Relevant bibliographies by topics / Metaheuristic

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  1. Journal articles
  2. Dissertations / Theses
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  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Metaheuristic'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2025

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Journal articles on the topic "Metaheuristic"

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Kusuma, Purba Daru, and Ashri Dinimaharawati. "TREBLE SEARCH OPTIMIZER: A STOCHASTIC OPTIMIZATION TO OVERCOME BOTH UNIMODAL AND MULTIMODAL PROBLEMS." IIUM Engineering Journal 24, no. 2 (July 4, 2023): 86–99. http://dx.doi.org/10.31436/iiumej.v24i2.2700.

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Today, many metaheuristics have used metaphors as their inspiration and baseline for novelty. It makes the novel strategy of these metaheuristics difficult to investigate. Moreover, many metaheuristics use high iteration or swarm size in their first introduction. Based on this consideration, this work proposes a new metaheuristic free from metaphor. This metaheuristic is called treble search optimizer (TSO), representing its main concept in performing three searches performed by each member in each iteration. These three searches consist of two directed searches and one random search. Several seeds are generated from each search. Then, these searches are compared with each other to find the best seed that might substitute the current corresponding member. TSO is also designed to overcome the optimization problem in the low iteration or swarm size circumstance. In this paper, TSO is challenged to overcome the 23 classic optimization functions. In this experiment, TSO is compared with five shortcoming metaheuristics: slime mould algorithm (SMA), hybrid pelican komodo algorithm (HPKA), mixed leader-based optimizer (MLBO), golden search optimizer (GSO), and total interaction algorithm (TIA). The result shows that TSO performs effectively and outperforms these five metaheuristics by making better fitness scores than SMA, HPKA, MLBO, GSO, and TIA in overcoming 21, 21, 23, 23, and 17 functions, consecutively. The result also indicates that TSO performs effectively in overcoming unimodal and multimodal problems in the low iteration and swarm size. ABSTRAK: Dewasa ini, terdapat ramai metaheuristik menggunakan metafora sebagai inspirasi dan garis dasar pembaharuan. Ini menyebabkan strategi baharu metaheuristik ini susah untuk dikaji. Tambahan, ramai metaheuristik menggunakan ulangan berulang atau saiz kerumunan dalam pengenalan mereka. Berdasarkan penilaian ini, kajian ini mencadangkan metaheuristk baharu bebas metafora. Metaheuristik ini dipanggil pengoptimum pencarian ganda tiga (TSO), mewakilkan konsep utama dalam pemilihan tiga pencarian yang dilakukan oleh setiap ahli dalam setiap ulangan. Ketiga-tiga carian ini terdiri daripada dua pencarian terarah dan satu pencarian rawak. Beberapa benih dihasilkan dalam setiap carian. Kemudian, carian ini dibandingkan antara satu sama lain bagi mencari benih terbaik yang mungkin berpotensi menggantikan ahli yang sedang digunakan. TSO juga direka bagi mengatasi masalah pengoptimuman dalam ulangan rendah atau lingkungan saiz kerumunan. Kajian ini TSO dicabar bagi mengatasi 23 fungsi pengoptimuman klasik. Eksperimen ini TSO dibandingkan dengan lima kekurangan metaheuristik: algoritma acuan lendir (SMA), algorithma hibrid komodo burung undan (HPKA), Pengoptimum Campuran berdasarkan-Ketua (MLBO), Pengoptimuman Carian Emas (GSO), dan algoritma jumlah interaksi (TIA). Dapatan kajian menunjukkan TSO berkesan menghasilkan dan lebih baik daripada kelima-lima metaheuristik dengan menghasilkan pemarkahan padanan terbaik berbanding SMA, HPKA, MLBO, GSO, dan TIA dalam mengatasi fungsi 21, 21, 23, 23, dan 17, secara berurutan. Dapatan kajian juga menunjukkan TSO turut berperanan efektif dalam mengatasi masalah modal tunggal dan modal ganda dalam iterasi rendah dan saiz kerumunan.
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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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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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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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Chicco, Gianfranco, and Andrea Mazza. "Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the ‘Rush to Heuristics’." Energies 13, no. 19 (September 30, 2020): 5097. http://dx.doi.org/10.3390/en13195097.

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In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods that are based on weak comparisons. This ‘rush to heuristics’ does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems and aims at providing a comprehensive view of the main issues that concern the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems.
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Bouhmala, N. "A Variable Depth Search Algorithm for Binary Constraint Satisfaction Problems." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/637809.

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The constraint satisfaction problem (CSP) is a popular used paradigm to model a wide spectrum of optimization problems in artificial intelligence. This paper presents a fast metaheuristic for solving binary constraint satisfaction problems. The method can be classified as a variable depth search metaheuristic combining a greedy local search using a self-adaptive weighting strategy on the constraint weights. Several metaheuristics have been developed in the past using various penalty weight mechanisms on the constraints. What distinguishes the proposed metaheuristic from those developed in the past is the update ofkvariables during each iteration when moving from one assignment of values to another. The benchmark is based on hard random constraint satisfaction problems enjoying several features that make them of a great theoretical and practical interest. The results show that the proposed metaheuristic is capable of solving hard unsolved problems that still remain a challenge for both complete and incomplete methods. In addition, the proposed metaheuristic is remarkably faster than all existing solvers when tested on previously solved instances. Finally, its distinctive feature contrary to other metaheuristics is the absence of parameter tuning making it highly suitable in practical scenarios.
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Zhang, Le, and Jinnan Wu. "A PSO-Based Hybrid Metaheuristic for Permutation Flowshop Scheduling Problems." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/902950.

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This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature.
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Crawford, Broderick, Ricardo Soto, José Lemus-Romani, Marcelo Becerra-Rozas, José M. Lanza-Gutiérrez, Nuria Caballé, Mauricio Castillo, et al. "Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics." Mathematics 9, no. 16 (August 4, 2021): 1839. http://dx.doi.org/10.3390/math9161839.

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One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.
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Omran, Mahamed G., and Andries Engelbrecht. "Time Complexity of Population-Based Metaheuristics." MENDEL 29, no. 2 (December 20, 2023): 255–60. http://dx.doi.org/10.13164/mendel.2023.2.255.

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This paper is a brief guide aimed at evaluating the time complexity of metaheuristic algorithms both mathematically and empirically. Starting with the mathematical foundational principles of time complexity analysis, key notations and fundamental concepts necessary for computing the time efficiency of a metaheuristic are introduced. The paper then applies these principles on three well-known metaheuristics, i.e. differential evolution, harmony search and the firefly algorithm. A procedure for the empirical analysis of metaheuristics' time efficiency is then presented. The procedure is then used to empirically analyze the computational cost of the three aforementioned metaheuristics. The pros and cons of the two approaches, i.e. mathematical and empirical analysis, are discussed.
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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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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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Dissertations / Theses on the topic "Metaheuristic"

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Auer, Jens. "Metaheuristic Multiple Sequence Alignment Optimisation." Thesis, University of Skövde, School of Humanities and Informatics, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-899.

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The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restarting

a local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA. After that, ILS will be evaluated and compared to other MSA algorithms by BAliBASE (Thomson et al., 1999), a set of manually refined alignments used in most recent publications of algorithms and in at least two MSA algorithm surveys. The runtime-behaviour will be evaluated using runtime-distributions.

The quality of alignments produced by ILS is at least as good as the best algorithms available and significantly superiour to previously published Metaheuristics for MSA, Tabu Search and Genetic Algorithm (SAGA). On the average, ILS performed best in five out of eight test cases, second for one test set and third for the remaining two. A drawback of all iterative methods for MSA is the long runtime needed to produce good alignments. ILS needs considerably less runtime than Tabu Search and SAGA, but can not compete with progressive or consistency based methods, e. g. ClustalW or T-COFFEE.

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Clark, John A. "Metaheuristic search as a cryptological tool." Thesis, University of York, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.247755.

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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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Landa, Silva Jesus Dario. "Metaheuristic and multiobjective approaches for space allocation." Thesis, University of Nottingham, 2003. http://eprints.nottingham.ac.uk/10147/.

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This thesis presents an investigation on the application of metaheuristic techniques to tackle the space allocation problem in academic institutions. This is a combinatorial optimisation problem which refers to the distribution of the available room space among a set of entities (staff, research students, computer rooms, etc.) in such a way that the space is utilised as efficiently as possible and the additional constraints are satisfied as much as possible. The literature on the application of optimisation techniques to approach the problem mentioned above is scarce. This thesis provides a description and formulation of the problem. It also proposes and compares a range of heuristics for the initialisation of solutions and for neighbourhood exploration. Four well-known metaheuristics (iterative improvement, simulated annealing, tabu search and genetic algorithms) are adapted and tuned for their application to the problem investigated here. The performance of these techniques is assessed and benchmark results are obtained. Also, hybrid approaches are designed that produce sets of high quality and diverse solutions in much shorter time than those required by space administrators who construct solutions manually. The hybrid approaches are also adapted to tackle the space allocation problem from a two-objective perspective. It is also revealed that the use of aggregating functions or relaxed dominance to evaluate solutions in Pareto optimisation, can be more beneficial than the standard dominance relation to enhance the performance of some multiobjective optimisers in some problem domains. A range of single-solution metaheuristics are extended to create hybrid evolutionary approaches based on the scheme of cooperative local search. This scheme promotes the cooperation of a population of local searchers by means of mechanisms to share the information gained during the search. This thesis also reports the best results known so far for a set of test instances of the space allocation problem in academic institutions. This thesis pioneers the application of metaheuristics to solve the space allocation problem. The major contributions are: provides a formulation of the problem together with tests data sets, reports the best known results for these test instances, investigates the multiobjective nature of the problem and proposes a new form of hybridising metaheuristics.
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Lara, Garazi Zabalo Manrique de. "Metaheuristic Algorithms for Transportation Problems in HealthCare." Doctoral thesis, Università di Siena, 2018. http://hdl.handle.net/11365/1050844.

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One of the most challenging problems in healthcare systems nowadays is the one of satisfying all the demand while delivering a high-quality service with very limited resources. That is why optimization problems in healthcare have been the subject of many research studies. During recent years transportation problems in particular have gathered a lot of attention. Healthcare transportation problems can be divided in two main groups, patient transportation and the transportation of goods, such as biological samples. Patient transportation is an important issue in healthcare systems, both for emergency and non-emergency transportation. Non-emergency transportations contain for example, the transfer of patients between hospitals, the transportation between patients’ homes and medical structures and the transportation to nursing homes and rehabilitation centers. The complexity of the above mentioned problems, the many user related constraints and the limited resources make these types of problems very challenging. That is why OR and in particular optimization algorithms have become a useful tool to solve these problems. Pickup and delivery problems (PDP) are a variant of vehicle routing problems (VRP), where a number of loads have to be transported from pickup locations to delivery locations with the aim of finding a routing for a fleet of available vehicles that minimizes the overall routing cost. Each available vehicle has a given capacity and is located in a depot, where it has to return at the end of the service. Each request is characterized by the size of the load and by the location where it has to be picked up (pickup location) and the location where it has to be dropped off (delivery location). Dial-a-ride problem (DARP) is a generalization of the Pickup and Delivery Problem with Time Windows (PDPTW). In DARP, people are transported instead of goods and consequently issues on the quality of the provided service and timing must be carefully taken into account (through additional constraints or by extra terms in the objective function). In this thesis different metaheuristic algorithms are proposed to solve transportation problems in healthcare. Two real-life transportation problems are presented one focusing on the on-demand patient transportation and the other focused on the collection and transportation of biological samples. The thesis tackles the unforeseen constraints that arise when adapting pickup and delivery (PDP) problems to real scenarios. These unforeseen constraints include the user’s preferences, complex cost functions, user’s quality service for the patient transportation problem and the possibility of transfers for the biological sample transportation problem. The first addressed problem is a multi-depot dial-a-ride problem arising from a real-world healthcare application, concerning the non-emergency transportation of patients in the Italian region of Tuscany. Different versions of Variable Neighborhood Search (VNS) algorithms have been created able to tackle all the characteristics of the problem. The computational results obtained by testing the VNS algorithms on literature instances and on random instances taken from a real-life healthcare problem show the effectiveness of the proposed approaches. Finally, the last problem deals with a multi-depot pickup and delivery problem with transfers that arises from a real-world healthcare application, the blood and biological sample transportation in the Metropolitan Area of Bologna, an Italian city. The proposed Adaptive Large Neighborhood Search algorithm is able to tackle all the characteristics of the problem. Computational results on real-life instances show the effectiveness of the proposed approach, in quality of the solution as well as in the distribution of the vehicles to the hospital, compared to the current real situation of the HUB, the main hospital of Bologna.
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Fan, Lang. "Metaheuristic methods for the urban transit routing problem." Thesis, Cardiff University, 2009. http://orca.cf.ac.uk/54237/.

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In our research, we focus on these three issues, and concentrate on developing a metaheuristic framework for solving the UTRP.  Embedding simple heuristic algorithms (hill-climbing and simulated annealing) within this framework, we have beaten previously best published results for Mandi’s benchmark problem, which is the only generally available data set.  Due to the lack of “standard models” for the UTRP, and a shortage of benchmark data it is difficult for researchers to compare their approaches.  Thus we introduce a simplified model and implement a data set generation program to produce realistic test data sets much larger than Mandi’s problem.  Furthermore, some Lower Bounds and necessary constraints of the UTRP are also researched, which we use to help validate the quality of our results, particularly those obtained for our new data sets. Finally, a multi-objective optimisation algorithm is designed to solve our urban transit routing problem in which the operator’s cost is modelled in addition to passenger quality of service.
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Yagiura, Mutsunori. "Studies on Metaheuristic Algorithms for Combinatorial Optimization Problems." Kyoto University, 1999. http://hdl.handle.net/2433/157060.

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本文データは平成22年度国立国会図書館の学位論文(博士)のデジタル化実施により作成された画像ファイルを基にpdf変換したものである
Kyoto University (京都大学)
0048
新制・論文博士
博士(工学)
乙第10101号
論工博第3416号
新制||工||1146(附属図書館)
UT51-99-G578
(主査)教授 茨木 俊秀, 教授 岩間 一雄, 教授 加藤 直樹
学位規則第4条第2項該当
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Yang, Yulian. "Metaheuristic based peer rewiring for semantic overlay networks." Thesis, Lyon, INSA, 2014. http://www.theses.fr/2014ISAL0036/document.

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Nous considérons une plate-forme pair-à-pair pour la Recherche d'Information (RI) collaborative. Chaque pair héberge une collection de documents textuels qui traitent de ses sujets d'intérêt. En l'absence d'un mécanisme d'indexation global, les pairs indexent localement leurs documents et s'associent pour fournir un service distribué de réponse à des requêtes. Notre objectif est de concevoir un protocole décentralisé qui permette aux pairs de collaborer afin de transmettre une requête depuis son émetteur jusqu'aux pairs en possession de documents pertinents. Les réseaux logiques sémantiques (Semantic Overlay Networks, SON) représentent la solution de référence de l'état de l'art. Les pairs qui possèdent des ressources sémantiques similaires sont regroupés en clusters. Les opérations de RI seront alors efficaces puisqu'une requête sera transmise aux clusters de pairs qui hébergent les ressources pertinentes. La plupart des approches actuelles consistent en une reconfiguration dynamique du réseau de pairs (peer rewiring). Pour ce faire, chaque pair exécute périodiquement un algorithme de marche aléatoire ou gloutonne sur le réseau pair-à-pair afin de renouveler les pairs de son cluster. Ainsi, un réseau à la structure initialement aléatoire évolue progressivement vers un réseau logique sémantique. Jusqu'à présent, les approches existantes n'ont pas considéré que l'évolution de la topologie du réseau puisse influer sur les performances de l'algorithme de reconfiguration dynamique du réseau. Cependant, s'il est vrai que, pour une configuration initiale aléatoire des pairs, une marche aléatoire sera efficace pour découvrir les pairs similaires, lorsque des clusters commencent à émerger une approche gloutonne devient alors mieux adaptée. Ainsi, nous proposons une stratégie qui applique un algorithme de recuit simulé (Simulated Annealing, SA) afin de faire évoluer une stratégie de marche aléatoire vers une stratégie gloutonne lors de la construction du SON. Cette thèse contient plusieurs avancées concernant l'état de l'art dans ce domaine. D'abbord, nous modélisions formellement la reconfiguration dynamique d'un réseau en un SON. Nous identifions un schéma générique pour la reconfiguration d'un réseau pair-à-pair, et après le formalisons en une procédure constituée de trois étapes. Ce framework cohérent offre à ses utilisateurs de quoi le paramétrer. Ensuite, le problème de la construction d'un SON est modélisé sous la forme d'un problème d'optimisation combinatoire pour lequel les opérations de reconfiguration du réseau correspondent à la recherche décentralisée d'une solution locale. Fondée sur ce modèle, une solution concrète à base de recuit simulé est proposée. Nous menons une étude expérimentale poussée sur la construction du SON et la RI sur SONs, et validions notre approche
A Peer-to-Peer (P2P) platform is considered for collaborative Information Retrieval (IR). Each peer hosts a collection of text documents with subjects related to its owner's interests. Without a global indexing mechanism, peers locally index their documents, and provide the service to answer queries. A decentralized protocol is designed, enabling the peers to collaboratively forward queries from the initiator to the peers with relevant documents. Semantic Overlay Network (SONs) is one the state of the art solutions, where peers with semantically similar resources are clustered. IR is efficiently performed by forwarding queries to the relevant peer clusters in an informed way. SONs are built and maintained mainly via peer rewiring. Specifically, each peer periodically sends walkers to its neighborhood. The walkers walk along peer connections, aiming at discovering more similar peers to replace less similar neighbors of its initiator. The P2P network then gradually evolves from a random overlay network to a SON. Random and greedy walk can be applied individually or integrated in peer rewiring as a constant strategy during the progress of network evolution. However, the evolution of the network topology may affect their performance. For example, when peers are randomly connected with each other, random walk performs better than greedy walk for exploring similar peers. But as peer clusters gradually emerge in the network, a walker can explore more similar peers by following a greedy strategy. This thesis proposes an evolving walking strategy based on Simulated Annealing (SA), which evolves from a random walk to a greedy walk along the progress of network evolution. According to the simulation results, SA-based strategy outperforms current approaches, both in the efficiency to build a SON and the effectiveness of the subsequent IR. This thesis contains several advancements with respect to the state of the art in this field. First of all, we identify a generic peer rewiring pattern and formalize it as a three-step procedure. Our technique provides a consistent framework for peer rewiring, while allowing enough flexibility for the users/designers to specify its properties. Secondly, we formalize SON construction as a combinatorial optimization problem, with peer rewiring as its decentralized local search solution. Based on this model, we propose a novel SA-based approach to peer rewiring. Our approach is validated via an extensive experimental study on the effect of network wiring on (1) SON building and (2) IR in SONs
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Yang, Y. "Metaheuristic based Peer Rewiring for Semantic Overlay Networks." Doctoral thesis, Università degli Studi di Milano, 2014. http://hdl.handle.net/2434/236979.

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A Peer-to-Peer (P2P) platform is considered for collaborative Information Retrieval (IR). Each peer hosts a collection of text documents with subjects related to its owner's interests. Without a global indexing mechanism, peers locally index their documents, and provide the service to answer queries. A decentralized protocol is designed, enabling the peers to collaboratively forward queries from the initiator to the peers with relevant documents. Semantic Overlay Network (SON) is one of the state-of-the-art solutions, where peers with semantically similar resources are clustered. IR can then be efficiently performed by forwarding queries to the relevant peer clusters in an informed way. SONs are built and maintained mainly via peer rewiring. Specifically, each peer periodically sends walkers to its neighborhood. The walkers walk along peer connections, aiming at discovering more similar peers to replace less similar neighbors of its initiator. The P2P network hence gradually evolves from a random overlay network to a SON. Random and greedy walk can be applied individually or integrated in peer rewiring as a constant strategy during the progress of network evolution. However, the evolution of the network topology may affect their performance. For example, when peers are randomly connected with each other, random walk performs better than greedy walk for exploring similar peers. But as peer clusters gradually emerge in the network, a walker can explore more similar peers by following a greedy strategy. This thesis proposes an evolving walking strategy based on Simulated Annealing (SA), which evolves from a random walk to a greedy walk along the progress of network evolution. According to the simulation results, SA-based strategy outperforms current approaches, both in the efficiency to build a SON and the effectiveness of the subsequent IR. This thesis contains several advancements with respect to the state-of-the-art in this field. First of all, we identify a generic peer rewiring pattern and formalize it as a three-step procedure. Our technique provides a consistent framework for peer rewiring, while allowing enough flexibility for the users/designers to specify its properties. Secondly, we formalize SON construction as a combinatorial optimization problem, with peer rewiring as its decentralized local search solution. Based on this model, we propose a novel SA-based approach to peer rewiring. Our approach is validated via an extensive experimental study on the effect of network rewiring on (i) SON building and (ii) IR in SONs.
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Joubert, Johannes Wilhelm. "An integrated and intelligent metaheuristic for constrained vehicle routing." Pretoria : [s.n.], 2006. http://upetd.up.ac.za/thesis/available/etd-07202007-175138.

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Books on the topic "Metaheuristic"

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1968-, Abraham Ajith, and Konar Amit, eds. Metaheuristic clustering. Berlin: Springer, 2009.

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Zäpfel, Günther, Roland Braune, and Michael Bögl. Metaheuristic Search Concepts. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11343-7.

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Shah, Pritesh, Ravi Sekhar, Anand J. Kulkarni, and Patrick Siarry. Metaheuristic Algorithms in Industry 4.0. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003143505.

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Kunche, Prajna, and K. V. V. S. Reddy. Metaheuristic Applications to Speech Enhancement. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31683-3.

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Cuevas, Erik, Primitivo Diaz, and Octavio Camarena. Metaheuristic Computation: A Performance Perspective. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-58100-8.

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Alba, Enrique, and Rafael Martí, eds. Metaheuristic Procedures for Training Neutral Networks. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/0-387-33416-5.

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Sharda, Ramesh, Stefan Voß, César Rego, and Bahram Alidaee, eds. Metaheuristic Optimization via Memory and Evolution. Boston: Kluwer Academic Publishers, 2005. http://dx.doi.org/10.1007/b102147.

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Cuevas, Erik, Alma Rodríguez, Avelina Alejo-Reyes, and Carolina Del-Valle-Soto. Recent Metaheuristic Computation Schemes in Engineering. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66007-9.

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Cuevas, Erik, Daniel Zaldívar, and Marco Pérez-Cisneros. New Metaheuristic Schemes: Mechanisms and Applications. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-45561-2.

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Jana, Nanda Dulal, Swagatam Das, and Jaya Sil. A Metaheuristic Approach to Protein Structure Prediction. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74775-0.

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Book chapters on the topic "Metaheuristic"

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Fürnkranz, Johannes, Philip K. Chan, Susan Craw, Claude Sammut, William Uther, Adwait Ratnaparkhi, Xin Jin, et al. "Metaheuristic." In Encyclopedia of Machine Learning, 662. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_537.

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Dorigo, Marco, Mauro Birattari, and Thomas Stützle. "Metaheuristic." In Encyclopedia of Machine Learning and Data Mining, 817–18. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_537.

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Patel, Vivek K., Vimal J. Savsani, and Mohamed A. Tawhid. "Metaheuristic Methods." In Thermal System Optimization, 7–32. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10477-1_2.

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Raidl, Günther R., Jakob Puchinger, and Christian Blum. "Metaheuristic Hybrids." In Handbook of Metaheuristics, 469–96. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1665-5_16.

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Agarwal, Anurag, Selcuk Colak, and Selcuk Erenguc. "Metaheuristic Methods." In Handbook on Project Management and Scheduling Vol.1, 57–74. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05443-8_4.

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Raidl, Günther R., Jakob Puchinger, and Christian Blum. "Metaheuristic Hybrids." In Handbook of Metaheuristics, 385–417. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91086-4_12.

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Affenzeller, Michael, Andreas Beham, Monika Kofler, Gabriel Kronberger, Stefan A. Wagner, and Stephan Winkler. "Metaheuristic Optimization." In Hagenberg Research, 103–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-02127-5_4.

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Oliva, Diego, Mohamed Abd Elaziz, and Salvador Hinojosa. "Metaheuristic Optimization." In Metaheuristic Algorithms for Image Segmentation: Theory and Applications, 13–26. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12931-6_3.

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Aguiar e Oliveira Junior, Hime, Lester Ingber, Antonio Petraglia, Mariane Rembold Petraglia, and Maria Augusta Soares Machado. "Metaheuristic Methods." In Intelligent Systems Reference Library, 21–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27479-4_3.

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Bandaru, Sunith, and Kalyanmoy Deb. "Metaheuristic Techniques." In Decision Sciences, 693–750. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2016. http://dx.doi.org/10.1201/9781315183176-12.

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Conference papers on the topic "Metaheuristic"

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Ahmed, Bestoun S. "An Adaptive Metaheuristic Framework for Changing Environments." In 2024 IEEE Congress on Evolutionary Computation (CEC), 1–10. IEEE, 2024. http://dx.doi.org/10.1109/cec60901.2024.10611806.

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Abreu, Bruno T. de, Eliane Martins, and Fabiano L. de Sousa. "Automatic test data generation for path testing using a new stochastic algorithm." In Simpósio Brasileiro de Engenharia de Software. Sociedade Brasileira de Computação, 2005. http://dx.doi.org/10.5753/sbes.2005.23823.

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Software testing is an important activity of the software development process, and automate test data generation contributes to reduce cost and time efforts. Path testing is a complex problem and metaheuristics have been proposed to deal with it. In this paper, an initial assessment of the efficacy of a recently proposed metaheuristic, the Generalized Extremal Optimization (GEO), in dealing with such kind of problem is made. Results show that it can be very competitive with the frequently used Genetic Algorithm (GA) metaheuristic, while requiring a much lesser effort in parameter setting, making it an alternative to such method in path testing.
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Fadhil, Heba. "Metaheuristic Algorithms in Optimization and its Application: A Review." In The 3rd International Conference On Engineering And Innovative Technology. Salahaddin University-Erbil, 2025. https://doi.org/10.31972/iceit2024.013.

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Metaheuristic algorithms are an intelligent way of thinking and working developed for resolving diverse issues about optimization. The number of potential solutions for such problems often is too large to be properly analyzed using standard procedures; thus, these algorithms are highly flexible and can be useful in many cases where needed to predict different types of optimizations accurately. Metaheuristics take inspiration from several natural processes like evolution or animal behavior, which allow them to show strength without being specific only towards one area. Some Metaheuristics algorithms are commonly being used like : Genetic Algorithm (GA), Simulated Annealing (SA), Evolutionary Algorithm (EA), Tabu Search (TS), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), and Cuckoo Search Approach (CSA). All of them derives from this initial set of solutions and employ heuristics to get from this set of solutions.. The objective of this paper is to thoroughly analyze different metaheuristic algorithms. Their principles, mechanisms and the area where they are applied and will delve into. This paper provides a qualitative analysis of these algorithmic performances in diverse settings that underscore their strong suits as well as their weaknesses. The discourse also makes mention of some specific examples like how metaheuristic algorithms find utility application in various fields which include but are not limited to engineering or computer science, even economics and healthcare later down the line receive due consideration with an eye towards specific results; showing not only how effective these individual algorithms can be when applied under differing scenarios but also pointing out areas deserving further research efforts be directed onto them.
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Nepomuceno, Lucas Santiago, Gabriel Schreider Silva, Edimar Jose Oliveira, Arthur Neves Paula, and Edmarcio Antonio Belati. "The Nomadic People Optimizer applied to the economic dispatch problem with prohibited operating zones." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-112.

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This work proposes the application of the Nomadic People Optimizer (NPO) to solve the economic dispatch problem considering Prohibitive Operating Zones (POZ). The NPO is a swarm-based metaheuristic recently introduced in the literature and still under-explored. In addition, the POZ increase the difficulties to find the optimal solution of the economic dispatch problem. The performance of the proposed methodology is compared with others metaheuristics present in the literature. Also, a sensibility analysis was performed. The NPO performed better than Ant Colony Optimization (ACO) and Whale Optimization Algorithm (WOA) metaheuristics in solving the problem.
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Aljawawdeh, Hamzeh J., Christopher L. Simons, and Mohammed Odeh. "Metaheuristic Design Pattern." In GECCO '15: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2739482.2768498.

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Brownlee, Alexander E. I., John R. Woodward, and Jerry Swan. "Metaheuristic Design Pattern." In GECCO '15: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2739482.2768499.

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Shackelford, Mark R. N., and Christopher L. Simons. "Metaheuristic design pattern." In GECCO '14: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2598394.2609849.

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Krawiec, Krzysztof. "Metaheuristic design pattern." In GECCO '14: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2598394.2609847.

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Singh, Manjinder, Alexander E. I. Brownlee, and David Cairns. "Towards explainable metaheuristic." In GECCO '22: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3520304.3533966.

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Klimeš, L., L. Kozubík, and P. Charvát. "Computational Design Optimization of PCM-Based Attenuator of Fluid Temperature Fluctuations." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-10381.

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Abstract A fluid-PCM heat exchanger (attenuator) of a circular design with water as the fluid was investigated both numerically and experimentally. A computational model of the PCM-based attenuator was developed with the use of the control volume method and the effective heat capacity. Square wave fluctuations of the water temperature at the inlet of the attenuator were considered in the study. The model and its functionality was validated by means of experimental data. The experimental investigation was carried out in a lab environment and two tanks containing water of different temperatures with the computer-controlled mixing valve were used to simulate square wave temperature fluctuations. The validated model was then coupled with metaheuristic optimization methods. The bee algorithm, the genetic algorithm, and the particle swarm optimization algorithm were applied in the study. Design optimization of the attenuator was performed with the aim to maximize the attenuation capability of the attenuator, but considering a cost factor as well. Results indicated that the metaheuristic approach represents a viable way for the solution of this kind of problems. All three metaheuristics provided comparable results in terms of the value of objective function as well as of the computational efficiency.
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Reports on the topic "Metaheuristic"

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Variansyah, Ilham, Jin Whan Bae, Benjamin R. Betzler, and Germina Ilas. Metaheuristic Optimization Tool. Office of Scientific and Technical Information (OSTI), March 2020. http://dx.doi.org/10.2172/1608209.

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YANG, Xin-She. Metaheuristic Optimization and Geophysical Inversion. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0254.

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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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Fleischer, Mark. The Measure of Pareto Optima: Applications to Multiobjective Metaheuristics. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada441037.

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