Relevant bibliographies by topics / Meta-heuristic method

Contents

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

Academic literature on the topic 'Meta-heuristic method'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Meta-heuristic method"

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Kaveh, A., and M. Khayatazad. "A new meta-heuristic method: Ray Optimization." Computers & Structures 112-113 (December 2012): 283–94. http://dx.doi.org/10.1016/j.compstruc.2012.09.003.

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Kaur, Simarpreet, and Amit Grover. "Adaptive Path Selection using Meta-heuristic Ping Method." International Journal of Computer Applications 145, no. 6 (2016): 1–4. http://dx.doi.org/10.5120/ijca2016910527.

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Kaveh, A., and V. R. Mahdavi. "Colliding bodies optimization: A novel meta-heuristic method." Computers & Structures 139 (July 2014): 18–27. http://dx.doi.org/10.1016/j.compstruc.2014.04.005.

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Chen, Jiawei, Ming Chen, Jun Wen, Lei He, and Xiaolu Liu. "A Heuristic Construction Neural Network Method for the Time-Dependent Agile Earth Observation Satellite Scheduling Problem." Mathematics 10, no. 19 (2022): 3498. http://dx.doi.org/10.3390/math10193498.

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The agile earth observation satellite scheduling problem (AEOSSP), as a time-dependent and arduous combinatorial optimization problem, has been intensively studied in the past decades. Many studies have proposed non-iterative heuristic construction algorithms and iterative meta-heuristic algorithms to solve this problem. However, the heuristic construction algorithms spend a relatively shorter time at the expense of solution quality, while the iterative meta-heuristic algorithms accomplish a high-quality solution with a lot of time. To overcome the shortcomings of these approaches and efficien
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Kaveh, Ali, and Ataollah Zaerreza. "Shuffled shepherd optimization method: a new Meta-heuristic algorithm." Engineering Computations 37, no. 7 (2020): 2357–89. http://dx.doi.org/10.1108/ec-10-2019-0481.

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Purpose This paper aims to present a new multi-community meta-heuristic optimization algorithm, which is called shuffled shepherd optimization algorithm (SSOA). In this algorithm. Design/methodology/approach The agents are first separated into multi-communities and the optimization process is then performed mimicking the behavior of a shepherd in nature operating on each community. Findings A new multi-community meta-heuristic optimization algorithm called a shuffled shepherd optimization algorithm is developed in this paper and applied to some attractive examples. Originality/value A new meta
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Himawan, Salamet Nur, Rendi Rendi, and Nur Budi Nugraha. "Feature Selection Menggunakan Algoritma Meta-Heuristik." Journal of Practical Computer Science 2, no. 2 (2023): 84–89. http://dx.doi.org/10.37366/jpcs.v2i2.2289.

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Machine learning requires data to make predictions. Data can have a large number of features. The large number of features can cause machine learning models to overfit, increase model complexity, and high computational costs. Feature selection is one method for optimizing machine learning models. Feature selection reduces the number of features used in the learning process. This research proposes a feature selection method using meta-heuristic algorithms. The machine learning model serves as the objective function for the meta-heuristic algorithm. The objective function is evaluated at each it
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Zhang, Zhi Cheng, Xiao Hui Yu, and Hai Xin Sun. "Weighted Subspace Fitting DOA Estimation Based on Shuffled Frog Leaping Algorithm." Applied Mechanics and Materials 411-414 (September 2013): 1049–52. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1049.

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In this paper, shuffled frog leaping algorithm (SFLA) is used to reduce the computation load of weighted subspace fitting (WSF) method for DOA estimation. As a recently proposed meta-heuristic algorithm, SFLA is suitable to solve the nonlinear multimodal optimization problem which WSF method encounters, so it offers an excellent alternative to the conventional methods in WSF-DOA estimation. Thus, the feasibility of SFLA applying to WSF-DOA estimation is analyzed, and its performance is compared with other popular meta-heuristic methods. Simulation results demonstrate that the proposed method i
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Mahani, Ali, Ebrahim Farahmand, Saiede Sheikhpour, and Nooshin Taheri. "Availability Improvement Method for Repairable Systems Using Modified Ant Colony Optimisation." International Journal of Computational Intelligence and Applications 16, no. 02 (2017): 1750011. http://dx.doi.org/10.1142/s1469026817500110.

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Reliability is deemed as an important issue in many broad applications, e.g., telecommunication systems, electric power systems, computation and parallel processing systems. In order to reach efficient performance and reliable/available design, we need to optimize the cost and reliability/availability of the proposed designs. The main contribution of this paper is to address the optimum model for repairable component with series-parallel structure using redundancy allocation problem (RAP). In this regard, two novel modified Ant Colony Optimization (ACO), are defined. In order to improve availa
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Muklason, Ahmad, Shof Rijal Ahlan Robbani, Edwin Riksakomara, and I. Gusti Agung Premananda. "A comparison of meta-heuristic and hyper-heuristic algorithms in solving an urban transit routing problems." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 2923. http://dx.doi.org/10.11591/ijai.v13.i3.pp2923-2933.

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<p class="p1">Public transport is a serious problem that is difficult to solve in many countries. Public transport routing optimization problem also known as urban transit routing problem (UTRP) is time-consuming process, therefore effective approches are urgently needed. UTRP aims to minimize cost passenger and operator from a combination of route set. UTRP can be optimize with heuristics, meta-heuristics, and hyper-heuristics methods. In several previous studies, UTRP can be optimized with any meta-heuristics and hyper-heuristics methods. In this study we compare the performance of meta
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Csébfalvi, Anikó. "A hybrid meta-heuristic method for continuous engineering optimization." Periodica Polytechnica Civil Engineering 53, no. 2 (2009): 93. http://dx.doi.org/10.3311/pp.ci.2009-2.05.

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Dissertations / Theses on the topic "Meta-heuristic method"

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Tatjana, Jakšić Krüger. "Development, implementation and theoretical analysis of the bee colony optimization meta-heuristic method." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2017. https://www.cris.uns.ac.rs/record.jsf?recordId=104550&source=NDLTD&language=en.

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The Ph.D. thesis addresses a comprehensive study of the bee colonyoptimization meta-heuristic method (BCO). Theoretical analysis of themethod is conducted with the tools of probability theory. Necessary andsufficient conditions are presented that establish convergence of the BCOmethod towards an optimal solution. Three parallelization strategies and fivecorresponding implementations are proposed for BCO for distributed-memorysystems. The influence of method’s parameters on the performance of theBCO algorithm for two combinatorial optimization problems is analyzedthrough the experimental
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Eusuff, Muzaffar M. "Water resources decision making using meta-heuristic optimization methods." Diss., The University of Arizona, 2004. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu_e9791_2004_051_sip1_w.pdf&type=application/pdf.

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Hopper, Eva. "Two-dimensional packing utilising evolutionary algorithms and other meta-heuristic methods." Thesis, Cardiff University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342878.

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Loubiere, Peio. "Amélioration des métaheuristiques d'optimisation à l'aide de l'analyse de sensibilité." Thesis, Paris Est, 2016. http://www.theses.fr/2016PESC1051/document.

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L'optimisation difficile représente une classe de problèmes dont la résolution ne peut être obtenue par une méthode exacte en un temps polynomial.Trouver une solution en un temps raisonnable oblige à trouver un compromis quant à son exactitude.Les métaheuristiques sont une classe d'algorithmes permettant de résoudre de tels problèmes, de manière générique et efficiente (i.e. trouver une solution satisfaisante selon des critères définis: temps, erreur, etc.).Le premier chapitre de cette thèse est notamment consacré à la description de cette problématique et à l'étude détaillée de deux familles
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Zuloaga, Maximiliano S. (Maximiliano Sebastian). "Optimizing resource allocation in a portfolio of projects related to technology infusion using heuristic and meta-heuristic methods." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/110145.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, System Design and Management Program, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 115-120).<br>In today's competitive environment, manufacturing companies are under constant pressure to improve previous products or release new ones. Nonetheless, most products are not designed and built from scratch, but rather, are based on previous versions of the product with the addition of incremental improvements given by the infusion of new technologie
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Bauer, Andreas, Bernd Bullnheimer, Richard F. Hartl, and Christine Strauß. "Applying ant colony optimization to solve the single machine total tardiness problem." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1999. http://epub.wu.ac.at/1368/1/document.pdf.

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Ant Colony Optimization is a relatively new meta-heuristic that has proven its quality and versatility on various combinatorial optimization problems such as the traveling salesman problem, the vehicle routing problem and the job shop scheduling problem. The paper introduces an Ant Colony Optimization approach to solve the problem of determining a job-sequence that minimizes the overall tardiness for a given set of jobs to be processed on a single, continuously available machine, the Single Machine Total Tardiness Problem. We experiment with various heuristic information as well as with varian
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Ameli, Mostafa. "Heuristic Methods for Calculating Dynamic Traffic Assignment Simulation-based dynamic traffic assignment: meta-heuristic solution methods with parallel computing Non-unicity of day-to-day multimodal user equilibrium: the network design history effect Improving traffic network performance with road banning strategy: a simulation approach comparing user equilibrium and system optimum." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSET009.

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Les systèmes de transport sont caractérisés de manière dynamique non seulement par des interactions non linéaires entre les différents composants, mais également par des boucles de rétroaction entre l'état du réseau et les décisions des utilisateurs. En particulier, la congestion du réseau impacte à la fois la répartition de la demande locale en modifiant les choix d’itinéraire et la demande multimodale globale. Selon les conditions du réseau, ils peuvent décider de changer, par exemple, leur mode de transport. Plusieurs équilibres peuvent être définis pour les systèmes de transport. L'équilib
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Chen, Bai-Jie, and 陳百傑. "A Meta-Heuristic Method for Vehicle Routing Problem with Time Window Constraints." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/21993520915168074947.

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碩士<br>中原大學<br>工業工程研究所<br>90<br>ABSTRACT This research proposes a heuristic, Tabu-Disturbance Algorithm (TDA), to efficiently and effectively solve Vehicle Routing Problem with Time Window Constraints (VRPTW). TDA integrates Tabu Search (TS) and Noising Method (NM). TS is the most popular generic heuristic in solving VRPHTW in recent years and NM is a combinatorial optimization meta-heuristic. The first objective is to determine the route that minimizes the total vehicle travel distances. This leads to quick response to satisfy customer demands. The second objective is to find the minimum requ
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Tsai, Yu-Jing, and 蔡玉晶. "A Meta-Heuristic Method for Vehicle Routing Problem with Hard Time Window Constraints." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/06187522153149675757.

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碩士<br>中原大學<br>工業工程研究所<br>92<br>This research proposes a heuristic, Tabu-Threshold Genetic Algorithm (TTGA), to efficiently and effectively solve Vehicle Routing Problem with Hard Time Window Constraints (VRPHTW). TTGA integrates Tabu Search (TS), Threshold Accepting (TA) and Genetic Algorithms (GAs) that are the most popular generic heuristic in solving VRPHTW in recent years. The first objective is to determine the routes that minimize the total vehicle travel distances, and the second objective is to find the minimum required number of vehicles. Both objectives lead to quick response to s
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Zlobinsky, Natasha. "A new hybrid meta-heuristic algorithm for solving single machine scheduling problems." Thesis, 2017. http://hdl.handle.net/10539/23456.

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A dissertation submitted in partial ful lment of the degree of Master of Science in Engineering (Electrical) (50/50) in the Faculty of Engineering and the Built Environment Department of Electrical and Information Engineering May 2017<br>Numerous applications in a wide variety of elds has resulted in a rich history of research into optimisation for scheduling. Although it is a fundamental form of the problem, the single machine scheduling problem with two or more objectives is known to be NP-hard. For this reason we consider the single machine problem a good test bed for solution alg
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Books on the topic "Meta-heuristic method"

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Jennings, Len, and Thomas Skovholt. Expertise in Counseling and Psychotherapy. Oxford University Press, 2016. http://dx.doi.org/10.1093/med:psych/9780190222505.001.0001.

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Expertise in Counseling and Psychotherapy features seven master therapist studies from around the world and provides an extensive synthesis of these studies to produce the first international perspective of expert counselors and psychotherapists. The study of expertise has a rich history, whereas research on psychotherapy expertise has mostly surfaced in the past two decades. Jennings and Skovholt first applied qualitative methodology to the study of expert therapists in 1996. Qualitative research has proven to be an extremely effective method for capturing the complexity of the master therapi
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Verschaffel, Lieven, Fien Depaepe, and Wim Van Dooren. Individual Differences in Word Problem Solving. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.040.

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There is currently a rather broad consensus that the competencies that are required to solve word problems involve: (a) a well-organized and flexibly accessible knowledge base involving the relevant factual, conceptual, and procedural knowledge that is relevant for solving word problems; (b) heuristic methods, i.e. search strategies for problem analysis and transformation which increase the probability of finding a solution; (c) metacognition, involving both metacognitive knowledge and metacognitive skills; (d) positive task-related affects, involving positive beliefs, attitudes, and emotions;
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Book chapters on the topic "Meta-heuristic method"

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Kaveh, Ali, and Ataollah Zaerreza. "Shuffled Shepherd Optimization Method: A New Meta-Heuristic Algorithm." In Structural Optimization Using Shuffled Shepherd Meta-Heuristic Algorithm. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25573-1_2.

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Kaveh, Ali, and Ataollah Zaerreza. "Shuffled Shepherd Optimization Method Simplified for Reducing the Parameter Dependency." In Structural Optimization Using Shuffled Shepherd Meta-Heuristic Algorithm. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25573-1_3.

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Kaveh, Ali, and Ataollah Zaerreza. "An Improved PSO Using the SRM of the ESSOA for Optimum Design of the Frame Structures via the Force Method." In Structural Optimization Using Shuffled Shepherd Meta-Heuristic Algorithm. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25573-1_8.

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Mohapatra, Soumya Snigdha, and Rakesh Ranjan Kumar. "A Framework for Ranking Cloud Services Based on an Integrated BWM-Entropy-TOPSIS Method." In Meta Heuristic Techniques in Software Engineering and Its Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11713-8_29.

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Bansal, Jagdish Chand, Prathu Bajpai, Anjali Rawat, and Atulya K. Nagar. "Sine Cosine Algorithm." In Sine Cosine Algorithm for Optimization. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9722-8_2.

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AbstractSine cosine algorithm (SCA) [1] is relatively a new algorithm, in the field of meta-heuristic algorithms. SCA is a population-based probabilistic search method that updates the position of search agents in the population using simple concept of trigonometric functions sine and cosine. SCA algorithm is inspired from the periodic property of the sine and cosine functions. The periodicity of the sine and cosine function in the range $$[-1,1]$$ provides great capacity to exploit the search space and helps in maintaining a fine balance between exploration and exploitation. In previous Chap. 1, we have already discussed about the criticality of the exploration and exploitation capabilities of any meta-heuristic algorithm.
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Tilva, Surbhi, and Jayesh Dhodiya. "Developing a Meta-Heuristic Method for Solving Multi-objective COTS Selection Problems." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5952-2_25.

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Tamura, Hiroyuki, Tomohiro Shibata, and Itsuo Hatono. "Multiobjective combinatorial optimization for performance evaluation by a meta-heuristic satisficing tradeoff method." In Global Production Management. Springer US, 1999. http://dx.doi.org/10.1007/978-0-387-35569-6_60.

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Bahranipour, Fatemeh, Sepehr Ebrahimi Mood, Alireza Souri, and Mohammad Farshi. "An Energy-Aware Task Scheduling Method using the Meta-Heuristic Algorithm in IoT Environments." In Intelligent Computing on IoT 2.0, Big Data Analytics, and Block Chain Technology. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003326236-15.

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Cruz, N. C., Juana L. Redondo, E. M. Ortigosa, and P. M. Ortigosa. "On the Design of a New Stochastic Meta-Heuristic for Derivative-Free Optimization." In Computational Science and Its Applications – ICCSA 2022 Workshops. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10562-3_14.

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AbstractOptimization problems are frequent in several fields, such as the different branches of Engineering. In some cases, the objective function exposes mathematically exploitable properties to find exact solutions. However, when it is not the case, heuristics are appreciated. This situation occurs when the objective function involves numerical simulations and sophisticated models of reality. Then, population-based meta-heuristics, such as genetic algorithms, are widely used because of being independent of the objective function. Unfortunately, they have multiple parameters and generally require numerous function evaluations to find competitive solutions stably. An attractive alternative is DIRECT, which handles the objective function as a black box like the previous meta-heuristics but is almost parameter-free and deterministic. Unfortunately, its rectangle division behavior is rigid, and it may require many function evaluations for degenerate cases. This work presents an optimizer that combines the lack of parameters and stochasticity for high exploration capabilities. This method, called Tangram, defines a self-adapted set of division rules for the search space yet relies on a stochastic hill-climber to perform local searches. This optimizer is expected to be effective for low-dimensional problems (less than 20 variables) and few function evaluations. According to the results achieved, Tangram outperforms Teaching-Learning-Based Optimization (TLBO), a widespread population-based method, and a plain multi-start configuration of the stochastic hill-climber used.
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Moosavi, Ehsan. "Modeling Long-Term Production Scheduling Problem and Its Solution Using a Bat Meta-heuristic Method." In Proceedings of the 28th International Symposium on Mine Planning and Equipment Selection - MPES 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33954-8_13.

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Conference papers on the topic "Meta-heuristic method"

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Hu, Zhiyuan, Jianbo Wang, Lei Liu, Zhongda Li, Xiaojie Li, and Shilong Zhang. "Intelligent traffic management method for random environment cloud platform based on meta heuristic algorithm." In Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), edited by Qinghua Lu and Weishan Zhang. SPIE, 2024. http://dx.doi.org/10.1117/12.3050019.

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Manjunatha Kumar, B. H., G. R. Ananya, S. V. Anusha, Poornima Paramanantti, and K. Prakruthi. "Advanced Meta-Heuristic Algorithm Based on AlBiruni Earth Radius and Particle Swarm Optimization Methods for Oral Cancer Detection." In 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS). IEEE, 2024. http://dx.doi.org/10.1109/ickecs61492.2024.10617164.

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Abazine, Ismail, Mustapha Elyaqouti, EL Hanafi Arjdal, et al. "Accurate Parameter Determination for Photovoltaic Systems Using Meta-Heuristic Methods: A Comparative Study with Particle Swarm Optimization and Multiple Objective Functions." In 2024 6th International Symposium on Advanced Electrical and Communication Technologies (ISAECT). IEEE, 2024. https://doi.org/10.1109/isaect64333.2024.10799603.

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Iwundu, Chukwudi Paschal, Ogheneruona Endurance Diemuodeke, and Joseph Chukwuma Ofodu. "Modelling and Optimization of Hybrid Photovoltaic-Wind Turbine with Energy Storage System for Autonomous Electricity Generation." In Africa International Conference on Clean Energy and Energy Storage. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-k0qcyv.

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The integration of renewable energy sources, such as photovoltaic (PV) and wind turbines, has gained significant attention due to the growing demand for reliable and clean energy solutions. This paper presents a comprehensive modelling and optimization approach for hybrid PV and wind turbine systems to maximize system performance at the same time minimizing cost and surplus energy. The proposed model incorporates detailed mathematical formulations that capture the interactions between PV modules, wind turbines, and the storage system which is a battery energy storage system (BESS). The model a
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Gonzalez, Paulo, Philip Iglesias, and Estefanía Silva. "Restricted Particle Swarm Optimization Meta-heuristic method." In 2023 42nd IEEE International Conference of the Chilean Computer Science Society (SCCC). IEEE, 2023. http://dx.doi.org/10.1109/sccc59417.2023.10315753.

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Maghrebi, Mojtaba, Travis S. Waller, and Claude Sammut. "Scheduling Concrete Delivery Problems by a Robust Meta Heuristic Method." In 2013 European Modelling Symposium (EMS). IEEE, 2013. http://dx.doi.org/10.1109/ems.2013.64.

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Mori, Hiroyuki, and Yukihiro Maeda. "A hybrid meta-heuristic method for optimal allocation of UPFCs." In 2009 IEEE International Symposium on Circuits and Systems - ISCAS 2009. IEEE, 2009. http://dx.doi.org/10.1109/iscas.2009.5118103.

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Mori, Hiroyuki, Kojiro Shimomugi, and Kanagawa Branch. "A multi-objective meta-heuristic method for distribution network optimization." In 2010 IEEE International Symposium on Circuits and Systems - ISCAS 2010. IEEE, 2010. http://dx.doi.org/10.1109/iscas.2010.5537840.

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Su, Wenjun, Zhao Li, Zhihui Wang, and Dengxin Yang. "A Meta-heuristic Test Case Prioritization Method Based on Hybrid Model." In 2020 International Conference on Computer Engineering and Application (ICCEA). IEEE, 2020. http://dx.doi.org/10.1109/iccea50009.2020.00099.

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Mori, Hiroyuki, and Hiroki Kakuta. "A new meta-heuristic method for probabilistic transmission network expansion planning." In IEEE PES T&D 2010. IEEE, 2010. http://dx.doi.org/10.1109/tdc.2010.5484393.

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