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Journal articles on the topic 'Tunicate swarm algorithm'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

Sharma, Abhishek, Abhinav Sharma, Ankit Dasgotra, et al. "Opposition-Based Tunicate Swarm Algorithm for Parameter Optimization of Solar Cells." IEEE Access 9 (September 7, 2021): 125590–602. https://doi.org/10.1109/ACCESS.2021.3110849.

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Parameter estimation of photovoltaic modules is an essential step to observe, analyze, and optimize the performance of solar power systems. An efficient optimization approach is needed to obtain the finest value of unknown parameters. Herewith, this article proposes a novel opposition-based tunicate swarm algorithm for parameter estimation. The proposed algorithm is developed based on the exploration and exploitation components of the tunicate swarm algorithm. The opposition-based learning mechanism is employed to improve the diversification of the search space to provide a precise solution. T

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Krstić, Mladen, Branislav Milenković, and Đorđe Jovanović. "APPLICATION OF THE METAHEURISTIC TUNICATE SWARM ALGORITHM IN SOLVING APPLIED MECHANICS PROBLEMS." Journal of Production Engineering 24, no. 2 (2021): 31–34. http://dx.doi.org/10.24867/jpe-2021-02-031.

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In this paper, the principles of a metaheuristic algorithm based on tunicate swarm behavior are shown. The Tunicate Swarm Algorithm (TSA for short) was used for solving problems in applied mechanics (speed reducer, cantilever beam and three-dimensional beam optimization). In the end, a comparison of results obtained by TSA and results obtained by other methods is given.

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Sharma, Abhishek, Ankit Dasgotra, Sunil Kumar Tiwari, Abhinav Sharma, Vibhu Jately, and Brian Azzopardi. "Parameter Extraction of Photovoltaic Module Using Tunicate Swarm Algorithm." Electronics 10, no. 8 (2021): 878. http://dx.doi.org/10.3390/electronics10080878.

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In the renewable energy sector, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction is a non-linear complex optimization problem for solar PV cells. In this research work, the authors have implemented the Tunicate swarm algorithm (TSA) to estimate the optimized value of the unknown parameters of a PV cell/module under standard temperature conditions. The simulation results have been compared with four different, pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimizatio

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Sharma, Abhishek, Ankit Abhishek, Sunil Kumar Tiwari, Abhinav Sharma, Vibhu Jately, and Brian Azzopardi. "Parameter Extraction of Photovoltaic Module Using Tunicate Swarm Algorithm." Electronics 10, no. 8 (2022): 878. https://doi.org/10.3390/electronics10080878.

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In the renewable energy sector, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction is a non-linear complex optimization problem for solar PV cells. In this research work, the authors have implemented the Tunicate swarm algorithm (TSA) to estimate the optimized value of the unknown parameters of a PV cell/module under standard temperature conditions. The simulation results have been compared with four different, pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimizatio

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Sharma, Abhishek, Abhinav Sharma, Vibhu Jately, Moshe Averbukh, Shailendra Rajput, and Brian Azzopardi. "A Novel TSA-PSO Based Hybrid Algorithm for GMPP Tracking under Partial Shading Conditions." Energies 15, no. 9 (2022): 3164. http://dx.doi.org/10.3390/en15093164.

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In this paper, a new hybrid TSA-PSO algorithm is proposed that combines tunicate swarm algorithm (TSA) with the particle swarm optimization (PSO) technique for efficient maximum power extraction from a photovoltaic (PV) system subjected to partial shading conditions (PSCs). The performance of the proposed algorithm was enhanced by incorporating the PSO algorithm, which improves the exploitation capability of TSA. The response of the proposed TSA-PSO-based MPPT was investigated by performing a detailed comparative study with other recently published MPPT algorithms, such as tunicate swarm algor

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Sharma, Abhishek, Abhinav Sharma, Vibhu Jately, Moshe Averbukh, Shailendra Rajput, and Brian Azzopardi. "A Novel TSA-PSO Based Hybrid Algorithm for GMPP Tracking under Partial Shading Conditions." Energies 15, no. 9 (2022): 3164. http://dx.doi.org/10.3390/en15093164.

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In this paper, a new hybrid TSA-PSO algorithm is proposed that combines tunicate swarm algorithm (TSA) with the particle swarm optimization (PSO) technique for efficient maximum power extraction from a photovoltaic (PV) system subjected to partial shading conditions (PSCs). The performance of the proposed algorithm was enhanced by incorporating the PSO algorithm, which improves the exploitation capability of TSA. The response of the proposed TSA-PSO-based MPPT was investigated by performing a detailed comparative study with other recently published MPPT algorithms, such as tunicate swarm algor

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7

Sharma, Abhishek, Abhinav Sharma, Vibhu Jately, Moshe Averbukh, Shailendra Rajput, and Brian Azzopardi. "A Novel TSA-PSO based Hybrid Algorithm for GMPP Tracking under Partial Shading Conditions." Energies 15, no. 9 (2022): 3164. https://doi.org/10.3390/en15093164.

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In this paper, a new hybrid TSA-PSO algorithm is proposed that combines tunicate swarm algorithm (TSA) with the particle swarm optimization (PSO) technique for efficient maximum power extraction from a photovoltaic (PV) system subjected to partial shading conditions (PSCs). The performance of the proposed algorithm was enhanced by incorporating the PSO algorithm, which improves the exploitation capability of TSA. The response of the proposed TSA-PSO-based MPPT was investigated by performing a detailed comparative study with other recently published MPPT algorithms, such as tunicate swarm algor

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8

Liu, Guangwei, Zhiqing Guo, Wei Liu, Bo Cao, Senlin Chai, and Chunguang Wang. "MSHHOTSA: A variant of tunicate swarm algorithm combining multi-strategy mechanism and hybrid Harris optimization." PLOS ONE 18, no. 8 (2023): e0290117. http://dx.doi.org/10.1371/journal.pone.0290117.

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This paper proposes a novel hybrid algorithm, named Multi-Strategy Hybrid Harris Hawks Tunicate Swarm Optimization Algorithm (MSHHOTSA). The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. Firstly, inspired by the idea of the neighborhood and thermal distribution map, the hyperbolic tangent domain is introduced to modify the position of new tunicate individuals, which can not only effectively enhance the convergence performance of the a

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Jianpo Li, Jianpo Li, Geng-Chen Li Jianpo Li, Shu-Chuan Chu Geng-Chen Li, Min Gao Shu-Chuan Chu, and Jeng-Shyang Pan Min Gao. "Modified Parallel Tunicate Swarm Algorithm and Application in 3D WSNs Coverage Optimization." 網際網路技術學刊 23, no. 2 (2022): 227–44. http://dx.doi.org/10.53106/160792642022032302004.

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<p>As the application of Wireless Sensor Networks (WSNs) in today’s society becomes more and more extensive, and the status is getting higher and higher, the node layout of sensors has also begun to attract social attention. In reality, the coverage of WSNs in 3D space is particularly important. Therefore, it is worth investigating an efficient way to find out the maximum coverage of WSNs. In this paper, a Modified Parallel Tunicate Swarm Algorithm (MPTSA) is proposed based on modified parallelism, which can improve the convergence of the algorithm and optimal global solution. Next, the

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Kaya, Ebubekir, Ceren Baştemur Kaya, Emre Bendeş, Sema Atasever, Başak Öztürk, and Bilgin Yazlık. "Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum Power Point Tracking." Biomimetics 8, no. 5 (2023): 402. http://dx.doi.org/10.3390/biomimetics8050402.

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One of the most used artificial intelligence techniques for maximum power point tracking is artificial neural networks. In order to achieve successful results in maximum power point tracking, the training process of artificial neural networks is important. Metaheuristic algorithms are used extensively in the literature for neural network training. An important group of metaheuristic algorithms is swarm-intelligent-based optimization algorithms. In this study, feed-forward neural network training is carried out for maximum power point tracking by using 13 swarm-intelligent-based optimization al

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Diaz, P. M., and Julie Emerald Jiju. "Feature Selection and Feature Weighting Using Tunicate Swarm Genetic Optimization Algorithm With Deep Residual Networks." International Journal of Swarm Intelligence Research 13, no. 1 (2022): 1–16. http://dx.doi.org/10.4018/ijsir.309939.

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Feature selection (FS) method is applied for extracting only the relevant information from the dataset. FS seemed to be an optimization concept because appropriate feature selection is the significant role of any classification problem. Similarly, feature weighting is employed to enhance the classification performance along with FS process. In this paper, feature selection and feature weighting has been performed by integrated an optimization algorithm called tunicate swarm genetic algorithm (TSGA) with deep residual network (DRN). TSGA is the combination of tunicate swarm algorithm (TSA) and

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12

Arabali, Amirbahador, Mohammad Khajehzadeh, Suraparb Keawsawasvong, Adil Hussein Mohammed, and Baseem Khan. "An Adaptive Tunicate Swarm Algorithm for Optimization of Shallow Foundation." IEEE Access 10 (2022): 39204–19. http://dx.doi.org/10.1109/access.2022.3164734.

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13

Du, Chengtao, and Jinzhong Zhang. "An Enhanced Tunicate Swarm Algorithm with Symmetric Cooperative Swarms for Training Feedforward Neural Networks." Symmetry 16, no. 7 (2024): 866. http://dx.doi.org/10.3390/sym16070866.

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The input layer, hidden layer, and output layer are three models of neural processors that comprise feedforward neural networks. In this paper, an enhanced tunicate swarm algorithm based on a differential sequencing alteration operator (ETSA) with symmetric cooperative swarms is presented to train feedforward neural networks. The objective is to accomplish minimum classification errors and the most appropriate neural network layout by regulating the layers’ connection weights and neurons’ deviation thresholds according to the transmission error between the anticipated input and the authentic o

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14

Houssein, Essam H., Bahaa El-Din Helmy, Ahmed A. Elngar, Diaa Salama Abdelminaam, and Hassan Shaban. "An Improved Tunicate Swarm Algorithm for Global Optimization and Image Segmentation." IEEE Access 9 (2021): 56066–92. http://dx.doi.org/10.1109/access.2021.3072336.

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15

Sharma, Abhishek, Abhinav Sharma, Ankit Dasgotra, et al. "Opposition-Based Tunicate Swarm Algorithm for Parameter Optimization of Solar Cells." IEEE Access 9 (2021): 125590–602. http://dx.doi.org/10.1109/access.2021.3110849.

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16

Li, Ling-Ling, Zhi-Feng Liu, Ming-Lang Tseng, Sheng-Jie Zheng, and Ming K. Lim. "Improved tunicate swarm algorithm: Solving the dynamic economic emission dispatch problems." Applied Soft Computing 108 (September 2021): 107504. http://dx.doi.org/10.1016/j.asoc.2021.107504.

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Yadav, Kusum, Jalawi Sulaiman Alshudukhi, Gaurav Dhiman, and Wattana Viriyasitavat. "iTSA: an improved Tunicate Swarm Algorithm for defensive resource assignment problem." Soft Computing 26, no. 10 (2022): 4929–37. http://dx.doi.org/10.1007/s00500-022-06979-z.

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18

Tripathi, Amrendra, Tanupriya Choudhury, and Hitesh Kumar Sharma. "EEG Based Emotion Detection by Using Modified Tunicate Swarm Optimization Algorithm." Ingénierie des systèmes d information 29, no. 4 (2024): 1333–42. http://dx.doi.org/10.18280/isi.290409.

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19

Saad Mohammed Mohammed, Aya, Islam Hegazy, and El-Sayed El-Horabty. "TUNICATE SWARM BASED CLUSTERING AND ROUTING ALGORITHM FOR INTERNET OF THINGS." International Journal of Intelligent Computing and Information Sciences 23, no. 1 (2023): 53–68. http://dx.doi.org/10.21608/ijicis.2023.175550.1228.

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Khan, Shadab, Yash Veer Singh, Pushpendra Singh, and Ram Sewak Singh. "An Optimized Artificial Intelligence System Using IoT Biosensors Networking for Healthcare Problems." Computational Intelligence and Neuroscience 2022 (March 24, 2022): 1–14. http://dx.doi.org/10.1155/2022/2206573.

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In today’s environment, electronics technology is growing rapidly because of the availability of the numerous and latest devices which can be deployed for monitoring and controlling the various healthcare systems. Due to the limitations of such devices, there is a dire need to optimize the utilization of the devices. In healthcare systems, Internet of things (IoT) based biosensors networking has minimal energy during transmission and collecting data. This paper proposes an optimized artificial intelligence system using IoT biosensors networking for healthcare problems for efficient data collec

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Singh, Gunjan, and Arpita Nagpal. "HFCVO-DMN: Henry Fuzzy Competitive Verse Optimizer-Integrated Deep Maxout Network for Incremental Text Classification." Computation 11, no. 1 (2023): 13. http://dx.doi.org/10.3390/computation11010013.

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One of the effectual text classification approaches for learning extensive information is incremental learning. The big issue that occurs is enhancing the accuracy, as the text is comprised of a large number of terms. In order to address this issue, a new incremental text classification approach is designed using the proposed hybrid optimization algorithm named the Henry Fuzzy Competitive Multi-verse Optimizer (HFCVO)-based Deep Maxout Network (DMN). Here, the optimal features are selected using Invasive Weed Tunicate Swarm Optimization (IWTSO), which is devised by integrating Invasive Weed Op

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Evi, Nafiatus Sholikhah, Ayub Windarko Novie, and Sumantri Bambang. "Tunicate swarm algorithm based maximum power point tracking for photovoltaic system under non-uniform irradiation." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 4559–70. https://doi.org/10.11591/ijece.v12i5.pp4559-4570.

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A new maximum power point tracking (MPPT) technique based on the bio-inspired metaheuristic algorithm for photovoltaic system (PV system) is proposed, namely tunicate swarm algorithm-based MPPT (TSA-MPPT). The proposed algorithm is implemented on the PV system with five PV modules arranged in series and integrated with DC-DC buck converter. Then, the PV system is tested in a simulation using PowerSim (PSIM) software. TSA-MPPT is tested under varying irradiation conditions both uniform irradiation and non-uniform irradiation. Furthermore, to evaluate the performance, TSA-MPPT is compared with p

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Dogra, Roopali, Shalli Rani, Sandeep Verma, Sahil Garg, and Mohammad Mehedi Hassan. "TORM: Tunicate Swarm Algorithm-based Optimized Routing Mechanism in IoT-based Framework." Mobile Networks and Applications 26, no. 6 (2021): 2365–73. http://dx.doi.org/10.1007/s11036-021-01833-2.

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24

Hu, Gang, Jiaoyue Zheng, Xiaomin Ji, and Xinqiang Qin. "Enhanced tunicate swarm algorithm for optimizing shape of C2 RQI-spline curves." Engineering Applications of Artificial Intelligence 121 (May 2023): 105958. http://dx.doi.org/10.1016/j.engappai.2023.105958.

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Khan, Shadab, Yash Veer Singh, Prasant Singh Yadav, Vishnu Sharma, Chia-Chen Lin, and Ki-Hyun Jung. "An Intelligent Bio-Inspired Autonomous Surveillance System Using Underwater Sensor Networks." Sensors 23, no. 18 (2023): 7839. http://dx.doi.org/10.3390/s23187839.

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Energy efficiency is important for underwater sensor networks. Designing such networks is challenging due to underwater environmental traits that hinder network lifespan extension. Unlike terrestrial protocols, underwater settings require novel protocols due to slower signal propagation. To enhance energy efficiency in underwater sensor networks, ongoing research concentrates on developing innovative solutions. Thus, in this paper, an intelligent bio-inspired autonomous surveillance system using underwater sensor networks is proposed as an efficient method for data communication. The tunicate

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Doumari, Sajjad Amiri, Hadi Givi, Mohammad Dehghani, Zeinab Montazeri, Victor Leiva, and Josep M. Guerrero. "A New Two-Stage Algorithm for Solving Optimization Problems." Entropy 23, no. 4 (2021): 491. http://dx.doi.org/10.3390/e23040491.

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Optimization seeks to find inputs for an objective function that result in a maximum or minimum. Optimization methods are divided into exact and approximate (algorithms). Several optimization algorithms imitate natural phenomena, laws of physics, and behavior of living organisms. Optimization based on algorithms is the challenge that underlies machine learning, from logistic regression to training neural networks for artificial intelligence. In this paper, a new algorithm called two-stage optimization (TSO) is proposed. The TSO algorithm updates population members in two steps at each iteratio

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Sholikhah, Evi Nafiatus, Novie Ayub Windarko, and Bambang Sumantri. "Tunicate swarm algorithm based maximum power point tracking for photovoltaic system under non-uniform irradiation." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 4559. http://dx.doi.org/10.11591/ijece.v12i5.pp4559-4570.

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<span>A new maximum power point tracking (MPPT) technique based on the bio-inspired metaheuristic algorithm for photovoltaic system (PV system) is proposed, namely tunicate swarm algorithm-based MPPT (TSA-MPPT). The proposed algorithm is implemented on the PV system with five PV modules arranged in series and integrated with DC-DC buck converter. Then, the PV system is tested in a simulation using PowerSim (PSIM) software. TSA-MPPT is tested under varying irradiation conditions both uniform irradiation and non-uniform irradiation. Furthermore, to evaluate the performance, TSA-MPPT is com

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Sadeghi, Ali, Sajjad Amiri Doumari, Mohammad Dehghani, Zeinab Montazeri, Pavel Trojovský, and Hamid Jafarabadi Ashtiani. "A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems." Applied Sciences 11, no. 10 (2021): 4382. http://dx.doi.org/10.3390/app11104382.

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Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups

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Dehghani, Mohammad, Štěpán Hubálovský, and Pavel Trojovský. "Cat and Mouse Based Optimizer: A New Nature-Inspired Optimization Algorithm." Sensors 21, no. 15 (2021): 5214. http://dx.doi.org/10.3390/s21155214.

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Numerous optimization problems designed in different branches of science and the real world must be solved using appropriate techniques. Population-based optimization algorithms are some of the most important and practical techniques for solving optimization problems. In this paper, a new optimization algorithm called the Cat and Mouse-Based Optimizer (CMBO) is presented that mimics the natural behavior between cats and mice. In the proposed CMBO, the movement of cats towards mice as well as the escape of mice towards havens is simulated. Mathematical modeling and formulation of the proposed C

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Abedallah, Abedallah, and Rasha Almajed. "Improving Network Security using Tunicate Swarm Algorithm with Stacked Deep Learning Model on IoT Environment." International Journal of Wireless and Ad Hoc Communication 8, no. 2 (2024): 67–80. http://dx.doi.org/10.54216/ijwac.080207.

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The Internet of Things (IoT) represents important security vulnerabilities, increasing difficulties in cyberattacks. Attackers employ these vulnerabilities to establish distributed denial-of-service (DDoS) attacks, compromising availability and causing financial losses to digital platforms. Newly, numerous Machine Learning (ML) and Deep Learning (DL) approaches have been presented for the identification of botnet attacks in IoT networks. By analyzing the patterns of communication and behavior of IoT devices, DL algorithms will be differentiated between malicious and normal activity, therefore

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Fayek, Hady H., and Panos Kotsampopoulos. "Central Tunicate Swarm NFOPID-Based Load Frequency Control of the Egyptian Power System Considering New Uncontrolled Wind and Photovoltaic Farms." Energies 14, no. 12 (2021): 3604. http://dx.doi.org/10.3390/en14123604.

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This paper presents load frequency control of the 2021 Egyptian power system, which consists of multi-source electrical power generation, namely, a gas and steam combined cycle, and hydro, wind and photovoltaic power stations. The simulation model includes five generating units considering physical constraints such as generation rate constraints (GRC) and the speed governor dead band. It is assumed that a centralized controller is located at the national control center to regulate the frequency of the grid. Four controllers are applied in this research: PID, fractional-order PID (FOPID), non-l

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Srinivas, Paruchuri, and P. Swapna. "Quantum tunicate swarm algorithm based energy aware clustering scheme for wireless sensor networks." Microprocessors and Microsystems 94 (October 2022): 104653. http://dx.doi.org/10.1016/j.micpro.2022.104653.

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Daniel, Jesline, Sangeetha Francelin Vinnarasi Francis, and S. Velliangiri. "Cluster head selection in wireless sensor network using tunicate swarm butterfly optimization algorithm." Wireless Networks 27, no. 8 (2021): 5245–62. http://dx.doi.org/10.1007/s11276-021-02812-x.

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侯, 磊磊. "PID Parameter Tuning of Quadrotor Attitude Control Based on Mended Tunicate Swarm Algorithm." Journal of Sensor Technology and Application 12, no. 02 (2024): 175–86. http://dx.doi.org/10.12677/jsta.2024.122020.

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Kaur, Satnam, Lalit K. Awasthi, A. L. Sangal, and Gaurav Dhiman. "Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization." Engineering Applications of Artificial Intelligence 90 (April 2020): 103541. http://dx.doi.org/10.1016/j.engappai.2020.103541.

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Alharbi, Ali H., Salem Alkhalaf, Yousef Asiri, Sayed Abdel-Khalek, and Romany F. Mansour. "Automated Fruit Classification using Enhanced Tunicate Swarm Algorithm with Fusion based Deep Learning." Computers and Electrical Engineering 108 (May 2023): 108657. http://dx.doi.org/10.1016/j.compeleceng.2023.108657.

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Rama, Rama, and Arwa Hajjari. "Intelligent Data Processing and Mining of Histopathological Images using Improved Tunicate Swarm Algorithm with Deep Learning." International Journal of Advances in Applied Computational Intelligence 5, no. 1 (2024): 40–55. http://dx.doi.org/10.54216/ijaaci.050104.

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Intelligent data processing and mining of histopathological images involve the application of advanced techniques and algorithms to analyze and extract meaningful information from digital pathology images. Osteosarcoma is a general malignant bone cancer generally established in teenagers and children. Manual diagnoses of osteosarcoma is a laborious task and needs skilled professionals. The mortality rate can be minimalized only if it is identified on time. Automatic detection systems and new technologies were utilized to classify and analyze medical images that, minimalize the dependency on sp

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El-Sayed, El, Amel Ali Alhussan, Doaa Sami Khafaga, et al. "Comment Feedback Optimization Algorithm (CFOA): A Feedback-Driven Framework for Robust and Adaptive Optimization." Journal of Intelligent Systems and Internet of Things 16, no. 2 (2025): 123–41. https://doi.org/10.54216/jisiot.160210.

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The Comment Feedback Optimization Algorithm (CFOA) presented a novel feedback-driven model for solving optimization problems, incorporating ideas based on positive and negative feedback loops. Unlike other optimization algorithms, CFOA includes feedback adjustments for better tuning the exploration-exploitation trade-off, thus making CFOA less sensitive to the dimensions of problems and their nonlinearity. Some proposed features include feedback dynamics for adaptive search options, parameter control by a decay function, and mechanisms for escaping local optima. CFOA’s performance has been ben

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Larouci, Benyekhlef, Ahmed Nour El Islam Ayad, Hisham Alharbi, et al. "Investigation on New Metaheuristic Algorithms for Solving Dynamic Combined Economic Environmental Dispatch Problems." Sustainability 14, no. 9 (2022): 5554. http://dx.doi.org/10.3390/su14095554.

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In this paper, the dynamic combined economic environmental dispatch problems (DCEED) with variable real transmission losses are tackled using four metaheuristics techniques. Due to the consideration of the valve-point loading effects (VPE), DCEED have become a non-smooth and more complex optimization problem. The seagull optimization algorithm (SOA), crow search algorithm (CSA), tunicate swarm algorithm (TSA), and firefly algorithm (FFA), as both nature and biologic phenomena-based algorithms, are investigated to solve DCEED problems. Our proposed algorithms, SOA, TSA, and FFA, were evaluated

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Althaqafi, Turki. "Mathematical modeling of a Hybrid Mutated Tunicate Swarm Algorithm for Feature Selection and Global Optimization." AIMS Mathematics 9, no. 9 (2024): 24336–58. http://dx.doi.org/10.3934/math.20241184.

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<p>The latest advances in engineering, science, and technology have contributed to an enormous generation of datasets. This vast dataset contains irrelevant, redundant, and noisy features that adversely impact classification performance in data mining and machine learning (ML) techniques. Feature selection (FS) is a preprocessing stage to minimize the data dimensionality by choosing the most prominent feature while improving the classification performance. Since the size data produced are often extensive in dimension, this enhances the complexity of search space, where the maximal number

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Danin, Zekharya, Abhishek Sharma, Moshe Averbukh, and Arabinda Meher. "Improved Moth Flame Optimization Approach for Parameter Estimation of Induction Motor." Energies 15, no. 23 (2022): 8834. http://dx.doi.org/10.3390/en15238834.

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The effective deployment of electrical energy has received attention because of its environmental implications. On the other hand, induction motors are the primary equipment used in many industries. Industrial facilities demand the maximum percentage of energy. This energy demand is determined by the operating circumstances imposed by the internal characteristics of the induction motor. Because internal parameters of an induction motor are not immediately measurable, they must be obtained through an identification process. This paper proposed an improved version of moth flame optimization (IMF

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Cui, Yi, Ronghua Shi, and Jian Dong. "CLTSA: A Novel Tunicate Swarm Algorithm Based on Chaotic-Lévy Flight Strategy for Solving Optimization Problems." Mathematics 10, no. 18 (2022): 3405. http://dx.doi.org/10.3390/math10183405.

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In this paper, we proposed a tunicate swarm algorithm based on Tent-Lévy flight (TLTSA) to avoid converging prematurely or failing to escape from a local optimal solution. First, we combined nine chaotic maps with the Lévy flight strategy to obtain nine different TSAs based on a Chaotic-Lévy flight strategy (CLTSA). Experimental results demonstrated that a TSA based on Tent-Lévy flight (TLTSA) performed the best among nine CLTSAs. Afterwards, the TLTSA was selected for comparative research with other well-known meta-heuristic algorithms. The 16 unimodal benchmark functions, 14 multimodal bench

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Zhang, Yu, Qing He, Liu Yang, and Chenghan Liu. "An Improved Tunicate Swarm Algorithm for Solving the MultiObjective Optimisation Problem of Airport Gate Assignments." Applied Sciences 12, no. 16 (2022): 8203. http://dx.doi.org/10.3390/app12168203.

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Airport gate assignment is a critical issue in airport operations management. However, limited airport parking spaces and rising fuel costs have caused serious issues with gate assignment. In this paper, an effective multiobjective optimisation model for gate assignment is proposed, with the optimisation objectives of minimising real-time flight conflicts, maximising the boarding bridge rate, and minimising aircraft taxiing fuel consumption. An improved tunicate swarm algorithm based on cosine mutation and adaptive grouping (CG-TSA) is proposed to solve the airport gate assignment problem. Fir

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Gandikota, Hari Prasad, Abirami S., and Sunil Kumar M. "CT scan pancreatic cancer segmentation and classification using deep learning and the tunicate swarm algorithm." PLOS ONE 18, no. 11 (2023): e0292785. http://dx.doi.org/10.1371/journal.pone.0292785.

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Pancreatic cancer (PC) is a very lethal disease with a low survival rate, making timely and accurate diagnoses critical for successful treatment. PC classification in computed tomography (CT) scans is a vital task that aims to accurately discriminate between tumorous and non-tumorous pancreatic tissues. CT images provide detailed cross-sectional images of the pancreas, which allows oncologists and radiologists to analyse the characteristics and morphology of the tissue. Machine learning (ML) approaches, together with deep learning (DL) algorithms, are commonly explored to improve and automate

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Nhu Nguyen, Gia, Nin Ho Le Viet, Gyanendra Prasad Joshi, and Bhanu Shrestha. "Intelligent Tunicate Swarm-Optimization-Algorithm-Based Lightweight Security Mechanism in Internet of Health Things." Computers, Materials & Continua 66, no. 1 (2020): 551–62. http://dx.doi.org/10.32604/cmc.2020.012441.

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Li, Yun, Yufei Wu, Xiaohui Zhang, Xinglin Tan, and Wei Zhou. "Unmanned Bicycle Balance Control Based on Tunicate Swarm Algorithm Optimized BP Neural Network PID." International Journal of Information Technologies and Systems Approach 16, no. 3 (2023): 1–16. http://dx.doi.org/10.4018/ijitsa.324718.

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In this study, the authors introduce a novel approach that leverages the tunicate swarm algorithm (TSA) to optimize proportional-integral-derivative (PID) controller based on a back propagation (BP) neural network. The core objective of the approach is to manage and counteract uncertainties and disturbance that may jeopardize the balance and stability of self-driving bicycles in operation. By using the self-learning capabilities of BP neural networks, the controller can dynamically adjust PID parameters in real time. This enables an enhanced robustness and reliability during operation. Further

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Shaban, Hassan, Essam H. Houssein, Marco Pérez-Cisneros, et al. "Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimizer." Mathematics 9, no. 18 (2021): 2313. http://dx.doi.org/10.3390/math9182313.

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Recently, the resources of renewable energy have been in intensive use due to their environmental and technical merits. The identification of unknown parameters in photovoltaic (PV) models is one of the main issues in simulation and modeling of renewable energy sources. Due to the random behavior of weather, the change in output current from a PV model is nonlinear. In this regard, a new optimization algorithm called Runge–Kutta optimizer (RUN) is applied for estimating the parameters of three PV models. The RUN algorithm is applied for the R.T.C France solar cell, as a case study. Moreover, t

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Shaheen, Abdullah, Ragab El-Sehiemy, Salah Kamel, and Ali Selim. "Optimal Operational Reliability and Reconfiguration of Electrical Distribution Network Based on Jellyfish Search Algorithm." Energies 15, no. 19 (2022): 6994. http://dx.doi.org/10.3390/en15196994.

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In this paper, the electricity network automation based on Power Network Reconfiguration (PNR) is implemented to improve the operational reliability of distribution systems using jellyfish search algorithm. For this purpose, system average interruption frequency index (SAIFI), system average interruption unavailability index (SAIUI) and total energy not supplied (TENS) are critical measures. In this paper, a new optimization technique of jellyfish search (JFS) algorithm is employed for distribution network reconfiguration for reliability improvement. It is concerned with the moving patterns of

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Najm Al-Din Abed, Wisam. "Modern Meta-Heuristic Algorithms for Solving Combined Economic and Emission Dispatch." Iraqi Journal for Electrical and Electronic Engineering 21, no. 2 (2025): 76–87. https://doi.org/10.37917/ijeee.21.2.8.

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The traditional economic dispatch (ED) inattention to the fossil fuels emission of thermal power plants no longer satisfies the environmental needs. As a result of the non-convex, non-smooth fuel cost functions in addition to the nonlinearity of the emission modelling. These make the combined economic and emission dispatch (CEED) a highly nonlinear optimization problem. Furthermore, different operation process constraints should be taken into account, such as loss in electrical networks and power balance of unit operation. These constraints increase the difficulty of obtaining the global optim

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Irshad, Ahmad Thukroo, Bashir Rumaan, and Kaiser Giri J. "Improved Support Vector-Recurrent Neural Network with Optimal Feature Selection-based Spoken Language Identification System." Indian Journal of Science and Technology 16, no. 10 (2023): 680–97. https://doi.org/10.17485/IJST/v16i10.2119.

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Abstract <strong>Objective:</strong> Spoken language identification being the fore-front of language recognition tasks and most significant medium of communication has to be enhanced in order to improve the accuracy of recently developed spoken language recognition systems. The purpose of this paper is to enhance the Spoken Language Identification (SLID) model using hybrid machine learning with deep learning model for regionally spoken languages of Jammu & Kashmir (JK) and Ladakh.<strong> Method:</strong> Initially, the speech signals of different languages of JK and Ladakh

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