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Journal articles on the topic 'METAHEURISTIC OPTIMIZATION TECHNIQUES'

Author: Grafiati

Published: 11 September 2023

Last updated: 26 July 2025

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1

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 (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 meta

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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 (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

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Misevičius, Alfonsas, Vytautas Bukšnaitis, and Jonas Blonskis. "Kombinatorinis optmizavimas ir metaeuristiniai metodai: teoriniai aspektai." Informacijos mokslai 42, no. 43 (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 ai

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Preeti Lakhani. "Harnessing Metaheuristics for Superior Intrusion Detection: Deep Learning on Benchmark Datasets." Journal of Information Systems Engineering and Management 10, no. 34s (2025): 588–602. https://doi.org/10.52783/jisem.v10i34s.5853.

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While intrusion detection systems (IDS) are crucial to safeguarding network environments from evolving cyber threats, traditional methods often struggle to optimize detection capabilities and balance computational efficiency. To enhance the performance of IDS, this study integrates metaheuristic optimization techniques with deep learning algorithms. Combining various metaheuristics, including Genetic Algorithms, Particle Swarm Optimization, and Harris Hawks Optimization, with deep learning models, such as Graph Neural Networks, Recurrent Neural Networks, and Convolutional Neural Networks, is a

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Wang, Chia-Hung, Kun Hu, Xiaojing Wu, and Yufeng Ou. "Rethinking Metaheuristics: Unveiling the Myth of “Novelty” in Metaheuristic Algorithms." Mathematics 13, no. 13 (2025): 2158. https://doi.org/10.3390/math13132158.

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In recent decades, the rapid development of metaheuristic algorithms has outpaced theoretical understanding, with experimental evaluations often overshadowing rigorous analysis. While nature-inspired optimization methods show promise for various applications, their effectiveness is often limited by metaphor-driven design, structural biases, and a lack of sufficient theoretical foundation. This paper systematically examines the challenges in developing robust, generalizable optimization techniques, advocating for a paradigm shift toward modular, transparent frameworks. A comprehensive review of

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Sahoo, Rashmi Rekha, and Mitrabinda Ray. "Metaheuristic Techniques for Test Case Generation." Journal of Information Technology Research 11, no. 1 (2018): 158–71. http://dx.doi.org/10.4018/jitr.2018010110.

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The primary objective of software testing is to locate bugs as many as possible in software by using an optimum set of test cases. Optimum set of test cases are obtained by selection procedure which can be viewed as an optimization problem. So metaheuristic optimizing (searching) techniques have been immensely used to automate software testing task. The application of metaheuristic searching techniques in software testing is termed as Search Based Testing. Non-redundant, reliable and optimized test cases can be generated by the search based testing with less effort and time. This article prese

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Funes Lora, Miguel Angel, Edgar Alfredo Portilla-Flores, Raul Rivera Blas, Emmanuel Alejandro Merchán Cruz, and Manuel Faraón Carbajal Romero. "Metaheuristic techniques comparison to optimize robotic end-effector behavior and its workspace." International Journal of Advanced Robotic Systems 15, no. 5 (2018): 172988141880113. http://dx.doi.org/10.1177/1729881418801132.

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Many robots are dedicated to replicate trajectories recorded manually; the recorded trajectories may contain singularities, which occur when positions and/or orientations are not achievable by the robot. The optimization of those trajectories is a complex issue and classical optimization methods present a deficient performance when solving them. Metaheuristics are well-known methodologies for solving hard engineering problems. In this case, they are applied to obtain alternative trajectories that pass as closely as possible to the original one, reorienting the end-effector and displacing its p

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Nandal, Deepak, and Om Prakash Sangwan. "A Survey Report on Various Software Estimation Techniques and Practices." International Journal of Control Theory and Applications 9, no. 22 (2016): 75–83. https://doi.org/10.5281/zenodo.163521.

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Paper offers thoroughly examine of software and project analysis methods established in industry and literature, its skills and flaws The Software Estimation is very important task for completing the project successfully. A successful software project development not only relies on the product efficiency but also the accurate estimation The estimation in software development depends on various factors especially on cost and effort factors for which further AI(Artificial Intelligence) and Algorithmic models have been put into usage. The low accuracy and non- reliable structures of the algorithm

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Augusto, Adriano, Marlon Dumas, Marcello La Rosa, Sander J. J. Leemans, and Seppe K. L. M. vanden Broucke. "Optimization framework for DFG-based automated process discovery approaches." Software and Systems Modeling 20, no. 4 (2021): 1245–70. http://dx.doi.org/10.1007/s10270-020-00846-x.

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AbstractThe problem of automatically discovering business process models from event logs has been intensely investigated in the past two decades, leading to a wide range of approaches that strike various trade-offs between accuracy, model complexity, and execution time. A few studies have suggested that the accuracy of automated process discovery approaches can be enhanced by means of metaheuristic optimization techniques. However, these studies have remained at the level of proposals without validation on real-life datasets or they have only considered one metaheuristic in isolation. This art

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Radhika, Sajja, and Aparna Chaparala. "Optimization using evolutionary metaheuristic techniques: a brief review." Brazilian Journal of Operations & Production Management 15, no. 1 (2018): 44–53. http://dx.doi.org/10.14488/bjopm.2018.v15.n1.a17.

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Optimization is necessary for finding appropriate solutions to a range of real life problems. Evolutionary-approach-based meta-heuristics have gained prominence in recent years for solving Multi Objective Optimization Problems (MOOP). Multi Objective Evolutionary Approaches (MOEA) has substantial success across a variety of real-world engineering applications. The present paper attempts to provide a general overview of a few selected algorithms, including genetic algorithms, ant colony optimization, particle swarm optimization, and simulated annealing techniques. Additionally, the review is ex

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Navarro-Acosta, Jesús Alejandro, Irma D. García-Calvillo, Vanesa Avalos-Gaytán, and Edgar O. Reséndiz-Flores. "Metaheuristics and Support Vector Data Description for Fault Detection in Industrial Processes." Applied Sciences 10, no. 24 (2020): 9145. http://dx.doi.org/10.3390/app10249145.

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In this study, a system for faults detection using a combination of Support Vector Data Description (SVDD) with metaheuristic algorithms is presented. The presented approach is applied to a real industrial process where the set of measured faults is scarce. The original contribution in this work is the industrial context of application and the comparison of swarm intelligence algorithms to optimize the SVDD hyper-parameters. Four recent metaheuristics are compared hereby to solve the corresponding optimization problem in an efficient manner. These optimization techniques are then implemented f

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Soto, Ricardo, Broderick Crawford, Boris Almonacid, and Fernando Paredes. "Efficient Parallel Sorting for Migrating Birds Optimization When Solving Machine-Part Cell Formation Problems." Scientific Programming 2016 (2016): 1–39. http://dx.doi.org/10.1155/2016/9402503.

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The Machine-Part Cell Formation Problem (MPCFP) is a NP-Hard optimization problem that consists in grouping machines and parts in a set of cells, so that each cell can operate independently and the intercell movements are minimized. This problem has largely been tackled in the literature by using different techniques ranging from classic methods such as linear programming to more modern nature-inspired metaheuristics. In this paper, we present an efficient parallel version of the Migrating Birds Optimization metaheuristic for solving the MPCFP. Migrating Birds Optimization is a population meta

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Tahami, Hesamoddin, and Hengameh Fakhravar. "A Literature Review on Combining Heuristics and Exact Algorithms in Combinatorial Optimization." European Journal of Information Technologies and Computer Science 2, no. 2 (2022): 6–12. http://dx.doi.org/10.24018/compute.2022.2.2.50.

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There are several approaches for solving hard optimization problems. Mathematical programming techniques such as (integer) linear programming-based methods and metaheuristic approaches are two extremely effective streams for combinatorial problems. Different research streams, more or less in isolation from one another, created these two. Only several years ago, many scholars noticed the advantages and enormous potential of building hybrids of combining mathematical programming methodologies and metaheuristics. In reality, many problems can be solved much better by exploiting synergies between

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Palominos, Pedro, Carla Ortega, Miguel Alfaro, et al. "Chaotic Honeybees Optimization Algorithms Approach for Traveling Salesperson Problem." Complexity 2022 (October 11, 2022): 1–17. http://dx.doi.org/10.1155/2022/8903005.

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Due to the difficulty in solving combinatorial optimization problems, it is necessary to improve the performance of the algorithms by improving techniques to deal with complex optimizations. This research addresses the metaheuristics of marriage in honey-bees optimization (MBO) based on the behavior of bees. The current study proposes a technique for solving combinatorial optimization problems within proper computation times. The purpose of this study focuses on the travelling salesperson problem and the application of chaotic methods in important sections of the MBO metaheuristic. Three exper

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Fidanova, Stefka Stoyanova, and Olympia Nikolaeva Roeva. "Metaheuristic Techniques for Optimization of anE. ColiCultivation Model." Biotechnology & Biotechnological Equipment 27, no. 3 (2013): 3870–76. http://dx.doi.org/10.5504/bbeq.2012.0136.

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Roeva, Olympia, Dafina Zoteva, and Velislava Lyubenova. "Escherichia coli Cultivation Process Modelling Using ABC-GA Hybrid Algorithm." Processes 9, no. 8 (2021): 1418. http://dx.doi.org/10.3390/pr9081418.

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In this paper, the artificial bee colony (ABC) algorithm is hybridized with the genetic algorithm (GA) for a model parameter identification problem. When dealing with real-world and large-scale problems, it becomes evident that concentrating on a sole metaheuristic algorithm is somewhat restrictive. A skilled combination between metaheuristics or other optimization techniques, a so-called hybrid metaheuristic, can provide more efficient behavior and greater flexibility. Hybrid metaheuristics combine the advantages of one algorithm with the strengths of another. ABC, based on the foraging behav

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Adekanmbi, Oluwole, and Paul Green. "Conceptual Comparison of Population Based Metaheuristics for Engineering Problems." Scientific World Journal 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/936106.

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Metaheuristic algorithms are well-known optimization tools which have been employed for solving a wide range of optimization problems. Several extensions of differential evolution have been adopted in solving constrained and nonconstrained multiobjective optimization problems, but in this study, the third version of generalized differential evolution (GDE) is used for solving practical engineering problems. GDE3 metaheuristic modifies the selection process of the basic differential evolution and extends DE/rand/1/bin strategy in solving practical applications. The performance of the metaheuris

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Jain, Prachi, and Vinod Maan. "Optimizing Emotion Recognition of Non-Intrusive E-Walking Dataset." Data and Metadata 2 (December 30, 2023): 162. http://dx.doi.org/10.56294/dm2023162.

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Emotion recognition being a complex task because of its valuable usages in critical fields like Robotics, human-computer interaction and mental health has recently gathered huge attention. The selection and optimization of suitable feature sets that can accurately capture the underlying emotional states is one of the critical challenges in Emotion Recognition. Metaheuristic optimization techniques have shown promise in addressing this challenge by efficiently exploring the large and complex feature space. This research paper proposes a novel framework for emotion recognition that uses metaheur

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Diyaley, Sunny, and Shankar Chakraborty. "OPTIMIZATION OF MULTI-PASS FACE MILLING PARAMETERS USING METAHEURISTIC ALGORITHMS." Facta Universitatis, Series: Mechanical Engineering 17, no. 3 (2019): 365. http://dx.doi.org/10.22190/fume190605043d.

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In this paper, six metaheuristic algorithms, in the form of artificial bee colony optimization, ant colony optimization, particle swarm optimization, differential evolution, firefly algorithm and teaching-learning-based optimization techniques are applied for parametric optimization of a multi-pass face milling process. Using those algorithms, the optimal values of cutting speed, feed rate and depth of cut for both roughing and finishing operations are determined for having minimum total production time and total production cost. It is observed that the teaching-learning-based optimization alg

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Saber, Mohamed, Abdelaziz A. Abdelhamid, and Abdelhameed Ibrahim. "Metaheuristic Optimization Review: Algorithms and Applications." Journal of Artificial Intelligence and Metaheuristics 3, no. 1 (2023): 21–30. http://dx.doi.org/10.54216/jaim.030102.

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Metaheuristic optimisation algorithms have become more well liked in recent years due to their success in solving challenging optimisation problems. Only a few of the metaheuristic optimisation techniques covered in this work include genetic algorithms, particle swarm optimisation, simulated annealing, ant colony optimisation, and many others. This paper discusses the history, operation, and applications of each method, including applications in engineering, finance, and bioinformatics.

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Manish, Chhabra Rajesh E. "Optimizing cloud tasks scheduling based on the hybridization of darts game hypothesis and beluga whale optimization technique." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 1195–207. https://doi.org/10.11591/ijeecs.v38.i2.pp1195-1207.

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This paper presents the hybridization of two metaheuristic algorithms which belongs to different categories, for optimizing the tasks scheduling in cloud environment. Hybridization of a game-based metaheuristic algorithm namely, darts game optimizer (DGO), with a swarm-based metaheuristic algorithm namely, beluga whale optimization (BWO), yields to the evolution of a new algorithm known as “hybrid darts game hypothesis – beluga whale optimization” (hybrid DGH-BWO) algorithm. Task scheduling optimization in cloud environment is a critical process and is determined as a non-det

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Rahman, Muhammad Affiq Abd, Bazilah Ismail, Kanendra Naidu, and Mohd Khairil Rahmat. "Review on population-based metaheuristic search techniques for optimal power flow." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 1 (2019): 373. http://dx.doi.org/10.11591/ijeecs.v15.i1.pp373-381.

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<span>Optimal power flow (OPF) is a non-linear solution which is significantly important in order to analyze the power system operation. The use of optimization algorithm is essential in order to solve OPF problems. <br /> The emergence of machine learning presents further techniques which capable to solve the non-linear problem. The performance and the key aspects which enhances the effectiveness of these optimization techniques are compared within several metaheuristic search techniques. This includes the operation of particle swarm optimization (PSO) algorithm, firefly algorithm

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Vaiyapuri, Thavavel, Ashit Kumar Dutta, Mohamed Yacin Sikkandar, et al. "Design of Metaheuristic Optimization-Based Vascular Segmentation Techniques for Photoacoustic Images." Contrast Media & Molecular Imaging 2022 (January 30, 2022): 1–12. http://dx.doi.org/10.1155/2022/4736113.

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Biomedical imaging technologies are designed to offer functional, anatomical, and molecular details related to the internal organs. Photoacoustic imaging (PAI) is becoming familiar among researchers and industrialists. The PAI is found useful in several applications of brain and cancer imaging such as prostate cancer, breast cancer, and ovarian cancer. At the same time, the vessel images hold important medical details which offer strategies for a qualified diagnosis. Recently developed image processing techniques can be employed to segment vessels. Since vessel segmentation on PAI is a difficu

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Gordan, Meisam, Zubaidah Binti Ismail, Hashim Abdul Razak, Khaled Ghaedi, and Haider Hamad Ghayeb. "Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring." Journal of Civil Engineering and Construction 9, no. 1 (2020): 14–23. http://dx.doi.org/10.32732/jcec.2020.9.1.14.

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In recent years, data mining technology has been employed to solve various Structural Health Monitoring (SHM) problems as a comprehensive strategy because of its computational capability. Optimization is one the most important functions in Data mining. In an engineering optimization problem, it is not easy to find an exact solution. In this regard, evolutionary techniques have been applied as a part of procedure of achieving the exact solution. Therefore, various metaheuristic algorithms have been developed to solve a variety of engineering optimization problems in SHM. This study presents the

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Abbas, Ghulam, Irfan Ahmad Khan, Naveed Ashraf, Muhammad Taskeen Raza, Muhammad Rashad, and Raheel Muzzammel. "On Employing a Constrained Nonlinear Optimizer to Constrained Economic Dispatch Problems." Sustainability 15, no. 13 (2023): 9924. http://dx.doi.org/10.3390/su15139924.

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Recently, different metaheuristic techniques, their variants, and hybrid forms have been extensively used to solve economic load dispatch (ELD) problems with and without valve point loading (VPL) effects. Due to the randomization involved in these metaheuristic techniques, one has to perform extensive runs for each experiment to get an optimal solution. The process may sometimes become laborious and time-consuming to converge to an optimal solution. On the other hand, advanced calculus-based techniques, being deterministic, perform iteration systematically and come up with the same solution on

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P., Maruthupandi, and Vikram G.D. "Metaheuristic Techniques Based Detection of Faults in a Photovoltaic System Under Partial Shading Condition – A Review." Recent Research Reviews Journal 4, no. 1 (2025): 1–15. https://doi.org/10.36548/rrrj.2025.1.001.

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The growing demand for renewable energy has led to increased adoption of photovoltaic (PV) systems. However, their efficiency and reliability are significantly affected by partial shading conditions (PSCs), which cause power losses and fault occurrences. Traditional fault detection methods often fail to provide accurate and timely identification of shading-induced issues. To address this challenge, metaheuristic techniques have emerged as effective solutions due to their optimization capabilities in complex, nonlinear environments. This review explores various metaheuristic-based fault detecti

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Aouragh, Abd Allah, Mohamed Bahaj, and Fouad Toufik. "Balancing and metaheuristic techniques for improving machine learning models in brain stroke prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 1 (2025): 473. http://dx.doi.org/10.11591/ijai.v14.i1.pp473-481.

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A brain stroke, medically referred to as a stroke, represents a critical condition triggered by the disruption of blood flow to a region of the brain. Early detection of stroke is crucial to prevent fatal complications. In this study, we worked with an unbalanced dataset of 4981 entries on stroke, which we balanced using the K-means synthetic minority over-sampling technique (KMeansSMOTE) algorithm. We then employed five machine learning algorithms: decision tree, random forest, support vector machine, K-nearest neighbors, and gradient boosting. We compared the hyperparameter optimization of t

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Abd, Allah Aouragh, Bahaj Mohamed, and Toufik Fouad. "Balancing and metaheuristic techniques for improving machine learning models in brain stroke prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 1 (2025): 473–81. https://doi.org/10.11591/ijai.v14.i1.pp473-481.

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A brain stroke, medically referred to as a stroke, represents a critical condition triggered by the disruption of blood flow to a region of the brain. Early detection of stroke is crucial to prevent fatal complications. In this study, we worked with an unbalanced dataset of 4981 entries on stroke, which we balanced using the K-means synthetic minority over-sampling technique (KMeansSMOTE) algorithm. We then employed five machine learning algorithms: decision tree, random forest, support vector machine, K-nearest neighbors, and gradient boosting. We compared the hyperparameter optimization of t

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Peñacoba, Mario, Jesús Enrique Sierra-García, Matilde Santos, and Ioannis Mariolis. "Path Optimization Using Metaheuristic Techniques for a Surveillance Robot." Applied Sciences 13, no. 20 (2023): 11182. http://dx.doi.org/10.3390/app132011182.

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This paper presents an innovative approach to optimize the trajectories of a robotic surveillance system, employing three different optimization methods: genetic algorithm (GA), particle swarm optimization (PSO), and pattern search (PS). The research addresses the challenge of efficiently planning routes for a LiDAR-equipped mobile robot to effectively cover target areas taking into account the capabilities and limitations of sensors and robots. The findings demonstrate the effectiveness of these trajectory optimization approaches, significantly improving detection efficiency and coverage of c

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Chhabra, Manish, and Rajesh E. "Optimizing cloud tasks scheduling based on the hybridization of darts game hypothesis and beluga whale optimization technique." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 1195. https://doi.org/10.11591/ijeecs.v38.i2.pp1195-1207.

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<p>This paper presents the hybridization of two metaheuristic algorithms which belongs to different categories, for optimizing the tasks scheduling in cloud environment. Hybridization of a game-based metaheuristic algorithm namely, darts game optimizer (DGO), with a swarm-based metaheuristic algorithm namely, beluga whale optimization (BWO), yields to the evolution of a new algorithm known as “hybrid darts game hypothesis – beluga whale optimization” (hybrid DGH-BWO) algorithm. Task scheduling optimization in cloud environment is a critical process and is determined as a non-deterministi

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Sadeeq, Haval Tariq, Araz Abrahim, Thamer Hameed, Najdavan Kako, Reber Mohammed, and Dindar Ahmed. "An improved pelican optimization algorithm for function optimization and constrained engineering design problems." Decision Science Letters 14, no. 3 (2025): 623–40. https://doi.org/10.5267/j.dsl.2025.4.004.

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Metaheuristic algorithms are a class of optimization techniques that have revolutionized problem-solving across various domains. These algorithms provide a versatile and powerful approach to finding near-optimal solutions for complex, combinatorial, and computationally intensive problems. They draw inspiration from natural processes, such as evolution, swarm behavior, or annealing, to iteratively refine solutions by intelligently navigating the problem space. Metaheuristics have become indispensable tools in both academia and industry, helping researchers and practitioners address real-world p

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García, José, Gino Astorga, and Víctor Yepes. "An Analysis of a KNN Perturbation Operator: An Application to the Binarization of Continuous Metaheuristics." Mathematics 9, no. 3 (2021): 225. http://dx.doi.org/10.3390/math9030225.

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The optimization methods and, in particular, metaheuristics must be constantly improved to reduce execution times, improve the results, and thus be able to address broader instances. In particular, addressing combinatorial optimization problems is critical in the areas of operational research and engineering. In this work, a perturbation operator is proposed which uses the k-nearest neighbors technique, and this is studied with the aim of improving the diversification and intensification properties of metaheuristic algorithms in their binary version. Random operators are designed to study the

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Mandour, Samia, Abduallah Gamal, Ahmed Sleem, and Mohamed Belal. "Data Mining Problems Optimization by using Metaheuristic Algorithms: A Survey." Multicriteria Algorithms with Applications 4 (June 20, 2024): 28–52. http://dx.doi.org/10.61356/j.mawa.2024.4301.

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Big data refers to large, diverse, and complicated data sets that are challenging to store, analyze, and visualize for use in subsequent operations or outcomes. Exploring and analyzing vast amounts of data in order to find significant patterns and principles is called data mining. Data mining is crucial to many human endeavors because it uncovers previously undiscovered patterns that are helpful. There are several main tasks of data mining, including Clustering, feature selection, and association rules. Several data mining techniques are employed to handle these significant duties. Metaheurist

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Becerra-Rozas, Marcelo, José Lemus-Romani, Felipe Cisternas-Caneo, Broderick Crawford, Ricardo Soto, and José García. "Swarm-Inspired Computing to Solve Binary Optimization Problems: A Backward Q-Learning Binarization Scheme Selector." Mathematics 10, no. 24 (2022): 4776. http://dx.doi.org/10.3390/math10244776.

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In recent years, continuous metaheuristics have been a trend in solving binary-based combinatorial problems due to their good results. However, to use this type of metaheuristics, it is necessary to adapt them to work in binary environments, and in general, this adaptation is not trivial. The method proposed in this work evaluates the use of reinforcement learning techniques in the binarization process. Specifically, the backward Q-learning technique is explored to choose binarization schemes intelligently. This allows any continuous metaheuristic to be adapted to binary environments. The illu

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Sivakumar, R. D. Assistant Professor Department of Computer Science, and S. Former Assistant Professor Department of Business Administration Brindha. "OPTIMIZATION TECHNIQUES FOR DECISION SUPPORT SYSTEMS." Indian Journal of Research and Development Systems in Technologization 1, no. 3 (2024): 30–40. https://doi.org/10.5281/zenodo.11202691.

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Decision Support Systems (DSS) are applied in these different areas like business, healthcare and logistics, they are critical tools that help users to get the best decision makings possible when using them. Optimization methods are paramount constitute the basis of the DSS for provisioning relevant and useful decision-making while maximizing accuracy. This paper starts by elucidating diverse optimization inner workings of DSSs, followed by data management and visualization, describing mathematical programming, heuristic approaches, and metaheuristic fundamentals. Mathematical programming cons

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Abbas, Noor Ali, Mahdi Abed Salman, and Muhammed Abaid Mahdi. "Deployment Approach in WSNS Using Metaheuristic Algorithm: A Survey." Al-Furat Journal of Innovations in Electronics and Computer Engineering 3, no. 2 (2024): 29–40. http://dx.doi.org/10.46649/fjiece.v3.2.3a.12.5.2024.

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Deploying wireless sensor networks (WSN) is one of the areas with the most significant research since the methods employed can have a big impact on the system's overall performance and the amount of energy the sensors in it consume.Thus, an effective deployment problem solution should not only "enhance its performance" but also save the energy needed to extend the lifetime of WSN. Finding an optimal solution for most deployment problems with limited computational resources is challenging, particularly for NP-hard optimization problems. By searching for a nearly optimal solution with constraine

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Nguyen, Trung Kien, In-Gon Lee, Obum Kwon, Yoon-Jae Kim, and Ic-Pyo Hong. "Metaheuristic Optimization Techniques for an Electromagnetic Multilayer Radome Design." Journal of Electromagnetic Engineering and Science 19, no. 1 (2019): 31–36. http://dx.doi.org/10.26866/jees.2019.19.1.31.

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Budi, Warsito, Santoso Rukun, and Yasin Hasbi. "Metaheuristic optimization in neural network model for seasonal data." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 6 (2021): 1892–901. https://doi.org/10.12928/telkomnika.v19i6.20409.

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The use of metaheuristic optimization techniques in obtaining the optimal weights of neural network model for the time series was the main part of this research. The three optimization methods used as experiments were genetic algorithm (GA), particle swarm optimization (PSO), and modified bee colony (MBC). Feed forward neural network (FFNN) was the neural network (NN) architecture chosen in this research. The limitations and weaknesses of gradient-based methods for learning algorithm inspired some researchers to use other techniques. A reasonable choice is non-gradient based method. Neural net

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Ammar, Hossam Hassan, Ahmad Taher Azar, Raafat Shalaby, and M. I. Mahmoud. "Metaheuristic Optimization of Fractional Order Incremental Conductance (FO-INC) Maximum Power Point Tracking (MPPT)." Complexity 2019 (November 28, 2019): 1–13. http://dx.doi.org/10.1155/2019/7687891.

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This paper seeks to improve the photovoltaic (PV) system efficiency using metaheuristic, optimized fractional order incremental conductance (FO-INC) control. The proposed FO-INC controls the output voltage of the PV arrays to obtain maximum power point tracking (MPPT). Due to its simplicity and efficiency, the incremental conductance MPPT (INC-MPPT) is one of the most popular algorithms used in the PV scheme. However, owing to the nonlinearity and fractional order (FO) nature of both PV and DC-DC converters, the conventional INC algorithm provides a trade-off between monitoring velocity and tr

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Santos-Ramos, Javier E., Sergio D. Saldarriaga-Zuluaga, Jesús M. López-Lezama, Nicolás Muñoz-Galeano, and Walter M. Villa-Acevedo. "Microgrid Protection Coordination Considering Clustering and Metaheuristic Optimization." Energies 17, no. 1 (2023): 210. http://dx.doi.org/10.3390/en17010210.

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This paper addresses the protection coordination problem of microgrids combining unsupervised learning techniques, metaheuristic optimization and non-standard characteristics of directional over-current relays (DOCRs). Microgrids may operate under different topologies or operative scenarios. In this case, clustering techniques such as K-means, balanced iterative reducing and clustering using hierarchies (BIRCH), Gaussian mixture, and hierarchical clustering were implemented to classify the operational scenarios of the microgrid. Such scenarios were previously defined according to the type of g

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Xin Ying, Felicia Lim, and Suliadi Firdaus Sufahani. "Benchmarking Metaheuristic Algorithms Against Optimization Techniques for Transportation Problem in Supply Chain Management." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 9, no. 3 (2025): 455–64. https://doi.org/10.29207/resti.v9i3.6513.

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The optimization of transportation problems plays a significant role in supply chain management (SCM), where minimizing costs and improving efficiency are mandatory. The transition from manual methods to advanced computational approaches, such as metaheuristic algorithms, enhances decision-making and consolidates operations within SCM. Malaysia's transportation system has been confronting crucial challenges, characterized by congested roadways, limited rail connectivity and inefficient port operations, which interfere with the fluidity of goods and supply chain efficiency. This highlights the

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Moayedi, Kalantar, Foong, Tien Bui, and Motevalli. "Application of Three Metaheuristic Techniques in Simulation of Concrete Slump." Applied Sciences 9, no. 20 (2019): 4340. http://dx.doi.org/10.3390/app9204340.

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Slump is a workability-related characteristic of concrete mixture. This paper investigates the efficiency of a novel optimizer, namely ant lion optimization (ALO), for fine-tuning of a neural network (NN) in the field of concrete slump prediction. Two well-known optimization techniques, biogeography-based optimization (BBO) and grasshopper optimization algorithm (GOA), are also considered as benchmark models to be compared with ALO. Considering seven slump effective factors, namely cement, slag, water, fly ash, superplasticizer (SP), fine aggregate (FA), and coarse aggregate (CA), the mentione

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Munien, Chanaleä, and Absalom E. Ezugwu. "Metaheuristic algorithms for one-dimensional bin-packing problems: A survey of recent advances and applications." Journal of Intelligent Systems 30, no. 1 (2021): 636–63. http://dx.doi.org/10.1515/jisys-2020-0117.

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Abstract The bin-packing problem (BPP) is an age-old NP-hard combinatorial optimization problem, which is defined as the placement of a set of different-sized items into identical bins such that the number of containers used is optimally minimized. Besides, different variations of the problem do exist in practice depending on the bins dimension, placement constraints, and priority. More so, there are several important real-world applications of the BPP, especially in cutting industries, transportation, warehousing, and supply chain management. Due to the practical relevance of this problem, re

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Sağ, Tahir. "Approaches Used In Adapting Metaheuristic Optimization Algorithms Developed For Continuous Problems to Discrete Problems." Proceedings of The International Conference on Academic Research in Science, Technology and Engineering 1, no. 1 (2023): 1–11. http://dx.doi.org/10.33422/icarste.v1i1.12.

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Many real-world problems such as determining the type and number of wind turbines, facility placement problems, job scheduling problems, are in the category of combinatorial optimization problems in terms of the type of decision variables. However, since many of the evolutionary optimization algorithms are developed for solving continuous optimization problems, they cannot be directly applied to optimization problems with discrete decision variables. Therefore, the continuous decision variable values generated by these metaheuristics need to be converted to binary values using some techniques.

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Niyonteze, Jean De Dieu, Fumin Zou, Godwin Norense Osarumwense Asemota, et al. "Applications of Metaheuristic Algorithms in Solar Air Heater Optimization: A Review of Recent Trends and Future Prospects." International Journal of Photoenergy 2021 (April 27, 2021): 1–36. http://dx.doi.org/10.1155/2021/6672579.

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A transition to solar energy systems is considered one of the most important alternatives to conventional fossil fuels. Until recently, solar air heaters (SAHs) were among the other solar energy systems that have been widely used in various households and industrial applications. However, the recent literature reveals that efficiencies of SAHs are still low. Some metaheuristic algorithms have been used to enhance the efficiencies of these SAH systems. In the paper, we do not only discuss the techniques used to enhance the performance of SAHs, but we also reviewed a majority of published papers

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Alagu, S., G. Kavitha, and K. Bhoopathy Bagan. "Advanced Detection of Acute Lymphoblastic Leukemia Using Integrated Deep Features and Metaheuristic Algorithms." Indian Journal Of Science And Technology 17, no. 43 (2024): 4487–93. http://dx.doi.org/10.17485/ijst/v17i43.3398.

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Objectives: Acute lymphoblastic leukemia is one form of blood cancer. This research work suggests the impact of meta-heuristic feature optimization techniques on leukemia diagnosis. Methods: ALL-IDB2 image database is utilized for this purpose which is publicly available. Techniques for pre-processing images include scaling, SMOTE, and augmentation. Segmentation of the nucleus plays a vital role in detecting leukemia. As a novelty, two distinct deep networks, SegNet and ResUNet are deployed for this purpose in place of conventional segmentation techniques. ResUNet performs better than SegNet w

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Abbas, Farkhanda, Feng Zhang, Muhammad Ismail, et al. "Optimizing Machine Learning Algorithms for Landslide Susceptibility Mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: A Comparative Study of Baseline, Bayesian, and Metaheuristic Hyperparameter Optimization Techniques." Sensors 23, no. 15 (2023): 6843. http://dx.doi.org/10.3390/s23156843.

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Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration of hyperparameters directly impact the performance of machine learning models. Achieving optimal hyperparameter settings often requires a deep understanding of the underlying models and the appropriate optimization techniques. While there are many automatic optimization techniques available, each with its own advantages and disadvantages, this

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Rawat, Devendra, Mukul Kumar Gupta, and Abhinav Sharma. "Trajectory Control of Robotic Manipulator using Metaheuristic Algorithms." International Journal of Mathematical, Engineering and Management Sciences 8, no. 2 (2023): 264–81. http://dx.doi.org/10.33889/ijmems.2023.8.2.016.

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Robotic manipulators are extremely nonlinear complex and, uncertain systems. They have multi-input multi-output (MIMO) dynamics, which makes controlling manipulators difficult. Robotic manipulators have wide applications in many industries like processes, medicine, and space. Effective control of these manipulators is extremely important to perform these industrial tasks. Researchers are working on the control of robotic manipulators using conventional and intelligent control methods. Conventional control methods are proportional integral and derivative (PID), Fractional order proportional int

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A. Bewoor, Laxmi, V. Chandra Prakash, and Sagar U. Sapkal. "Comparative Analysis of Metaheuristic Approaches for Makespan Minimization for No Wait Flow Shop Scheduling Problem." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 1 (2017): 417. http://dx.doi.org/10.11591/ijece.v7i1.pp417-423.

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This paper provides comparative analysis of various metaheuristic approaches for m-machine no wait flow shop scheduling (NWFSS) problem with makespan as an optimality criterion. NWFSS problem is NP hard and brute force method unable to find the solutions so approximate solutions are found with metaheuristic algorithms. The objective is to find out the scheduling sequence of jobs to minimize total completion time. In order to meet the objective criterion, existing metaheuristic techniques viz. Tabu Search (TS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are implemented for sma

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Laxmi, A. Bewoor, Chandra Prakash V., and U. Sapkal Sagar. "Comparative Analysis of Metaheuristic Approaches for Makespan Minimization for No Wait Flow Shop Scheduling Problem." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 1 (2017): 417–23. https://doi.org/10.11591/ijece.v7i1.pp417-423.

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This paper provides comparative analysis of various metaheuristic approaches for m-machine no wait flow shop scheduling (NWFSS) problem with makespan as an optimality criterion. NWFSS problem is NP hard and brute force method unable to find the solutions so approximate solutions are found with metaheuristic algorithms. The objective is to find out the scheduling sequence of jobs to minimize total completion time. In order to meet the objective criterion, existing metaheuristic techniques viz. Tabu Search (TS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are implemented for sma

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