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Hybrid Bio Inspire algorithm used for Optimum Path Communication and Improved Power Conserving Routing Protocol Design for Underwater Acoustic Sensor Network

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2025年第2期13卷 1-15页

作者： Kannappan, A. Kolagani, Nagesh SIMATS Saveetha Sch Engn Inst ECE Chennai India SIMATS Inst CSE Saveetha Sch Engn Chennai India

BackgroundUnderwater Acoustic Sensor Networks (UASNs) necessitate effective cluster head (CH) selection to enhance energy efficiency, prolong network longevity, and augment data transfer. Conventional methods frequently encounter energy inefficiencies, resulting in diminished network *** study presents a new method called the Hybrid tunicate-Whale Optimisation algorithm (HTWOA), which is based on natural processes to improve CH selection in UASNs. The goal is to make power use and network speed better by sorting nodes according to how much energy they *** leverages the tunicate swarm algorithm (TSA) to identify nodes as low-, intermediate-, or high-power users. The suggested technique was assessed using 120-node simulations in a 1500 m3 3D underwater environment. Performance parameters, including power consumption, packet delivery ratio, latency, and network lifespan, were tested to Depth-based Routing (DR), Power Conserving Depth-based Routing (PCDR), and Power Conserving Layered Cluster Head Rotation (PCLCHR).ResultsThe HTWOA protocol achieved a 30% decrease in power usage compared to conventional approaches. With a 100% increase in node count, the packet delivery ratio exhibited a small improvement. Additionally, the end-to-end latency dropped by about 20%, while network lifespan increased by up to 25%.ConclusionThe proposed HTWOA-based protocol considerably increases CH selection in UASNs, leading to better energy efficiency, decreased latency, and prolonged network longevity. These findings imply that HTWOA is an excellent approach for optimizing UASN performance and ensuring efficient data transmission.

关键词： Bio inspire algorithm Power conserving tunicate swarm algorithm Underwater acoustic sensor network Packet delivery ratio

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Automated machine learning techniques for estimating the unconfined compressive strength of soil stabilization

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年 1-13页

作者： Wang, Lei Lian Yungang Tech Coll Sch Mech & Elect Engn Lianyungang 222006 Jiangsu Peoples R China

This article outlines a new approach to predict the Unconfined compressive strength (UCS) of soil stabilization blends more accurately. It applies the Decision tree (DT) algorithm to develop accurate and comprehensive models. The developed model here utilizes several natural soil parameters, such as type and dosage of stabilizer additives, plasticity, linear shrinkage, and particle distribution. It can be predicted that the DT algorithm will provide better forecasting accuracy by developing meaningful correlations between these properties and UCS in stabilized soil. Two new, efficient meta-heuristic algorithms, the tunicate swarm algorithm (TSA) and the Sea-horse optimizer (SHO), have been integrated into the analysis to enhance the precision of the model. These are the DTSH, DTTS, and DT hybrid models. The DTSH model outperforms the other models developed for the study, with superior predictive abilities and exceptional generalization following an intensive evaluation based on a wide range of soil types compiled from previous stabilization test results. The DTSH model boasts remarkable R2 values of 0.996 and a perfect RMSE of 67.89 during training, indicating exceptionally high accuracy and reliability. In general, these strategies hold great promise for the precise prediction of UCS in soil stabilization blends for a wide variety of engineering applications. The models also become more accurate when combined with metaheuristic algorithms, hence making more reliable predictions. Such developments significantly impact the building sector, where soil stabilization is essential for building resilient and long-lasting infrastructure.

关键词： Unconfined compressive strength Decision tree tunicate swarm algorithm Sea-horse optimizer

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Opposition-Based Chaotic tunicate swarm algorithms for Global Optimization

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IEEE ACCESS 2024年 12卷 18168-18188页

作者： Si, Tapas Miranda, Pericles B. C. Nandi, Utpal Jana, Nanda Dulal Mallik, Saurav Maulik, Ujjwal Qin, Hong Univ Engn & Management Jaipur Dept Comp Sci & Engn AI Innovat Lab Jaipur 303807 Rajasthan India Univ Fed Rural Pernambuco UFRPE Dept Computacao DC BR-52171900 Recife PE Brazil Vidyasagar Univ Dept Comp Sci Paschim Medinipur 721102 W Bengal India Natl Inst Technol Durgapur Dept Comp Sci & Engn Durgapur 713209 W Bengal India Harvard TH Chan Sch Publ Hlth Dept Environm Hlth Boston MA 02115 USA Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Univ Tennessee Dept Comp Sci & Engn Chattanooga TN 37403 USA

tunicate swarm algorithm (TSA) is a novel swarm intelligence algorithm developed in 2020. Though it has shown superior performance in numerical benchmark function optimization and six engineering design problems over its competitive algorithms, it still needs further improvements. This article proposes two improved TSA algorithms using chaos theory, opposition-based learning (OBL) and Cauchy mutation. The proposed algorithms are termed OCSTA and COCSTA. The static and dynamic OBL are used respectively in the initialization and generation jumping phase of OCTSA, whereas centroid opposition-based computing is used, in the same phases, in COCTSA. The proposed algorithms are tested on 30 IEEE CEC2017 benchmark optimization problems consists of unimodal, multimodal, hybrid, and composite functions with 30, 50, and 100 dimensions. The experimental results are compared with the classical TSA, TSA with the local escaping operator (TSA-LEO), Sine Cosine algorithm (SCA), Giza-Pyramid Construction algorithm (GPC), Covariance Matrix Adaptation Evolution Strategy (CMAES), Archimedes Optimization algorithm (AOA), Opposition-Based Arithmetic Optimization algorithm (OBLAOA), and Opposition-Based Chimp Optimization algorithm (ChOAOBL). The statistical analysis of experimental results using the Wilcoxon Signed Rank Test establishes that the proposed algorithms outperform TSA and other algorithms for most of the problems. Moreover, high dimensions are used to validate the scalability of OCTSA and COCTSA, and the results show that the modified TSA algorithms are least impacted by larger dimensions. The experimental results with statistical analysis demonstrate the effectiveness of the proposed algorithms in solving global optimization problems.

关键词： tunicate swarm algorithm swarm intelligence metaheuristic opposition-based learning

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Fabric wrinkle rating model based on ResNet18 and optimized random vector functional-link network

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TEXTILE RESEARCH JOURNAL 2023年第1-2期93卷 172-193页

作者： Zhou, Zhiyu Ma, Zijian Wang, Yaming Zhu, Zefei Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou Peoples R China Lishui Univ 1 Xueyuan Rd CN-323000 Lishui Zhejiang Peoples R China Hangzhou Dianzi Univ Sch Mech Engn Hangzhou Peoples R China

To improve the accuracy and efficiency of the objective evaluation of the fabric wrinkle model, the R18-COTSA-RVFL model is proposed in this paper. The fabric wrinkle rating model is based on the combination of ResNet18 (R18) and the enhanced random vector functional-link network, in which the enhanced random vector functional-link network replaces the Softmax layer of ResNet18. This model uses ResNet18 (R18) to extract features from wrinkled images. In addition, a random vector functional-link network model improved by the tunicate swarm algorithm was proposed for wrinkle-level classification. First, we used chaotic-logistic maps and opposition-based learning to optimize the initial population of the tunicate swarm algorithm. The proposed search agent fragment mapping method enables the tunicate swarm algorithm simultaneously to optimize the hidden layer node parameters and bias, input layer weight, and activation function of the random vector functional-link network, thus avoiding the inaccurate classification result caused by random parameter selection in the random vector functional-link network. From comparative experiments, the proposed R18-COTSA-RVFL has the highest average classification accuracy and stability.

关键词： Fabric wrinkle rating ResNet18 chaotic-logistic maps opposition-based learning tunicate swarm algorithm random vector functional link

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A novel interpretable hybrid model for multi-step ahead dissolved oxygen forecasting in the Mississippi River basin

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2024年第12期38卷 4629-4656页

作者： Ali, Hayder Mohammed Ghaleni, Mehdi Mohammadi Moghaddasi, Mahnoosh Moradi, Mansour Arak Univ Fac Agr & Environm Dept Water Sci & Engn Arak Iran Arak Univ Res Inst Water Sci & Engn Arak Iran Natl Univ Skills NUS Dept Elect Engn Tehran Iran

Accurate forecasting of dissolved oxygen (DO) levels is vital for river ecosystem health. A novel methodology, MVMD-TSA-GPR, combines Multivariate Variational Mode Decomposition (MVMD), the tunicate swarm algorithm (TSA), and Gaussian Process Regression (GPR) to improve DO level predictions. This study also incorporated Generalized Additive Model and Regression Bagged Ensemble (RBE) for 1- and 3-month forecasts using monthly data (1974-2023) from 16 water quality parameters across five Mississippi River basin sites. Key predictors identified through cross-correlation include lagged values and parameters like water temperature, discharge, pH, total phosphorus, potassium, and sulfate, which significantly influence DO levels. The MVMD-TSA-GPR model outperformed others, especially at site 5, showing substantial improvements in accuracy with decreased RMSE values across various scenarios. Model ranking via the Taylor Diagram indicated MVMD-TSA-GPR had the highest performance, followed by MVMD-TSA-RBE and others. Notably, the GPR model's RMSE at site 3 decreased from 2.11 to 1.01 (109% reduction) for the 1-month forecast, while at site 4 for the 3-month forecast, it dropped from 1.85 to 1.04 (106% reduction). The results revealed that the MVMD-TSA-GPR model demonstrated the highest performance for DO (t + 1), achieving R = 0.90, PBIAS = 0.73%, and WI = 0.804, as well as for DO (t + 3), with R = 0.88, PBIAS = 0.54%, and WI = 0.779, at the Lower Mississippi site during the test phase. Additionally, the MVMD-TSA-RBE model excelled for DO (t + 1) in the Missouri River at the Hermann site, achieving R = 0.91, PBIAS = 0.86%, and WI = 0.805 during the test phase. These results underscore the effectiveness of the MVMD-TSA hybrid approach. Interpretative analysis using SHapley Additive exPlanations (SHAP) revealed water temperature, pH, and potassium as key factors affecting DO levels. The speed and accuracy of MVMD-TSA-GPR make it a promising tool for monitoring river water quali

关键词： Dissolved oxygen Hybrid models Mississippi River basin Multivariate variational mode decomposition SHAP tunicate swarm algorithm

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Modified Equilibrium Optimization algorithm With Deep Learning-Based DDoS Attack Classification in 5G Networks

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IEEE ACCESS 2023年 11卷 108561-108570页

作者： Aljebreen, Mohammed Alrayes, Fatma S. Maray, Mohammed Aljameel, Sumayh S. Salama, Ahmed S. Motwakel, Abdelwahed King Saud Univ Dept Comp Sci Community Coll Riyadh 11437 Saudi Arabia Princess Nourah bint Abdulrahman Univ Dept Informat Syst Coll Comp & Informat Sci Riyadh 11671 Saudi Arabia King Khalid Univ Dept Informat Syst Coll Comp Sci Abha 62529 Saudi Arabia Imam Abdulrahman Bin Faisal Univ Dept Comp Sci Coll Comp Sci & Informat Technol SAUDI ARAMCO Cybersecur Chair Dammam 31441 Saudi Arabia Future Univ Egypt Fac Engn & Technol Dept Elect Engn New Cairo 11845 Egypt Prince Sattam Bin Abdulaziz Univ Dept Informat Syst Coll Business Adm Hawtat Bani Tamim Al Kharj 16278 Saudi Arabia

5G networks offer high-speed, low-latency communication for various applications. As 5G networks introduce new capabilities and support a wide range of services, they also become more vulnerable to different kinds of cyberattacks, particularly Distributed Denial of Service (DDoS) attacks. Effective DDoS attack classification in 5G networks is a critical aspect of ensuring the security, availability, and performance of these advanced communication infrastructures. In recent days, machine learning (ML) and deep learning (DL) models can be employed for an accurate DDoS attack detection process. In this aspect, this study designs a Modified Equilibrium Optimization algorithm with Deep Learning based DDoS Attack Classification (MEOADL-ADC) method in 5G networks. The goal of the MEOADL-ADC technique is the automated classification of DDoS attacks in the 5G network. The MEOADL-ADC technique follows a three-stage process such as feature selection, classification, and hyperparameter tuning. Primarily, the MEOADL-ADC technique employs MEOA based feature selection approach. Next, the MEOADL-ADC technique utilizes the long short-term memory (LSTM) model for the classification of DDoS attacks. Finally, the tunicate swarm algorithm (TSA) is exploited to adjust the hyperparameter of the LSTM model. The design of MEOA-based feature selection and TSA-based hyperparameter tuning shows the novelty of the work. The experimental outcome of the MEOADL-ADC method is tested on a benchmark dataset, and the outcomes indicate the betterment of the MEOADL-ADC algorithm over the current methods with maximum accuracy of 97.60%.

关键词： Denial-of-service attack 5G mobile communication Feature extraction Optimization Computer crime Tuning Classification algorithms Deep learning 5G networks DDoS attack mitigation security deep learning feature selection tunicate swarm algorithm

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The implementation of a least square support vector regression model utilizing meta-heuristic algorithms for predicting undrained shear strength (Dec, 10.1007/s41939-023-00299-x, 2023)

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第3期7卷 3083-3083页

作者： Shao, Qiang Ma, Chenyue Kong, Dezhi Hebei Univ Water Resources & Elect Engn Fdn Dept Cangzhou 061001 Peoples R China

The undrained shear strength (USS) of soil is widely regarded as a crucial parameter in various structural engineering applications, including the design of foundations, rock fills and earth dams, highways and railways, and slope stability assessment. Furthermore, traditional methodologies exhibit low efficiency concerning both temporal and financial resources. The present study aims to address the limitations of the existing methods by proposing innovative machine learning techniques that employ the least square support vector regression (LSSVR) model for the accurate estimation of the USS. To achieve optimization objectives, this study employed three meta-heuristic optimization techniques, including the tunicate swarm algorithm (TSA), equilibrium optimization (EO), and turbulent flow of water-based optimization (TFWO). The models were trained to utilize a set of four input variables, identified explicitly as liquid limit (LL), plastic limit (PL), overburden weight (OBW), and sleeve friction (SF). In the final phase of the study, the assessment of the models was conducted by taking into consideration five distinct statistical metrics. As a result, LSTS obtained R2 equal to 0.998, and RMSE equal to 34.28, which was the most suitable value compared to other models.

关键词： Equilibrium optimization Least square support vector regression tunicate swarm algorithm Turbulent flow of water-based optimization Undrained shear strength

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Size Optimization of Grid-Tied Hybrid Energy System by Employing Forecasted Meteorological Data

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MAPAN-JOURNAL OF METROLOGY SOCIETY OF INDIA 2024年第3期39卷 739-750页

作者： Anand, Priyanka Sharma, Bandana Rizwan, Mohammad Bhagat Phool Singh Mahila Vishwavidyalaya Dept Elect & Commun Engn Sonipat 131305 India Delhi Technol Univ Dept Elect Engn Delhi 110042 India Krishna Inst Engn & Technol Dept Elect & Elect Engn Ghaziabad 201003 India

Embracing hybrid energy systems (HES) to ensure access to clean, reliable, and cost-effective energy is necessary for nations that are striving for sustainable development. By leveraging precise meteorological data from forecasts, the HES can be rendered more accurate. Thus, firstly, the research presented here employed four machine learning approaches, such as Gaussian process regression (GPR), support vector regression, extreme gradient boosting, and decision trees, to carry out hourly forecasting of meteorological data over a year. The results obtained revealed that the GPR outperformed the other three forecasting models. For this reason, the forecasted meteorological data acquired from GPR is employed in the sizing of the HES. tunicate swarm algorithm (TSA), a recently developed method, is applied to perform the size optimization of HES capable of meeting the energy necessities at remote sites in the Indian province of Uttar Pradesh. Following a comparative study of TSA, particle swarm optimization, and harmony search, TSA proved to yield a better outcome. Additionally, the simulation result showed a 0.33% cut in the per-unit cost of energy when forecasted data becomes the basis for the optimization of system size.

关键词： Hybrid energy system Forecasting tunicate swarm algorithm Machine learning approaches

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Estimating high-performance concrete compressive strength with support vector regression in hybrid method

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第1期7卷 477-490页

作者： Wang, Li Baicheng Normal Univ Coll Civil Engn Baicheng 137000 Jilin Peoples R China

Save time and energy by accurately predicting the mechanical parameters of concrete by employing artificial intelligence (AI) methods. Existing nonlinear relationships between concrete members have introduced uncertainties in estimating compressive strength (CS), among the most essential variables in concrete design. The use of conventional methods for individual use of AI models in predicting dependent components has been adopted in many investigations. The objective of this paper was to create estimation models for the CS of high-performance concrete (HPC) using hybrid approaches. Specifically, the study combined support vector regression (SVR) models with optimization algorithms such as Multi-objective Artificial Vultures Optimization (MAVO), African vulture's optimization algorithm (AVOA), and tunicate swarm algorithm (TSA). Linking the three predictive models and tuning their internal settings via optimization algorithms can result in different sorts of models. After evaluating the results of the proposed models utilizing multiple metrics, the performance of the SVMiA is computed to be greater than that of the other hybrid models, with R2 = 0.9672 and RMSE = 2.1884, respectively. In general, employing advanced kinds of individual models, such as hybrids, showed improved performance with a lower cost modeling process.

关键词： Compressive strength High-performance concrete Support vector regression Multi-objective artificial vultures optimization African vultures optimization algorithm tunicate swarm algorithm

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Design of hybrid deep learning using TSA with ANN for cost evaluation in the plastic injection industry

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FRONTIERS IN MECHANICAL ENGINEERING-SWITZERLAND 2024年 10卷

作者： Kengpol, Athakorn Tabkosai, Pornthip King Mongkuts Univ Technol North Bangkok Fac Engn Adv Ind Engn Management Syst Res Ctr Dept Ind Engn Bangkok Thailand King Mongkuts Univ Technol North Bangkok Fac Engn Dept Ind Engn Bangkok Thailand

In the plastic injection industry, plastic injection molding is one of the most extensively used mass production technologies and has been continuously increasing in recent years. Cost evaluation is essential in corporate operations to increase the market share and lead in plastic part pricing. The complexity of the plastic parts and manufacturing data resulted in a long data waiting time and inaccurate cost evaluation. Therefore, the aim of this research is to apply a cost evaluation approach that combines hybrid deep learning of a tunicate swarm algorithm (TSA) with an artificial neural network (ANN) for the cost evaluation of complicated surface products in the plastic injection industry to achieve a faster convergence rate for optimal solutions and higher accuracy. The methodology entails the ANN, which applies feature-based extraction of 3D-model complicated surface products to develop a cost evaluation model. The TSA is used to construct the initial weight into the learning model of the ANN, which can generate faster-to-convergent optimal solutions and higher accuracy. The result shows that the new hybrid deep learning TSA combined with the ANN provides more accurate cost evaluation than the ANN. The prediction accuracy of cost evaluation is approximately 96.66% for part cost and 93.75% for mold cost. The contribution of this research is the development of a new hybrid deep learning model combining the TSA with the ANN that includes the calculation of the number of hidden layers specifically for complicated surface products, which are unavailable in the literature. The cost evaluation approach can be practically applied and is accurate for complicated surface products in the plastic injection industry.

关键词： deep learning artificial neural network tunicate swarm algorithm cost evaluation plastic injection complicated surface products

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