): 2046. http://dx.doi.org/10.3390/math8112046.

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Анотація:

Metaheuristics have become a widely used approach for solving a variety of practical problems. The literature is full of diverse metaheuristics based on outstanding ideas and with proven excellent capabilities. Nonetheless, oftentimes metaheuristics claim novelty when they are just recombining elements from other methods. Hence, the need for a standard metaheuristic model is vital to stop the current frenetic tendency of proposing methods chiefly based on their inspirational source. This work introduces a first step to a generalised and mathematically formal metaheuristic model, which can be used for studying and improving them. This model is based on a scheme of simple heuristics, which perform as building blocks that can be modified depending on the application. For this purpose, we define and detail all components and concepts of a metaheuristic (i.e., its search operators), such as heuristics. Furthermore, we also provide some ideas to take into account for exploring other search operator configurations in the future. To illustrate the proposed model, we analyse search operators from four well-known metaheuristics employed in continuous optimisation problems as a proof-of-concept. From them, we derive 20 different approaches and use them for solving some benchmark functions with different landscapes. Data show the remarkable capability of our methodology for building metaheuristics and detecting which operator to choose depending on the problem to solve. Moreover, we outline and discuss several future extensions of this model to various problem and solver domains.

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Barraza, Juan, Luis Rodríguez, Oscar Castillo, Patricia Melin, and Fevrier Valdez. "A New Hybridization Approach between the Fireworks Algorithm and Grey Wolf Optimizer Algorithm." Journal of Optimization 2018 (May 27, 2018): 1–18. http://dx.doi.org/10.1155/2018/6495362.

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The main aim of this paper is to present a new hybridization approach for combining two powerful metaheuristics, one inspired by physics and the other one based on bioinspired phenomena. The first metaheuristic is based on physics laws and imitates the explosion of the fireworks and is called Fireworks Algorithm; the second metaheuristic is based on the behavior of the grey wolf and belongs to swarm intelligence methods, and this method is called the Grey Wolf Optimizer algorithm. For this work we studied and analyzed the advantages of the two methods and we propose to enhance the weakness of both methods, respectively, with the goal of obtaining a new hybridization between the Fireworks Algorithm (FWA) and the Grey Wolf Optimizer (GWO), which is denoted as FWA-GWO, and that is presented in more detail in this work. In addition, we are presenting simulation results on a set of problems that were tested in this paper with three different metaheuristics (FWA, GWO, and FWA-GWO) and these problems form a set of 22 benchmark functions in total. Finally, a statistical study with the goal of comparing the three different algorithms through a hypothesis test (Z-test) is presented for supporting the conclusions of this work.

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Yaghini, Masoud, Mohsen Momeni, and Mohammadreza Sarmadi. "A DIMMA-Based Memetic Algorithm for 0-1 Multidimensional Knapsack Problem Using DOE Approach for Parameter Tuning." International Journal of Applied Metaheuristic Computing 3, no. 2 (April 2012): 43–55. http://dx.doi.org/10.4018/jamc.2012040104.

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Multidimensional 0-1 Knapsack Problem (MKP) is a well-known integer programming problems. The objective of MKP is to find a subset of items with maximum value satisfying the capacity constraints. A Memetic algorithm on the basis of Design and Implementation Methodology for Metaheuristic Algorithms (DIMMA) is proposed to solve MKP. DIMMA is a new methodology to develop a metaheuristic algorithm. The Memetic algorithm is categorized as metaheuristics and is a particular class of evolutionary algorithms. The parameters of the proposed algorithm are tuned by Design of Experiments (DOE) approach. DOE refers to the process of planning the experiment so that appropriate data that can be analyzed by statistical methods will be collected, resulting in valid and objective conclusions. The proposed algorithm is tested on several MKP standard instances from OR-Library. The results show the efficiency and effectiveness of the proposed algorithm.

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Wahab, Hala Bahjat Abdul, Suhad Malallah Kadhem, and Estabraq Abdul Redaa Kadhim. "Proposed Approach for Elliptic Curve Cryptography Based on Metaheuristic Algorithms." International Journal of Scientific Research 2, no. 10 (June 1, 2012): 1–5. http://dx.doi.org/10.15373/22778179/oct2013/33.

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Дисертації з теми "METAHEURISTIC APPROACH"

Zhao, Jian-Hua. "The reliability optimization of mechanical systems using metaheuristic approach." Mémoire, École de technologie supérieure, 2005. http://espace.etsmtl.ca/326/1/ZHAO_Jian%2DHua.pdf.

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Le problème d'optimisation de fiabilité des systèmes mécaniques est un problème compliqué avec contraintes multicritères, dont la solution optimale est en générale un compromis. Le travail présenté dans ce mémoire se concentre sur l'optimisation de fiabilité des systèmes mécaniques en séries parallèles. Basée sur le ACSRAP (Ant Colony System for Redundancy Apportionment Problem), une nouvelle approche est présentée. Cette approche combine les caractéristiques de l'ACS avec des recherches locales. Donc il optimise la fiabilité globale du système tout en satisfaisant les contraintes en terme de coût, de poids et de volume. Les avantages sur la précision, l'efficacité, et la capacité de la nouvelle approche sont illustrés par les résultats de comparaison de là nouvelle technique avec ceux obtenues par d'autres approches. En outre, l'application de la technique sur une boite de transmission (Gear Train System) est aussi présenté pour montrer les procédures de l'application de la nouvelle technique sur les cas réels.

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Zamperin, Filippo <1994&gt. "Testing standard technical analysis parameters' efficiency, a metaheuristic approach." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/17564.

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The thesis proposed is aimed to exploit the profitability of Technical Analysis (TA) identifying among the wide set of indicators a sub-set of them, widely used both in literature and in practise, to optimize with metaheuristic algorithms and to test using a trading system. Since its introduction, there has been a wide discussion about the profitability of TA and mostly of the time it has been compared with the fundamental and Buy-And-Hold strategy. To achieve superior performance a trading system should be run using TA indicators that promptly signal when it is time to buy and sell. However, to find such TA indicators' values it requires the solution of a complex problem that can be expressed as an optimization one.The thesis proposed wants to compare three different metaheuristic algorithms, Particle Swarm Optimization (PSO), Differential Evolution (DE) and Fireworks (FWA), searching among them which one perform better.

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CUNHA, VICTOR ABU-MARRUL CARNEIRO DA. "RESCHEDULING OF OIL EXPLORATION SUPPORT VESSELS WITHIN A METAHEURISTIC APPROACH." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2017. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=30908@1.

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Анотація:

PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
A dissertação aborda um problema real de reprogramação de uma frota de embarcações do tipo PLSV (Pipe Laying Support Vessel), responsáveis pelas interligações de poços petrolíferos submarinos. O cronograma de curto prazo dessas embarcações está sujeito à inúmeras incertezas inerentes às operações realizadas, acarretando em ociosidade nas embarcações ou postergações na produção de petróleo, que podem resultar em prejuízo de milhões de reais. Uma metaheurística ILS (Iterated Local Search) é proposta para atender a frequente demanda por reprogramações dos PLSVs. O método é composto de uma fase inicial de viabilização, para tratar potenciais inconsistências nas programações. Na sequência, iterativamente, são realizadas perturbações na solução por meio de movimentos de swap e aplicada uma busca local baseada na vizinhança insert, a fim de fugir de ótimos locais e encontrar soluções que aprimorem o cronograma. Foram feitos experimentos com diferentes parâmetros e critérios do ILS, sendo definidas duas abordagens aplicadas a dez instâncias oriundas de uma programação real de PLSVs. A partir de uma função de avaliação, capaz de medir o impacto operacional na programação, o ILS proporcionou uma melhoria média nos cronogramas acima de 91 por cento, quando comparados aos cronogramas originais. As soluções foram obtidas em um tempo computacional médio de 30 minutos, aderente ao processo da companhia. Em função dos resultados alcançados, o método provou ser uma boa base para uma ferramenta de apoio à decisão para a reprogramação dos PLSVs.
This dissertation addresses a real-life rescheduling problem of a Pipe Laying Support Vessels (PLSVs) fleet, in charge of subsea oil wells interconnections. The short-term schedule of these vessels is subject to uncertainties inherent to its operations, resulting in ships idleness or delays in oil production, which may lead to losses of millions of Brazilian Reais. A method based on the ILS (Iterated Local Search) metaheuristic is proposed to meet the frequent demand of PLSVs rescheduling. The first step of this method aims to find a feasible initial solution from an incoming schedule with potencial inconsistencies. The following steps consists in, iteratively, performing a perturbation on a solution through swap movements and applying a local search based on the insertion neighborhood, in order to escape from local optimal and find better solutions. Extensive preliminary experiments were conducted considering different ILS parameters setups. The two most performing setups were selected and applied to ten instances of a real PLSV schedule. Taking into account an objective function that measures the operational impact on schedules, the ILS provided an average improvement above 91 percent in schedules when compared to the original planning. These solutions were obtained in an average computational time of 30 minutes, which fits in the company process. The obtained results showed that the proposed method might be a basis for a decision support tool for the PLSVs rescheduling problem.

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FADDA, GIANFRANCO. "A metaheuristic approach for the Vehicle Routing Problem with Backhauls." Doctoral thesis, Università degli Studi di Cagliari, 2017. http://hdl.handle.net/11584/249582.

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Despite the vivid research activity in the sector of the exact methods, nowadays many Optimization Problems are classified as Np-Hard and need to be solved by heuristic methods, even in the case of instances of limited size. In this thesis a Vehicle Routing Problem with Backhauls is investigated. A Greedy Randomized Adaptive Search Procedure metaheuristic is proposed for this problem. Several versions of the metaheuristic are tested on symmetric and asymmetric instances. Although the metaheuristic does not outperform the best known solutions, a large number of high-quality routes are determined in several solutions for each instance. Therefore the metaheuristic is a promising approach to generate feasible paths for set-partitioning-based formulations.

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Ahmad, Maqsood. "Mathematical models and methods based on metaheuristic approach for timetabling problem." Thesis, Clermont-Ferrand 2, 2013. http://www.theses.fr/2013CLF22393/document.

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Résumé indisponible
In this thesis we have concerned ourselves with university timetabling problems both course timetabling and examination timetabling problems. Most of the timetabling problems are computationally NP-complete problems, which means that the amount of computation required to ﬁnd solutions increases exponentially with problem size. These are idiosyncratic nature problems, for example different universities have their own set of constraints, their own definition of good timetable, feasible timetable and their own choice about the use of constraint type (as a soft or hard constraint). Unfortunately, it is often the case that a problem solving approach which is successfully applied for one specific problem may not become suitable for others. This is a motivation, we propose a generalized problem which covers many constraints used in different universities or never used in literature. Many university timetabling problems are sub problems of this generalized problem. Our proposed algorithms can solve these sub problems easily, moreover constraints can be used according to the desire of user easily because these constraints can be used as reference to penalty attached with them as well. It means that give more penalty value to hard constraints than soft constraint. Thus more penalty value constraints are dealt as a hard constraint by algorithm. Our algorithms can also solve a problem in two phases with little modification, where in first phase hard constraints are solved. In this work we have preferred and used two phase technique to solve timetabling problems because by using this approach algorithms have broader search space in first phase to satisfy hard constraints while not considering soft constraints at all. Two types of algorithms are used in literature to solve university timetabling problem, exact algorithms and approximation algorithms. Exact algorithms are able to find optimal solution, however in university timetabling problems exact algorithms constitute brute-force style procedures. And because these problems have the exponential growth rates of the search spaces, thus these kinds of algorithms can be applied for small size problems. On the other side, approximation algorithms may construct optimal solution or not but they can produce good practically useable solutions. Thus due to these factors we have proposed approximation algorithms to solve university timetabling problem. We have proposed metaheuristic based techniques to solve timetabling problem, thus we have mostly discussed metaheuristic based algorithms such as evolutionary algorithms, simulated annealing, tabu search, ant colony optimization and honey bee algorithms. These algorithms have been used to solve many other combinatorial optimization problems other than timetabling problem by modifying a general purpose algorithmic framework. We also have presented a bibliography of linear integer programming techniques used to solve timetabling problem because we have formulated linear integer programming formulations for our course and examination timetabling problems. We have proposed two stage algorithms where hard constraints are satisfied in first phase and soft constraints in second phase. The main purpose to use this two stage technique is that in first phase hard constraints satisfaction can use more relax search space because in first phase it does not consider soft constraints. In second phase it tries to satisfy soft constraints when maintaining hard constraints satisfaction which are already done in first phase. (...)

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Kuang, Yue(Yue Rick). "A metaheuristic approach to optimizing a multimodal truck and drone delivery system." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122401.

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Анотація:

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 50-51).
The success of e-commerce continues to push the bounds of delivery services as customers expect near instant fulfillment at little additional cost. This demand for delivery performance and operational cost efficiency has led to the exploration of the last-mile delivery problem using creative multimodal delivery systems. One promising system consists of a truck that can carry and deploy multiple autonomous drones to assist in the fulfillment of customer demand. The contribution of this thesis is towards furthering the understanding of the application of autonomous flying drones in such a system and improve parcel delivery performance within the constraint of the current state of technology. This thesis explores the feasibility of deploying drones in last-mile delivery by modeling and then optimizing the cost of serving customers with a system consisting of one truck and multiple drones under multiple customer demand scenarios. While this optimization problem can be solved with mixed integer linear programming (MILP), the computation requirement is such that MILP is inefficient for real world scenarios with 100 or more customers. This research applies metaheuristic methodology to solve the truck-and-drone problem for scenarios with up to 158 customers in approximately 30 minutes of computation time. The test results confirm an average of 7% to 9% in savings opportunity for a 2-drone baseline over traditional single truck delivery tours. This savings opportunity is shown to vary with customer density, number of drones carried, range of drone flight, and speed of drone relative to speed of truck.
by Yue Kuang.
M. Eng. in Supply Chain Management
M.Eng.inSupplyChainManagement Massachusetts Institute of Technology, Supply Chain Management Program

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Saremi, Alireza. "Mathematical programming enhanced metaheuristic approach for simulation-based optimization in outpatient appointment scheduling." Elsevier, 2013. http://hdl.handle.net/1993/21710.

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Анотація:

In the last two decades, the western world witnessed a continuous rise in the health expenditure. Meanwhile, complaints from patients on excessive waiting times are also increasing. In the past, many researchers have tried to devise appointment scheduling rules to provide trade-offs between maximizing patients’ satisfaction and minimizing the costs of the health providers. For instance, this challenge appears appointment scheduling problems (ASP). Commonly used methods in ASP include analytical methods, simulation studies, and combination of simulation with heuristic approaches. Analytical methods (e.g., queuing theory and mathematical programming) face challenges of fully capturing the complexities of systems and usually make strong assumptions for tractability of problems. These methods simplify the whole system to a single-stage unit and ignore the actual system factors such as the presence of multiple stages and/or resource constraints. Simulation studies, conversely, are able to model most complexities of the actual system, but they typically lack an optimization strategy to deliver optimal appointment schedules. Also, heuristic approaches normally are based on intuitive rules and do not perform well as standalone methods. In order to reach an optimal schedule while considering complexities in actual health care systems, this thesis proposes efficient and effective methods that yield (near) optimal appointment schedules by integrating mathematical programming, a tabu search optimization algorithm and discrete event simulation. The proposed methodologies address the challenges and complexities of scheduling in real world multistage healthcare units in the presence of stochastic service durations, a mix of patient types, patients with heterogeneous service sequence, and resource constraints. Moreover, the proposed methodology is capable of finding the optimum considering simultaneously multiple performance criteria. A Pareto front (a set of optimal solutions) for the performance criteria can be obtained using the proposed methods. Healthcare management can use the Pareto front to choose the appropriate policy based on different conditions and priorities. In addition, the proposed method has been applied to two case studies of Operating Rooms departments in two major Canadian hospitals. The comparison of actual schedules and the ones yielded by the proposed method indicates that proposed method can improve the appointment scheduling in realistic clinical settings.

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Alvarez, Fernandez Stephanie Milena. "A metaheuristic and simheuristic approach for the p-HUB median problem from a telecommunication perspective." Doctoral thesis, Universitat Oberta de Catalunya, 2018. http://hdl.handle.net/10803/666752.

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Анотація:

Els últims avenços en la indústria de les telecomunicacions ofereixen grans oportunitats per a ciutadans i organitzacions en un món globalment connectat, però alhora presenten reptes als quals s'enfronten tècnics i enginyers que prenen decisions. Alguns d'aquests reptes es poden modelitzar com a problemes d'optimització. El primer objectiu d'aquest treball és proporcionar un revisió de la literatura sobre com s'han utilitzat aquestes tècniques tradicionalment per a tractar els problemes d'optimització associats a sistemes de telecomunicació, detectant les principals tendències i desafiaments. Particularment, l'estudi es centra en els problemes de disseny de xarxes, encaminament i problemes d'assignació de recursos. A causa de la naturalesa d’aquests problemes, aquest treball també analitza com es poden combinar les tècniques metaheurístiques amb metodologies de simulació per a ampliar les capacitats de resoldre problemes d'optimització estocàstics. A més, es tracta un popular problema d'optimització amb aplicacions pràctiques per a xarxes de telecomunicació, el problema de la p mediana no capacitat, analitzant-lo des d'escenaris deterministes i estocàstics.
Los recientes avances en la industria de las telecomunicaciones ofrecen grandes oportunidades para ciudadanos y organizaciones en un mundo globalmente conectado, pero también presentan una gran cantidad de desafíos complejos a los que se enfrentan diariamente técnicos e ingenieros. Algunos de estos desafíos se pueden modelar como problemas de optimización. El primer objetivo de esta tesis es proporcionar una revisión de la literatura de cómo se han utilizado estas técnicas tradicionalmente para tratar los problemas de optimización asociados a sistemas de telecomunicaciones, detectando las principales tendencias y desafíos. En particular, el estudio se centra en los problemas de diseño de red, direccionamiento y problemas de asignación de recursos. Debido a la naturaleza de estos problemas, este trabajo también analiza cómo se pueden combinar las técnicas metaheurísticas con metodologías de simulación para ampliar las capacidades de resolver problemas de optimización estocásticos. Después se trata un popular problema de optimización con aplicaciones prácticas para redes de telecomunicaciones, el problema de la p mediana no capacitado, analizándolo desde escenarios deterministas y estocásticos.
Recent advances in the telecommunications industry offer great opportunities to citizens and organizations in a globally-connected world, but they also create a vast number of complex challenges that decision-makers must face. Some of these challenges can be modelled as optimization problems. First, this thesis reviews how metaheuristics have been used to date to deal with optimization problems associated with telecommunication systems, detecting the main trends and challenges. In particular, the analysis focuses on problems in network design, routing, and allocation. Given the nature of these challenges, this work also discusses how the hybridization of metaheuristics with methodologies such as simulation can be employed to increase metaheuristics' capabilities when solving stochastic optimization problems. In addition, a popular optimization problem with practical applications in the design of telecommunications networks, the Uncapacitated Single Allocation p-Hub Median Problem (USApHMP) – where a fixed number of hubs have unlimited capacity, each non-hub node is allocated to a single hub and the number of hubs is known in advance –, is analysed in deterministic and stochastic scenarios.

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Xu, Ying. "Metaheuristic approaches for QoS multicast routing problems." Thesis, University of Nottingham, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.546470.

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Christian, Blum, ed. Hybrid metaheuristics: An emerging approach to optimization. Berlin: Springer, 2008.

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Bandyopadhyay, Sanghamitra. Unsupervised classification: Similarity measures, classical and metaheuristic approaches, and applications. Berlin: Springer, 2013.

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Cognitive Big Data Intelligence with a Metaheuristic Approach. Elsevier, 2022. http://dx.doi.org/10.1016/c2020-0-02004-9.

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Savsani, Vimal J., Vivek K. Patel, and Mohamed A. Tawhid. Thermal System Optimization: A Population-Based Metaheuristic Approach. Springer, 2019.

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Blondin, Maude Josée. Controller Tuning Optimization Methods for Multi-Constraints and Nonlinear Systems: A Metaheuristic Approach. Springer International Publishing AG, 2021.

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Das, Swagatam, Nanda Dulal Jana, and Jaya Sil. A Metaheuristic Approach to Protein Structure Prediction: Algorithms and Insights from Fitness Landscape Analysis. Springer, 2018.

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Das, Swagatam, Nanda Dulal Jana, and Jaya Sil. A Metaheuristic Approach to Protein Structure Prediction: Algorithms and Insights from Fitness Landscape Analysis. Springer, 2018.

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Metaheuristic Approaches to Portfolio Optimization. IGI Global, 2020.

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Ray, Jhuma, Sadhan Kumar Dey, Goran Klepac, and Anirban Mukherjee. Metaheuristic Approaches to Portfolio Optimization. IGI Global, 2019.

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Jana, Nanda Dulal, Swagatam Das, and Jaya Sil. "Hybrid Metaheuristic Approach for Protein Structure Prediction." In Emergence, Complexity and Computation, 197–206. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74775-0_7.

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Pan, Hongqi, and Chung-Hsing Yeh. "A Metaheuristic Approach to Fuzzy Project Scheduling." In Lecture Notes in Computer Science, 1081–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45224-9_145.

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Yedurkar, Dhanalekshmi P., and Shilpa P. Metkar. "EEG Analysis Using Bio-Inspired Metaheuristic Approach." In Evolving Role of AI and IoMT in the Healthcare Market, 33–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82079-4_2.

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Kunche, Prajna, and K. V. V. S. Reddy. "Speech Enhancement Approach Based on Gravitational Search Algorithm (GSA)." In Metaheuristic Applications to Speech Enhancement, 61–75. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31683-3_6.

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Kunche, Prajna, and K. V. V. S. Reddy. "Speech Enhancement Approach Based on Accelerated Particle Swarm Optimization (APSO)." In Metaheuristic Applications to Speech Enhancement, 39–60. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31683-3_5.

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Lappas, P. Z., S. Z. Xanthopoulos, and A. N. Yannacopoulos. "Metaheuristic-Based Machine Learning Approach for Customer Segmentation." In Metaheuristics for Machine Learning, 101–33. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3888-7_4.

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Joseph, Caroline S., Vaishnavi Kini M, Sunad Suhas, Dherya Nagori, Arti Arya, and Pooja Agarwal. "Metaheuristic Approach for Optimizing Supply-Demand Algorithms." In 2022 IEEE 7th International conference for Convergence in Technology (I2CT). IEEE, 2022. http://dx.doi.org/10.1109/i2ct54291.2022.9824600.

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Mansor, Mohd Asyraf, Saratha Sathasivam, and Mohd Shareduwan Mohd Kasihmuddin. "Enhanced metaheuristic approach in pattern satisfiability problem." In PROCEEDING OF THE 25TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM25): Mathematical Sciences as the Core of Intellectual Excellence. Author(s), 2018. http://dx.doi.org/10.1063/1.5041557.

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Pal, Siddharth, Anniruddha Basak, Swagatam Das, Ajith Abraham, and Vaclav Snasel. "Automatic shell clustering using a metaheuristic approach." In 2010 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2010. http://dx.doi.org/10.1109/icsmc.2010.5641913.

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Zhang, Yuanyuan, and Ming Chen. "A Metaheuristic Approach for the Frequency Assignment Problem." In 2010 6th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2010. http://dx.doi.org/10.1109/wicom.2010.5600818.

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Adriaensen, Steven, Tim Brys, and Ann Nowe. "Designing reusable metaheuristic methods: A semi-automated approach." In 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900575.

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Hussein, Ahmed, Heba Mostafa, Mohamed Badrel-din, Osama Sultan, and Alaa Khamis. "Metaheuristic optimization approach to mobile robot path planning." In 2012 International Conference on Engineering and Technology (ICET). IEEE, 2012. http://dx.doi.org/10.1109/icengtechnol.2012.6396150.

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Shalchian, Hengameh, Mohammad-Hadi Sotoudeh, Habib G. Khosroshahi, Reza Ravanmehr, Surena Fatemi, and Hamed Altafi. "A metaheuristic approach for INO340 telescope flexible scheduling." In Software and Cyberinfrastructure for Astronomy VII, edited by Gianluca Chiozzi and Jorge Ibsen. SPIE, 2022. http://dx.doi.org/10.1117/12.2628899.

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Olin, Irwin D. Flat-Top Sector Beams Using Only Array Element Phase Weighting: A Metaheuristic Optimization Approach. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada569184.

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