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Optimal Design of Hybrid Stand-alone Microgrids Using tunicate swarm algorithm

Optimal Design of Hybrid Stand-alone Microgrids Using Tunica...

IEEE IFAC International Conference on Automation (ICA) / 24th Congress of the Chilean-Association-of-Automatic-Control (ACCA)

作者： Abd El-Sattar, Hoda Sultan, Hamdy M. Kamel, Salah Menesy, Ahmed S. Rahmann, Claudia Luxor Higher Inst Engn & Technol Luxor Dept Elect Engn Luxor Egypt Minia Univ Dept Elect Engn Al Minya Egypt Aswan Univ Dept Elect Engn Aswan Egypt Univ Chile Dept Elect Engn Santiago Chile

ISBN: (纸本)9781665401272

The current paper presents an approach for optimal configuration of stand-alone hybrid microgrids, while using battery storage system and diesel generator as a backup power supply. The proposed methodology highlights the application of a novel metaheuristic optimization technique, called tunicate swarm algorithm (TSA) for optimal design of the proposed microgrid. A case study is carried out in an oasis located in the southwest of Egypt for a hybrid system consists of photovoltaic (PV) panels, wind turbines (WT), battery storage, and diesel generator. The main target of the proposed methodology is to obtain the optimal installed capacities of the microgrid components while ensuring the minimum energy production cost and satisfying a high reliability index. The results given in this paper demonstrated that the proposed hybrid microgrid system is a profitable choice for electrification in this region.

关键词： Hybrid system Optimization Cost of Energy tunicate swarm algorithm Loss of power supply probability

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Commentary on: "STOA: A bio-inspired based optimization algorithm for industrial engineering problems'' [EAAI, 82 (2019), 148-174] and "tunicate swarm algorithm: A new bio-inspired based metaheuristic paradigm for global optimization'' [EAAI, 90 (2020), no. 103541]

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期113卷

作者： Kudela, Jakub Brno Univ Technol Inst Automat & Comp Sci Brno Czech Republic

This commentary concerns two recently developed metaheuristic algorithms, namely the Sooty Tern Optimization algorithm, and the tunicate swarm algorithm. Both of these algorithms claim computational superiority over other methods based on experimental results on a certain benchmark set. The aim of this note is to aware researchers that this claim is not valid: the proposed algorithms use a zero-bias operator and many of the studied benchmark functions on which they were found superior have optimal solutions located in the zero vector. Moreover, the codes for the methods provided by the authors are not achieving the results reported in the respective publications.

关键词： Sooty Tern Optimization tunicate swarm algorithm Metaheuristic optimization Benchmarking

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Modified Parallel tunicate swarm algorithm and Application in 3D WSNs Coverage Optimization

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JOURNAL OF INTERNET TECHNOLOGY 2022年第2期23卷 227-244页

作者： Li, Jianpo Li, Geng-Chen Chu, Shu-Chuan Gao, Min Pan, Jeng-Shyang Northeast Elect Power Univ Sch Comp Sci Jilin Jilin Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Shandong Peoples R China Chaoyang Univ Technol Dept Informat Management Taichung Taiwan

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 proposed MPTSA is implemented and tested on 23 benchmark functions to verify the algorithm performance. Finally, a WSNs network layout scheme based on MPTSA is proposed to improve the coverage of the whole network. Experimental results show that, compared with the traditional PSO (Particle swarm Optimization), improved PSO (PPSO and APSO), GBMO (Gases Brownian Motion Optimization) and traditional TSA, MPTSA family algorithms show better performance in WSNs network layout.

关键词： Network layout Modified parallel TSA tunicate swarm algorithm Wireless sensor networks

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A novel modified tunicate swarm algorithm for optimal charge scheduling and pricing of electric vehicles with user priority-based charging station selection

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第3期35卷 e7494-e7494页

作者： Nair, Lakshmi M. Prabha, D. M. Mary Synthia Regis Noorul Islam Ctr Higher Educ Kanyakumari Tamil Nadu India Noorul Islam Ctr Higher Educ Kanyakumari 629180 Tamil Nadu India

In recent days, electric vehicles (EVs) are seen as a viable transportation alternative by industry people to minimize the greenhouse gas emissions problems of regular fuel vehicles. While EVs' performance is limited due to their restricted battery power, charging wait for service, and high resource costs, they are still a viable option. To enhance the EV's performance, this work introduced an optimized priority-based charge scheduling with dynamic pricing model. For this, a novel modified tunicate swarm algorithm method, which is the enhanced form of the standard tunicate swarm algorithm, is proposed. When an EV arrives at the charging station, the EV user is permitted to choose the type of charging based on his/her requirements. In general, when charging EVs in public stations or during peak hours, customers must be patient and wait for significant periods of time. The introduced model ignores this negative effect. EV users have also mentioned the issue of controlled charging rates as a key barrier. This is a difficult task, but it is accomplished in this study, which optimizes total charge costs. Finally, the results acquired are discussed under overall cost and energy consumption.

关键词： charge scheduling EV optimal pricing priority based charger allocation tunicate swarm algorithm

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Enhanced tunicate swarm algorithm for optimizing shape of C2 RQI-spline curves

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期121卷

作者： Hu, Gang Zheng, Jiaoyue Ji, Xiaomin Qin, Xinqiang Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Xian Univ Technol Sch Mech & Precis Instrument Engn Xian 710048 Peoples R China Xian Univ Technol 5 South Jinhua Rd Xian 710048 Peoples R China

The shape optimization of interpolation curves is a crucial and intractable technique in scientific visualization, data analysis, manufacturing and computer-aided design and is potentially used in many engineering fields involving geometric modeling. In this paper, an enhanced hybrid tunicate swarm algorithm (TSA) is used to optimize the shape of complex rational quartic interpolation spline (RQI-spline, for short) curves. Firstly, to modify the shape of the interpolation curve more flexibly, a novel multi-parameter RQI-spline curve with local and global shape parameters is presented. Then the corresponding combined RQI-spline curves with 2th-order parametric continuity denoted C2 are constructed. In addition, considering that the optimization of shape parameters for RQI-spline curves is a complex optimization problem, an enhanced hybrid version of TSA called DQTSA is developed. The introduction of differential evolution and quadratic interpolation strategies enhances the ability of the TSA to jump out of local minima, thus effectively solving the shape parameter optimization of RQI-spline curves. DQTSA and some popular comparison algorithms are used to solve the set of 23 benchmark functions and CEC2019 test functions. The experimental results show that DQTSA performs the best in 7 (30.43%) of the 23 test problems, while DQTSA performs the best in 3 of the 10 CEC2019 suites. Meanwhile, applying DQTSA to three engineering optimization problems, the best value, mean and significantly better than other search algorithms are obtained from 20 optimized designs, which verifies the applicability and stability of DQTSA in solving engineering optimization problems. Finally, the proposed DQTSA is used to solve the shape optimization model for the combined C2 RQI-spline curve. Numerical results find that DQTSA obtains the optimal RQI-spline curve with minimum energy. Some representative numerical examples illustrate that DQTSA is a potentially excellent algorithm for solving

关键词： C2 Rational quartic interpolation spline curves Shape parameter Shape optimization tunicate swarm algorithm Differential evolution Quadratic interpolation strategies

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Quantum tunicate swarm algorithm based energy aware clustering scheme for wireless sensor networks

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MICROPROCESSORS AND MICROSYSTEMS 2022年 94卷

作者： Srinivas, Paruchuri Swapna, P. Andhra Univ Coll Engn Dept Instrument Technol Visakhapatnam 530003 India VR Siddhartha Engn Coll Dept Elect & Instrumentat Engn Vijayawada 520007 India Fac Andhra Univ Coll Engn Dept Instrument Technol Visakhapatnam 530003 India Velagapudi Ramakrishna Siddhartha Engn Coll Elect & Instrumentat Engn Flat 301 Vijayawada 520007 Andhra Pradesh India

Wireless Sensor Network (WSN) is represented by a group of sensor nodes with limited battery capacity deployed in a target region for data collection purposes. Energy is treated as a major issue from the design of WSN as it mainly affects the overall functioning of the network. A commonly employed energy-efficient mechanism is clustering which groups the nodes into several groups of clusters. Earlier works reported that clustering is treated as an NP hard issue which can be addressed by soft computing techniques. In this way, this article focuses on the design of Quantum tunicate swarm algorithm Based Energy Aware Clustering (QTSA-EAC) scheme for WSN. The presented QTSA-EAC technique mainly attempts to effectively manage the available energy among the nodes in WSN. In addition, the QTSA is derived by the incorporation of quantum computing concepts as to the traditional TSA. Moreover, the QTSA-EAC technique derives a fitness function with the minimization of total energy consumption in the networks. The inclusion of multiple parameters in the cluster head (CH) selection process helps to accomplish energy efficiency and maximum lifetime of WSN. The performance assessment of the QTSA-EAC technique is performed and the results reported that the presented model is effective, improves lifetime, reduce energy exploitation, and delay over the other methods.

关键词： Energy management Clustering WSN Soft computing tunicate swarm algorithm Fitness function

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A novel remaining useful life prediction method based on gated recurrent unit network optimized by tunicate swarm algorithm for lithium-ion batteries

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2024年

作者： Zhai, Qianchun Sun, Jing Shang, Yunlong Wang, Haofan Shandong Technol & Business Univ Coll Informat & Elect Engn Yantai 264005 Shandong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Shandong Peoples R China

Lithium-ion batteries have a wide range of applications in the field of new energy vehicles with advantages including small size, high efficiency, and low pollution. Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is particularly important to reduce unintended maintenance and avoid safety incidents caused by battery aging. To improve the accuracy and robustness of the RUL prediction of lithium-ion batteries, a prediction method is proposed based on gated recurrent unit (GRU) network optimized by tunicate swarm algorithm (TSA) in this paper. First, the capacity data during the life cycle of the aging battery are extracted as the prediction feature. Then, the GRU network is used to capture the dependencies between degraded capacities for RUL prediction. The main hyperparameters in the GRU network are optimized by the TSA to maximize the prediction performance. Finally, to ensure the validity and generalizability of the proposed RUL prediction method, data sets from University of Maryland, National Aeronautics and Space Administration (NASA), and our own laboratory are selected for validation. The superiority of the proposed method is verified by comparison with other different prediction methods.

关键词： Lithium-ion batteries remaining useful life gated recurrent unit tunicate swarm algorithm

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Automated Fruit Classification using Enhanced tunicate swarm algorithm with Fusion based Deep Learning

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COMPUTERS & ELECTRICAL ENGINEERING 2023年第1期108卷

作者： Alharbi, Ali H. Alkhalaf, Salem Asiri, Yousef Abdel-Khalek, Sayed Mansour, Romany F. Qassim Univ Coll Publ Hlth & Hlth Informat Dept Hlth Informat Al Bukayriyah Saudi Arabia Qassim Univ Coll Sci & Arts Ar Rass Dept Comp Ar Rass Saudi Arabia Najran Univ Coll Comp Sci & Informat Syst Sci & Engn Res Ctr Dept Comp Sci Najran 61441 Saudi Arabia Taif Univ Coll Sci Dept Math Taif 21944 Saudi Arabia New Valley Univ Fac Sci Dept Math El Kharga Egypt

Automatic fruit classification is an interesting problem in retail supply chain processes and in fruit industry, since the process can help the supermarkets as well as the fruit growers find various fruits and their statuses from the containers or stock. In this background, the current research article designs an Automated Fruit Classification system using Enhanced tunicate swarm Algo-rithm with Fusion-based Deep Learning (AFC-ETSAFDL) technique. The goal of the proposed AFC-ETSAFDL technique is to recognize different types of fruits from the captured images. In the presented technique, a fusion-based feature extraction method is applied using three DL models namely, DenseNet, ResNet, and Inception v3 with ETSA as a hyperparameter optimizer. For fruit classification, the Extreme Gradient Boosting (XGBoost) model is used in this study. The proposed model was experimentally evaluated using an open-access fruit dataset and the results showcase the enhanced performance of the proposed technique over other existing models.

关键词： Fruit classification Intelligent systems Computer vision Artificial intelligence tunicate swarm algorithm Fusion process

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Optimal Control and Operation of Fully Automated Distribution Networks Using Improved tunicate swarm Intelligent algorithm

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IEEE ACCESS 2020年 8卷 129689-129708页

作者： Fetouh, Tamer Elsayed, Abdallah M. Northern Border Univ Coll Engn Elect Engn Dept Ar Ar 91431 Saudi Arabia Menou Univ Fac Engn Elect Engn Dept Shibin Al Kawm 32511 Egypt Damietta Univ Fac Engn Elect Engn Dept Dumyat 34517 Egypt

Simultaneous control and operation of Capacitor Banks (CBs) and Distributed Generators (DGs) with Distribution Network Reconfiguration (DNR) is the most important modern trend for distribution systems performance enhancement. The complexity and variety of the control variables of this problem represent a challenge for distribution systems planners and operators as well as interested researchers. Optimal solution can provides quantitative as well as qualitative power service to satisfy consumers' satisfaction and reduces dissipated energy. Furthermore, performance enhancement of distribution systems is very vital where improving their performance directly affects the performance of transmission and generation systems. For solving the considered problem, an improved tunicate swarm algorithm (ITSA), which imitates the swarming behaviors of the marine tunicates and their jet propulsions during its navigation and foraging procedure, is proposed. In ITSA, Levy flight distributions are emerged in the traditional tunicate swarm algorithm (TSA), which improves the diversification searching abilities of the TSA and consequently avoids the stagnation possibilities. The proposed ITSA is applied and tested for optimal DNR simultaneously with optimal control of the switched CBs and dispatchable DGs taking into account daily load variations. The ITSA is compared with other techniques on the standard 33-bus, 69-bus and the large-scale 119-bus distribution systems considering different automation scenarios of the distribution systems. The simulation results reveal that significant enhancement in distribution system performance is obtained through the application of the proposed automation process using the ITSA. ITSA can efficiently search for the optimal solutions of the problem and outperforms the other existing algorithms in the literatures.

关键词： Switches Automation Reliability Optimal control Performance evaluation Power system reliability Resource management Automated distribution systems distributed generators power losses tunicate swarm algorithm voltage stability distribution network reconfiguration

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Prediction of Unconfined Compressive Strength of Stabilized Sand Using Machine Learning Methods

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INDIAN GEOTECHNICAL JOURNAL 2025年第1期55卷 315-332页

作者： Zhao, Qinggang Shi, Yan Zhengzhou Railway Vocat & Tech Coll Sch Art Zhengzhou 450000 Henan Peoples R China Huanghe S&T Univ Fac Engn Zhengzhou 450000 Henan Peoples R China

This article introduces a novel method using the K-nearest neighbors (KNN) model to predict the unconfined compressive strength (UCS) of soil-stabilizer combinations. To ensure accurate UCS estimation, customized KNN prediction models are created. The research combines two meta-heuristic algorithms, the giant trevally optimizer (GTO) and the tunicate swarm algorithm (TSA), for improved accuracy. These algorithms examine UCS samples made from various soil types and validate the models using the results of previous stabilization tests. The study's findings point to three distinct models: KNGT, KNTS, and an independent KNN model. Each of these models offers insightful information that helps with the precise prediction of UCS. As a result, the KNGT model performs best by displaying suitable statistics like a high R2 value of 0.995 and a low RMSE value of 84.5 (kN/m2). In addition, KNTS obtained the second suitable performance with R2 and RMSE equal to 0.989 and 121.2 (kN/m2), alternatively. These results highlight the accuracy, dependability, and prognostication capabilities of the KNGT model. This study describes a unique machine-learning approach for the exact prediction of critical soil parameters, with an emphasis on UCS in civil engineering. In order to overcome data difficulties, it leverages the KNN model and presents a novel dual-algorithm technique that combines the GTO and TSA. This integration improves KNN's accuracy and efficiency while also easing UCS structure planning in civil engineering. The work not only improves UCS prediction accuracy but also opens up a viable route for future research and practical applications in civil engineering undertakings, giving significant insights and a fresh method for UCS performance prediction.

关键词： Unconfined compressive strength Machine learning K-nearest neighbors Giant trevally optimizer tunicate swarm algorithm

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