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Journal articles on the topic 'Meta-heuristics'

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Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Hey, Spencer Phillips. "Heuristics and Meta-heuristics in Scientific Judgement." British Journal for the Philosophy of Science 67, no. 2 (June 1, 2016): 471–95. http://dx.doi.org/10.1093/bjps/axu045.

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2

Yasuda, Keiichiro, and Takaaki Nagaoka. "Multipoint Search Meta-Heuristics." Proceedings of Design & Systems Conference 2003.13 (2003): 128–29. http://dx.doi.org/10.1299/jsmedsd.2003.13.128.

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3

Ariyaratne, M. K. A., and R. M. Silva. "Meta-heuristics meet sports: a systematic review from the viewpoint of nature inspired algorithms." International Journal of Computer Science in Sport 21, no. 1 (March 1, 2022): 49–92. http://dx.doi.org/10.2478/ijcss-2022-0003.

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Abstract This review explores the avenues for the application of meta-heuristics in sports. The necessity of sophisticated algorithms to investigate different NP hard problems encountered in sports analytics was established in the recent past. Meta-heuristics have been applied as a promising approach to such problems. We identified team selection, optimal lineups, sports equipment optimization, scheduling and ranking, performance analysis, predictions in sports, and player tracking as seven major categories where meta-heuristics were implemented in research in sports. Some of our findings include (a) genetic algorithm and particle swarm optimization have been extensively used in the literature, (b) meta-heuristics have been widely applied in the sports of cricket and soccer, (c) the limitations and challenges of using meta-heuristics in sports. Through awareness and discussion on implementation of meta-heuristics, sports analytics research can be rich in the future.

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4

Gursoy, Arif, Mehmet Kurt, Hakan Kutucu, and Urfat Nuriyev. "New heuristics and meta-heuristics for the Bandpass problem." Engineering Science and Technology, an International Journal 20, no. 6 (December 2017): 1531–39. http://dx.doi.org/10.1016/j.jestch.2017.12.004.

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5

Santos, André S., Ana M. Madureira, and Leonilde R. Varela. "A Self-Parametrization Framework for Meta-Heuristics." Mathematics 10, no. 3 (February 1, 2022): 475. http://dx.doi.org/10.3390/math10030475.

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Even while the scientific community has shown great interest in the analysis of meta-heuristics, the analysis of their parameterization has received little attention. It is the parameterization that will adapt a meta-heuristic to a problem, but it is still performed, mostly, empirically. There are multiple parameterization techniques; however, they are time-consuming, requiring considerable computational effort and they do not take advantage of the meta-heuristics that they parameterize. In order to approach the parameterization of meta-heuristics, in this paper, a self-parameterization framework is proposed. It will automatize the parameterization as an optimization problem, precluding the user from spending too much time on parameterization. The model will automate the parameterization through two meta-heuristics: A meta-heuristic of the solution space and one of the parameter space. To analyze the performance of the framework, a self-parameterization prototype was implemented. The prototype was compared and analyzed in a SP (scheduling problem) and in the TSP (traveling salesman problem). In the SP, the prototype found better solutions than those of the manually parameterized meta-heuristics, although the differences were not statistically significant. In the TSP, the self-parameterization prototype was more effective than the manually parameterized meta-heuristics, this time with statistically significant differences.

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6

El-Henawy, Ibrahim, and Nagham Ahmed. "Meta-Heuristics Algorithms: A Survey." International Journal of Computer Applications 179, no. 22 (February 15, 2018): 45–54. http://dx.doi.org/10.5120/ijca2018916427.

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7

Proll, L., I. H. Osman, and J. P. Kelly. "Meta-Heuristics Theory and Applications." Journal of the Operational Research Society 48, no. 6 (June 1997): 657. http://dx.doi.org/10.2307/3010233.

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8

Zaki, Shereen, and Abd El-Nasser H. Zaied. "Meta-heuristics Algorithms: A survey." International Journal of Engineering Trends and Technology 67, no. 5 (May 25, 2019): 67–74. http://dx.doi.org/10.14445/22315381/ijett-v67i5p210.

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9

Joshi, Susheel Kumar, and Jagdish Chand Bansal. "Parameter tuning for meta-heuristics." Knowledge-Based Systems 189 (February 2020): 105094. http://dx.doi.org/10.1016/j.knosys.2019.105094.

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10

Osman, I. H., and J. P. Kelly. "Meta-Heuristics Theory and Applications." Journal of the Operational Research Society 48, no. 6 (June 1997): 657. http://dx.doi.org/10.1057/palgrave.jors.2600781.

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11

Osman, I. H., and J. P. Kelly. "Meta-Heuristics Theory and Applications." Journal of the Operational Research Society 48, no. 6 (1997): 657. http://dx.doi.org/10.1038/sj.jors.2600781.

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12

de Carvalho, Vinicius Renan, Ender Özcan, and Jaime Simão Sichman. "Comparative Analysis of Selection Hyper-Heuristics for Real-World Multi-Objective Optimization Problems." Applied Sciences 11, no. 19 (October 1, 2021): 9153. http://dx.doi.org/10.3390/app11199153.

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As exact algorithms are unfeasible to solve real optimization problems, due to their computational complexity, meta-heuristics are usually used to solve them. However, choosing a meta-heuristic to solve a particular optimization problem is a non-trivial task, and often requires a time-consuming trial and error process. Hyper-heuristics, which are heuristics to choose heuristics, have been proposed as a means to both simplify and improve algorithm selection or configuration for optimization problems. This paper novel presents a novel cross-domain evaluation for multi-objective optimization: we investigate how four state-of-the-art online hyper-heuristics with different characteristics perform in order to find solutions for eighteen real-world multi-objective optimization problems. These hyper-heuristics were designed in previous studies and tackle the algorithm selection problem from different perspectives: Election-Based, based on Reinforcement Learning and based on a mathematical function. All studied hyper-heuristics control a set of five Multi-Objective Evolutionary Algorithms (MOEAs) as Low-Level (meta-)Heuristics (LLHs) while finding solutions for the optimization problem. To our knowledge, this work is the first to deal conjointly with the following issues: (i) selection of meta-heuristics instead of simple operators (ii) focus on multi-objective optimization problems, (iii) experiments on real world problems and not just function benchmarks. In our experiments, we computed, for each algorithm execution, Hypervolume and IGD+ and compared the results considering the Kruskal–Wallis statistical test. Furthermore, we ranked all the tested algorithms considering three different Friedman Rankings to summarize the cross-domain analysis. Our results showed that hyper-heuristics have a better cross-domain performance than single meta-heuristics, which makes them excellent candidates for solving new multi-objective optimization problems.

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13

Martı́, Rafael, and Manuel Laguna. "Heuristics and meta-heuristics for 2-layer straight line crossing minimization." Discrete Applied Mathematics 127, no. 3 (May 2003): 665–78. http://dx.doi.org/10.1016/s0166-218x(02)00397-9.

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14

Sridharan, Seshadri, Ramesh Kumar Subramanian, and Arun Kumar Srirangan. "Physics based meta heuristics in manufacturing." Materials Today: Proceedings 39 (2021): 805–11. http://dx.doi.org/10.1016/j.matpr.2020.09.775.

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15

Mohamed, J. Sheik, S. Ramakrishna, and P. Chittibabu. "Approaches of Meta Heuristics Optimization Techniques." i-manager's Journal on Computer Science 1, no. 4 (February 15, 2014): 32–40. http://dx.doi.org/10.26634/jcom.1.4.2717.

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16

Osman, Ibrahim H. "Focused issue on applied meta-heuristics." Computers & Industrial Engineering 44, no. 2 (February 2003): 205–7. http://dx.doi.org/10.1016/s0360-8352(02)00175-4.

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17

Ragnarsson, Ragnar Magnús, Hlynur Stefánsson, and Eyjólfur Ingi Ásgeirsson. "Meta-Heuristics in Multi-Core Environments." Systems Engineering Procedia 1 (2011): 457–64. http://dx.doi.org/10.1016/j.sepro.2011.08.067.

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18

Diao, Ren, and Qiang Shen. "Nature inspired feature selection meta-heuristics." Artificial Intelligence Review 44, no. 3 (January 14, 2015): 311–40. http://dx.doi.org/10.1007/s10462-015-9428-8.

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19

Türk, Stefan. "Network migration optimization using meta-heuristics." AEU - International Journal of Electronics and Communications 68, no. 7 (July 2014): 584–86. http://dx.doi.org/10.1016/j.aeue.2014.04.005.

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20

Urli, Tommaso. "Hybrid meta-heuristics for combinatorial optimization." Constraints 20, no. 4 (September 23, 2015): 473. http://dx.doi.org/10.1007/s10601-015-9209-7.

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21

Ma, Zhenfang, Kaizhou Gao, Hui Yu, and Naiqi Wu. "Solving Heterogeneous USV Scheduling Problems by Problem-Specific Knowledge Based Meta-Heuristics with Q-Learning." Mathematics 12, no. 2 (January 19, 2024): 339. http://dx.doi.org/10.3390/math12020339.

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This study focuses on the scheduling problem of heterogeneous unmanned surface vehicles (USVs) with obstacle avoidance pretreatment. The goal is to minimize the overall maximum completion time of USVs. First, we develop a mathematical model for the problem. Second, with obstacles, an A* algorithm is employed to generate a path between two points where tasks need to be performed. Third, three meta-heuristics, i.e., simulated annealing (SA), genetic algorithm (GA), and harmony search (HS), are employed and improved to solve the problems. Based on problem-specific knowledge, nine local search operators are designed to improve the performance of the proposed algorithms. In each iteration, three Q-learning strategies are used to select high-quality local search operators. We aim to improve the performance of meta-heuristics by using Q-learning-based local search operators. Finally, 13 instances with different scales are adopted to validate the effectiveness of the proposed strategies. We compare with the classical meta-heuristics and the existing meta-heuristics. The proposed meta-heuristics with Q-learning are overall better than the compared ones. The results and comparisons show that HS with the second Q-learning, HS + QL2, exhibits the strongest competitiveness (the smallest mean rank value 1.00) among 15 algorithms.

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Luna Gutierrez, Ricardo, and Matteo Leonetti. "Meta Reinforcement Learning for Heuristic Planing." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 551–59. http://dx.doi.org/10.1609/icaps.v31i1.16003.

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Heuristic planning has a central role in classical planning applications and competitions. Thanks to this success, there has been an increasing interest in using Deep Learning to create high-quality heuristics in a supervised fashion, learning from optimal solutions of previously solved planning problems. Meta-Reinforcement learning is a fast growing research area concerned with learning, from many tasks, behaviours that can quickly generalize to new tasks from the same distribution of the training ones. We make a connection between meta-reinforcement learning and heuristic planning, showing that heuristic functions meta-learned from planning problems, in a given domain, can outperform both popular domain-independent heuristics, and heuristics learned by supervised learning. Furthermore, while most supervised learning algorithms rely on ad-hoc encodings of the state representation, our method uses as input a general PDDL 3.1 description. We evaluated our heuristic with an A* planner on six domains from the International Planning Competition and the FF Domain Collection, showing that the meta-learned heuristic leads to the expansion, on average, of fewer states than three popular heuristics used by the FastDownward planner, and a supervised-learned heuristic.

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23

Masrom, Suraya, Abdullah Sani Abd Rahman, Nasiroh Omar, and Suriani Rapa’ee. "PSO-GAScript: A Domain-specific Scripting Language for Meta-heuristics Algorithm." International Journal of Emerging Technology and Advanced Engineering 12, no. 7 (July 4, 2022): 86–93. http://dx.doi.org/10.46338/ijetae0722_09.

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PSO-GAScript is a domain-specific scripting language designed to support easy and rapid implementation of meta-heuristics algorithms focused on Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The programming language has been developed to allow the hybridization of the two meta-heuristics algorithms. Hybridizations between PSO and GA are proven to be a comprehensive tool for solving different kinds of optimization problems. Moreover, the two algorithms have achieved a remarkable improvement from the adaptation of dynamic parameterization. Nevertheless, implementing the suitable hybrid algorithms is a considerably difficult, which in most cases is time consuming. To the best of our knowledge, the existing tools are not adequately designed to enable users to easily develop the meta-heuristics hybridization of PSO-GA with dynamic parameterizations. This paper presents the fundamental research methodology of domain-specific scripting language from the language design, constructs and evaluations focused on the case of PSO-GA hybridization and dynamic parameterizations. The PSO-GAScript are shown to easily use with minimal number of codes lines and concisely describe the meta-heuristics algorithms in a directly publishable form.

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24

González Vargas, Guillermo, and Felipe González Aristizábal. "Metaheuristics applied to vehicle routing. A case study. Part 3: Genetic Clustering and Tabu Routing." Ingeniería e Investigación 27, no. 2 (May 1, 2007): 106–13. http://dx.doi.org/10.15446/ing.investig.v27n2.14838.

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This paper presents hybrid meta-heuristics called Genetic Clustering and Tabu Routing for solving a vehicle routing problem using two phases methodology: first clustering and then routing. The results are compared with those obtained using meta-heuristics and heuristic techniques presented in previous papers. Genetic clustering and Tabu routing average results were 23% and 9.1% better, respectively.

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25

Lidbe, Abhay D., Alexander M. Hainen, and Steven L. Jones. "Comparative study of simulated annealing, tabu search, and the genetic algorithm for calibration of the microsimulation model." SIMULATION 93, no. 1 (January 2017): 21–33. http://dx.doi.org/10.1177/0037549716683028.

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Microsimulation modeling is one of the contemporary techniques that has potential to perform complex transportation studies faster, safer, and in a less expensive manner. However, to get accurate and reliable results, the microsimulation models need to be well calibrated. Microsimulation model consists of various sub-models each having many parameters, most of which are user-adjustable and are attuned for calibrating the model. Manual calibration involves an iterative trial-and-error process of using the intuitive discrete values of each parameter and feasible combinations of multiple parameters each time until the desired results are obtained. With this approach, it is possible to easily get caught in a never-ending circular process of fixing one problem only to generate another. This can make manual calibration a time-consuming process and is suggested only when the number of parameters is small. However, when the calibration parameter subset is large, an automated process is suggested in the literature. Amongst the meta-heuristics used for calibrating microsimulation models, the genetic algorithm (GA) has been widely used and simulated annealing (SA) has been used only once in the past. Thus, the question of which meta-heuristics is more suitable for the problem of calibration of the microsimulation model still remains open. Thus, the objective of this paper is to evaluate and compare the manual and three (the GA, SA, and tabu search (TS)) meta-heuristics for calibration of microsimulation models. This paper therefore addresses the need to examine and identify the suitability of a meta-heuristics for calibrating microsimulation models. The results show that the meta-heuristics approach can be relied upon for calibrating simulation models very effectively, as it offers the benefit of automating the cumbersome calibrating process. All three meta-heuristics (the GA, SA, and TS) have the ability to find better calibrating parameters than the manually calibrated parameters. The number of better solutions, the best solution, and convergence to the best solution by TS is better than those by the GA and SA. Significant time can be saved by automating calibration of microsimulation models using meta-heuristics. The approach presented in this research can be used to help engineers and planners achieve better modeled results, as the calibration of microsimulation models is likely to become more complex in the future.

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26

Yin, Peng-Yeng. "Applying Modern Meta-Heuristics in Intelligent Systems." Applied Sciences 12, no. 19 (September 28, 2022): 9746. http://dx.doi.org/10.3390/app12199746.

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27

Ogunsakin, Rotimi, and Nikolay Mehandjiev. "Towards Autonomous Production: Enhanced Meta-heuristics Algorithm." Procedia Computer Science 200 (2022): 1575–81. http://dx.doi.org/10.1016/j.procs.2022.01.358.

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28

Reiners, T., and S. Voss. "Teaching meta-heuristics within virtual learning environments." International Transactions in Operational Research 11, no. 2 (March 2004): 225–38. http://dx.doi.org/10.1111/j.1475-3995.2004.00454.x.

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29

Saha, Seemanta, Ismet Burak Kadron, William Eiers, Lucas Bang, and Tevfik Bultan. "Attack Synthesis for Strings using Meta-Heuristics." ACM SIGSOFT Software Engineering Notes 43, no. 4 (January 2, 2019): 56. http://dx.doi.org/10.1145/3282517.3282527.

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Pholdee, Nantiwat, and Sujin Bureerat. "Meta-Heuristics for Engineering Optimisation - Applications to Metal Forming Processes." Key Engineering Materials 751 (August 2017): 145–50. http://dx.doi.org/10.4028/www.scientific.net/kem.751.145.

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This paper presents the use of meta-heuristics one of the most popular types of optimisation methods for solving real engineering applications. The general procedure of meta-heuristics is detailed. The applications are related to metal forming processes. Two design examples, optimisation of a strip coiling process and the non-circular wire drawing process, are demonstrated. The results obtained are compared while advantages and disadvantages of using the optimisers are discussed.

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Nordin, Norfarizani, Shahril Irwan Sulaiman, and Ahmad Maliki Omar. "Hybrid Artificial Neural Network with Meta-heuristics for Grid-Connected Photovoltaic System Output Prediction." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 1 (July 1, 2018): 121. http://dx.doi.org/10.11591/ijeecs.v11.i1.pp121-128.

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This paper presents the performance evaluation of hybrid Artificial Neural Network (ANN) model with selected meta-heuristics for predicting the AC output power fof a Grid-Connected Photovoltaic (GCPV). The ANN has been hybridized with three meta-heuristics, i.e. Cuckoo Search Algorithm (CSA), Evolutionary Programming (EP) and Firefly Algorithm (FA) separately. These meta-heuristics were used to optimize the number of neurons, learning rate and momentum rate such that the Root Mean Square Error (RMSE) of the prediction was minimized during the ANN training process. The results showed that CSA had outperformed EP and FA in producing the lowest RMSE. Later, Mutated Cuckoo Search Algorithm (MCSA) was introduced by incorporating Gaussian mutation operator in the conventional CSA. Further investigation showed that MSCA performed better prediction when compared with the conventional CSA in terms of RMSE and computation time.

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32

Wallin, Annika, and Peter Gärdenfors. "Smart people who make simple heuristics work." Behavioral and Brain Sciences 23, no. 5 (October 2000): 765. http://dx.doi.org/10.1017/s0140525x00493441.

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To evaluate the success of simple heuristics we need to know more about how a relevant heuristic is chosen and how we learn which cues are relevant. These meta-abilities are at the core of ecological rationality, rather than the individual heuristics.

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Santos Neto, Accacio Ferreira dos, Murillo Ferreira dos Santos, Mathaus Ferreira da Silva, Leonardo de Mello Honório, Edimar José de Oliveira, and Edvaldo Soares Araújo Neto. "Performance Comparison of Meta-Heuristics Applied to Optimal Signal Design for Parameter Identification." Sensors 23, no. 22 (November 10, 2023): 9085. http://dx.doi.org/10.3390/s23229085.

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This paper presents a comparative study that explores the performance of various meta-heuristics employed for Optimal Signal Design, specifically focusing on estimating parameters in nonlinear systems. The study introduces the Robust Sub-Optimal Excitation Signal Generation and Optimal Parameter Estimation (rSOESGOPE) methodology, which is originally derived from the well-known Particle Swarm Optimization (PSO) algorithm. Through a real-life case study involving an Autonomous Surface Vessel (ASV) equipped with three Degrees of Freedom (DoFs) and an aerial holonomic propulsion system, the effectiveness of different meta-heuristics is thoroughly evaluated. By conducting an in-depth analysis and comparison of the obtained results from the diverse meta-heuristics, this study offers valuable insights for selecting the most suitable optimization technique for parameter estimation in nonlinear systems. Researchers and experimental tests in the field can benefit from the comprehensive examination of these techniques, aiding them in making informed decisions about the optimal approach for optimizing parameter estimation in nonlinear systems.

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Pan, Quan-Ke, Liang Gao, Li Xin-Yu, and Framinan M. Jose. "Effective constructive heuristics and meta-heuristics for the distributed assembly permutation flowshop scheduling problem." Applied Soft Computing 81 (August 2019): 105492. http://dx.doi.org/10.1016/j.asoc.2019.105492.

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Schmitz Gonçalves, Mariana, and Aguinaldo dos Santos. "The Heuristic Method applied to Design for Sustainability in Urban Agriculture." e-Revista LOGO 10, no. 2 (December 31, 2021): 122–40. http://dx.doi.org/10.26771/e-revista.logo/2021.2.07.

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This article presents a study to identify heuristics that allow expanding social cohesion in urban agriculture initiatives. For this, two ex-post-facto case studies were carried out from Rotterdam (Netherlands) and Geneva (Switzerland). Another objective of the research is to reflect on the use of these heuristics in Service Design and Design for Social Innovation. The Heuristic Method is presented with the grounded theory approach and the steps of the method for identifying the heuristics. Finally, the contribution of this research is in the proposal of meta-actions for its conversion into Design actions based on the ten identified heuristics.

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Kalinowski, Krzysztof, Damian Krenczyk, and Cezary Grabowik. "Predictive - Reactive Strategy for Real Time Scheduling of Manufacturing Systems." Applied Mechanics and Materials 307 (February 2013): 470–73. http://dx.doi.org/10.4028/www.scientific.net/amm.307.470.

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In this paper a solution of soft real time scheduling in manufacturing systems is presented. The basic requirements of scheduling as a real time system are discussed. The proposed rescheduling method uses predictive-reactive strategy and multi thread searching approach with rule-based heuristics, meta-heuristics and random modules.

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37

FURUKAWA, Masashi. "Fundamental Method of Meta-Heuristics in Mathematical Programing." Journal of the Japan Society for Precision Engineering 82, no. 8 (2016): 735–39. http://dx.doi.org/10.2493/jjspe.82.735.

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Antal, Eugen, Peter Javorka, and Tomáš Hliboký. "Cryptanalysis of the Columnar Transposition Using Meta-Heuristics." Tatra Mountains Mathematical Publications 73, no. 1 (August 1, 2019): 39–60. http://dx.doi.org/10.2478/tmmp-2019-0005.

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Abstract The most commonly used methods for solving classical (historical) ciphers are based on global optimization (meta-heuristic methods). Despite the fact that global optimization is a well-studied problem, in the case of classical ciphers, there are still many open questions such as the construction of fitness functions or efficient transformation of the cryptanalysis (breaking attempt) to an optimization problem. Therefore the transformation of a cryptanalytical task to an optimization problem and the choice of a suitable fitness function form an important part of the topic. In this paper, we focus on the simple columnar transposition in depth. Our main contribution is a detailed analysis and comparison of different fitness functions, fitness landscape analysis and solving experiments.

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Mahdinia, Saeideh, Mehrdad Rezaie, Marischa Elveny, Noradin Ghadimi, and Navid Razmjooy. "Optimization of PEMFC Model Parameters Using Meta-Heuristics." Sustainability 13, no. 22 (November 18, 2021): 12771. http://dx.doi.org/10.3390/su132212771.

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The present study introduces an economical–functional design for a polymer electrolyte membrane fuel cell system. To do so, after introducing the optimization problem and solving the problem based on the presented equations in the fuel cell, a cost model is presented. The final design is employed for minimizing the construction cost of a 50 kW fuel cell stack, along with the costs of accessories regarding the current density, stoichiometric coefficient of the hydrogen and air, and pressure of the system as well as the temperature of the system as optimization parameters. The functional–economic model is developed for the studied system in which all components of the system are modeled economically as well as electrochemically–mechanically. The objective function is solved by a newly improved metaheuristic technique, called converged collective animal behavior (CCAB) optimizer. The final results of the method are compared with the standard CAB optimizer and genetic algorithm as a popular technique. The results show that the best optimal cost with 0.1061 $/kWh is achieved by the CCAB. Finally, a sensitivity analysis is provided for analyzing the consistency of the method.

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40

Nejad, Anahita Sabagh, and Gabor Fazekas. "Solving a traveling salesman problem using meta-heuristics." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (March 1, 2022): 41. http://dx.doi.org/10.11591/ijai.v11.i1.pp41-49.

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In this article, we have introduced an advanced new method of solving a traveling salesman problem (TSP) with the whale optimization algorithm (WOA), and K-means which is a partitioning-based algorithm used in clustering. The whale optimization algorithm first was introduced in 2016 and later used to solve a TSP problem. In the TSP problem, finding the best path, which is the path with the lowest value in the fitness function, has always been difficult and time-consuming. In our algorithm, we want to find the best tour by combining it with K-means which is a clustering method. In other words, we want to divide our problem into smaller parts called clusters, and then we join the clusters based on their distances. To do this, the WOA algorithm, TSP, and K-means must be combined. Separately, the WOA-TSP algorithm which is an unclustered algorithm is also implemented to be compared with the proposed algorithm. The results are shown through some figures and tables, which prove the effectiveness of this new method.

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41

El Karim, Tahari Abdou, Bendakmousse Abdeslam, and Ait Aoudia Samy. "Computing the Medical Image Registration Using Meta-Heuristics." Applied Mechanics and Materials 643 (September 2014): 237–42. http://dx.doi.org/10.4028/www.scientific.net/amm.643.237.

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The image registration is a very important task in image processing. In the field of medical imaging, it is used to compare the anatomical structures of two or more images taken at different time to track for example the evolution of a disease. Intensity-based techniques are widely used in the multi-modal registration. To have the best registration, a cost function expressing the similarity between these images is maximized. The registration problem is reduced to the optimization of a cost function. We propose to use neighborhood meta-heuristics (tabu search, simulated annealing) and a meta-heuristic population (genetic algorithms). An evaluation step is necessary to estimate the quality of registration obtained. In this paper we present some results of medical image registration

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42

TAKAHARA, Shigeyuki, and Sadaaki MIYAMOTO. "Application of Meta-Heuristics to a Nesting Problem." Transactions of the Society of Instrument and Control Engineers 33, no. 11 (1997): 1081–86. http://dx.doi.org/10.9746/sicetr1965.33.1081.

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B, Darsana. "Meta-Heuristics Based Arf Optimization for Image Retrieval." International Journal of Computer Science, Engineering and Information Technology 2, no. 2 (April 30, 2012): 177–85. http://dx.doi.org/10.5121/ijcseit.2012.2215.

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Zhang, Min-Xia, Bei Zhang, and Yu-Jun Zheng. "Bio-Inspired Meta-Heuristics for Emergency Transportation Problems." Algorithms 7, no. 1 (February 11, 2014): 15–31. http://dx.doi.org/10.3390/a7010015.

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Melab, N., M. Mezmaz, and E. G. Talbi. "Parallel cooperative meta-heuristics on the computational grid." Parallel Computing 32, no. 9 (October 2006): 643–59. http://dx.doi.org/10.1016/j.parco.2006.01.003.

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Dey, Sandip, Indrajit Saha, Siddhartha Bhattacharyya, and Ujjwal Maulik. "Multi-level thresholding using quantum inspired meta-heuristics." Knowledge-Based Systems 67 (September 2014): 373–400. http://dx.doi.org/10.1016/j.knosys.2014.04.006.

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Wang, Ling, Haoqi Ni, Ruixin Yang, Vijay Pappu, Michael B. Fenn, and Panos M. Pardalos. "Feature selection based on meta-heuristics for biomedicine." Optimization Methods and Software 29, no. 4 (September 26, 2013): 703–19. http://dx.doi.org/10.1080/10556788.2013.834900.

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Liao, Ching-Jong, Mitsuo Gen, Manoj Kumar Tiwari, and Pei-Chann Chang. "Meta-heuristics for manufacturing scheduling and logistics problems." International Journal of Production Economics 141, no. 1 (January 2013): 1–3. http://dx.doi.org/10.1016/j.ijpe.2012.09.004.

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Andersson, Tobias. "Solving the flight perturbation problem with meta heuristics." Journal of Heuristics 12, no. 1-2 (March 2006): 37–53. http://dx.doi.org/10.1007/s10732-006-4833-4.

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Golab, Amir, Ehsan Sedgh Gooya, Ayman Al Falou, and Mikael Cabon. "Review of conventional metaheuristic techniques for resource-constrained project scheduling problem." Journal of Project Management 7, no. 2 (2022): 95–110. http://dx.doi.org/10.5267/j.jpm.2021.10.002.

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Abstract:

This paper is concerned with an overview of the Resource-Constrained Project Scheduling Problem (RCPSP) and the conventional meta-heuristic solution techniques that have attracted the attention of many researchers in the field. Therefore, researchers have developed algorithms and methods to solve the problem. This paper addresses the single-mode RCPSP where the objective is to optimize and minimize the project duration while the quantities of resources are constrained during the project execution. In this problem, resource constraints and precedence relationships between activities are known to be the most important constraints for project scheduling. In this context, the standard RCPSP is presented. Then, the classifications of the collected papers according to the year of publication and the different meta-heuristic approaches applied are presented. Five weighted articles and their meta-heuristic techniques developed for RCPSP are described in detail and their results are summarized in the corresponding tables. In addition, researchers have developed various conventional meta-heuristic algorithms such as genetic algorithms, particle swarm optimization, ant colony optimization, bee colony optimization, simulated annealing, evolutionary algorithms, and so on. It is stated that genetic algorithms are more popular among researchers than other meta-heuristics. For this reason, the various conventional meta-heuristics and their corresponding articles are also presented to give an overview of the conventional meta-heuristic optimizing techniques. Finally, the challenges of the conventional meta-heuristics are explored, which may be helpful for future studies to apply new suitable techniques to solve the Resource-Constrained Project Scheduling Problem (RCPSP).

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