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Sustainable pavement maintenance and rehabilitation planning using the marine predator optimization algorithm

STRUCTURE AND INFRASTRUCTURE ENGINEERING 2024年第3期20卷 340-352页

作者： Naseri, Hamed Golroo, Amir Shokoohi, Mohammad Gandomi, Amir H. Amirkabir Univ Technol Dept Civil & Environm Engn Tehran Iran Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW Australia

The sustainability of pavement, especially in Maintenance and Rehabilitation (M&R) scheduling, has become an immense concern and has received limited attention in previous studies. Therefore, this study aimed to develop the M&R scheduling optimization based on sustainability. To this end, a novel sustainability index was introduced, in which all the sustainable development aspects were considered, including highway agency cost, environmental impacts, and social effects. A conventional model was used to assess the sustainable model's effectiveness. Two new constraints are introduced to reduce the budget fluctuation and not to apply the M&R treatments for two consecutive years to make the model practical. On the other hand, highway agencies face large-scale networks, in which the optimization of M&R scheduling has computational complexities. Thus, the novel and powerful metaheuristic algorithm, named marine predator algorithm (MPA), was applied to solve the pavement M&R scheduling problem. A large-scale pavement network, including 110 sections, was analyzed over a 5-year plan as the case study. The results indicated that using the sustainable model rather than the conventional one leads to a 6.5% reduction in CO2 emission. Besides, utilizing the sustainable approach enhances the equity and safety indices by 40.7% and 2.5% compared to the conventional treatment. However, the highway agency cost is increased by 1.1% using the sustainable model.

关键词： Pavement management system pavement maintenance and rehabilitation planning marine predator algorithm sustainability environment

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Performance Analysis of marine-predator-algorithm-Based Optimum PI Controller with Unified Power Flow Controller for Loss Reduction in Wind-Solar Integrated System

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ENERGIES 2023年第17期16卷 6157页

作者： Valuva, Chandu Chinnamuthu, Subramani SRM Inst Sci & Technol Dept Elect & Elect Engn Kattankulathur 603203 India

Transmission line losses are a crucial and essential issue in stable power system operation. Numerous methodologies and techniques prevail for minimizing losses. Subsequently, Flexible Alternating Current Transmission Systems (FACTSs) efficiently reduce transmission losses, and the Unified Power Flow Controller (UPFC) is a reactive power compensation controller. The parameter strength of the proportional-integral (PI) controller was calibrated with the marine predator algorithm (MPA), a recent metaheuristic algorithm. An MPA-based optimum PI controller with a UPFC evaluates the optimal location of the UPFC and PI controller parameters to accomplish the desired research objective. The power rating of the UPFC was determined depending on the voltage collapse rating and power loss and an evaluated performance analysis of the MPA-PI-controlled UPFC on a modified IEEE-30 bus transmission network in MATLAB Simulink code. The Newton-Raphson method was used to perform the load flow analysis. Hence, the proposed MPA-PI controller was examined in contrast to preferred heuristic algorithms, the Artificial Bee Colony (ABC) and Moth Flame Optimization algorithms (MFO);the results showed that the MPA-PI controller exhibited better performance with an improved voltage profile and surpasses active power losses with the optimal placement of the UPFC device under different loading conditions. The active power loss, considering a UPFC with the proposed algorithm, reduced from 0.0622 p.u to 0.0301 p.u;consequently, the voltage profile was improved in the respective buses, and the loss percentage reduction during a 100% base load was 68.39%, which was comparatively better than the ABC and MFO algorithms.

关键词： loss minimization proportional-integral controller FACTS unified power flow controller marine predator algorithm

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Ensuring Zero Trust Security in Consumer Internet of Things Using Federated Learning-Based Attack Detection Model

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IEEE ACCESS 2025年 13卷 54423-54438页

作者： Al-Sharafi, Ali M. Alrayes, Fatma S. Alruwais, Nuha Maray, Mohammed Alshuhail, Asma Darem, Abdulbasit A. Alotaibi, Shoayee Dlaim Al-Hagery, Mohammed Abdullah Univ Bisha Coll Comp & Informat Technol Dept Comp Sci & Artificial Intelligence Bisha 67714 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Saud Univ Coll Appl Studies & Community Serv Dept Comp Sci & Engn Riyadh 11495 Saudi Arabia King Khalid Univ Coll Comp Sci Dept Informat Syst Abha 61421 Saudi Arabia King Faisal Univ Coll Comp Sci & Informat Technol Dept Informat Syst Al Hufuf 36362 Saudi Arabia Northern Border Univ Ctr Sci Res & Entrepreneurship Ar Ar 73213 Saudi Arabia Univ Hail Coll Comp Sci & Engn Dept Artificial Intelligence & Data Sci Hail 55476 Saudi Arabia Qassim Univ Coll Comp Dept Comp Sci Buraydah 52571 Saudi Arabia

The rising adoption of Consumer Internet of Things (CIoT) devices brings heightened concerns about security vulnerabilities and privacy breaches. Given their connection to daily routine and data collection abilities, it is vital to protect individual data against unauthorized entry and possible leaks proactively. Data protection's maximum communication and storage charge will gradually challenge the typical ecology of integrated over-the-cloud learning and handling for IoT environments. Federated learning (FL) is developed as the most reasonable method for this challenge. In FL, training data-driven machine learning (ML) approaches is a form of association among several users with no need for data carried to a fundamental point;therefore, improving communication and storage charge offers a great degree of consumer-level secrecy. FL is an advanced ML that performs appropriately by inherently prioritizing confidentiality, circumventing requirements for centralized data collection, and supporting the defence. FL improves security in IoT by enabling decentralized model training without centralizing sensitive data, preserving privacy, and reducing communication costs. Integrating FL into attack detection models ensures zero-trust security, minimizing vulnerabilities and enhancing protection against cyber threats while keeping data on-device. This study develops an enhanced artificial gorilla troop optimizer with an FL-based attack detection and classification (EGTO-FLADC) approach on the CIoT environment. The EGTO-FLADC approach aims to improve security in the CIoT environment via FL and the attack detection process. Primarily, the EGTO-FLADC technique utilizes the FL model for collaborative training of combined models with many devices with no data-sharing process. In addition, the EGTO-FLADC technique uses a min-max scalar for data normalization. For attack detection, the EGTO-FLADC technique employs the temporal convolution networks with a gated recurrent unit (TCN-GR

关键词： Internet of Things Training Security Biological system modeling Data privacy Data models Botnet Accuracy Computer science Servers Consumer Internet of Things federated learning attack detection marine predator algorithm DDoS attacks

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Stochastic configuration networks for short-term power load forecasting

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INFORMATION SCIENCES 2025年 689卷

作者： Wang, Yonggang Hao, Yue Zhao, Kaixing Yao, Yilin Shenyang Agr Univ Coll Informat & Elect Engn Shenyang 110866 Liaoning Peoples R China

The accuracy of power load forecasting can enhance the operational efficacy and economic efficiency of the power system. This paper utilizes the stochastic configuration network (SCN) and improved marine predator algorithm (IMPA) for power systems short-term load forecasting. First, the daily load data is clustered using the kernel fuzzy C-means (KFCM), and the clustering results are utilized as training input data for the load forecasting model. Then, load forecasting is performed using SCN. Next, IMPA is developed by integrating the opposite-based differential evolution (ODE) and normal mutation perturbation strategy for the purpose of preventing the algorithm from converging to local minima. Finally, IMPA is employed to optimize the critical parameters of the load forecasting model. The experimental results clearly demonstrate that the proposed algorithm performs favorably in terms of the prediction accuracy compared to other modeling algorithms.

关键词： Short-term load forecasting Stochastic configuration network marine predator algorithm

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A hybrid bio-inspired approach for clustering and routing in UWSNs using MPA and HGS

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2025年 46卷

作者： Li, Haitao Khishe, Mohammad Hernando-Gallego, Francisco Martin, Diego Shandong Huayu Univ Technol Innovat & Entrepreneurship Coll Dezhou 253000 Shandong Peoples R China Imam Khomeini Naval Sci Univ Nowshahr Dept Elect Engn Nowshahr Iran Jadara Univ Jadara Univ Res Ctr Irbid Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Univ Valladolid Escuela Ingn Informat Segovia Dept Comp Sci Segovia Spain

Underwater Wireless Sensor Networks (UWSNs) encounter serious challenges due to dynamic topology, energy constraints, and high latency underwater communication. Existing methods for clustering and routing often fail to strike an optimal balance between data delivery reliability, energy efficiency, and latency reduction. This paper overcomes these shortcomings by developing a hybrid model that integrates the Hunger Games Search (HGS) and marine predator algorithm (MPA) for improved clustering and routing in UWSNs. The MPA was chosen due to its stability in selecting the first sensors/cluster heads and creating the clusters, drawing inspiration from the foraging strategies of marine predators, which guides it extensively in the balance of exploration and exploitation. Simulations demonstrate that the proposed method achieves significantly better results than classical methods. In particular, the HGS-MPA framework consumes 26.6 % less energy than GWO-PSO, increasing network lifetime by 22.1 % (FINOD) and 15.8 % (HANOD). The packet delivery ratio is improved by 3.1 % against the following best-performing method, reaching 92.4 %. A statistical test performed with ANOVA showed that these improvements are statistically significant at P < 0.001. The results reinforce how the HGS-MPA framework would help improve energy efficiency, network lifetime, and communication reliability in UWSN systems.

关键词： Underwater wireless sensor networks marine predator algorithm Hunger game search Clustering Routing

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A Single-End Location Method for Small Current Grounding System Based on the Minimum Comprehensive Entropy Kurtosis Ratio and Morphological Gradient

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APPLIED SCIENCES-BASEL 2025年第7期15卷 3539-3539页

作者： Cao, Jiyuan Wang, Yanwen Wu, Lingjie Zhao, Yongmei Wang, Le China Univ Min & Technol Beijing Sch Mech & Elect Engn Beijing 100083 Peoples R China Petrochina Planning & Engn Inst Beijing 100083 Peoples R China CHN Energy Tech & Econ Res Inst Beijing 100083 Peoples R China China Datang Corp Sci & Technol Gen Res Inst Co Lt Datang North China Elect Power Test & Res Inst Beijing 100043 Peoples R China

Fault location technology is crucial for enhancing the efficiency of fault maintenance and ensuring the safety of the power supply in small current grounding systems. To address the challenge that traditional single-end positioning methods experience when identifying the reflected wave head and that the adaptability of wave head calibration methods is typically limited, a single-end location method of modulus wave velocity differences based on marine predator algorithm optimized multivariate variational mode decomposition (MVMD) and morphological gradient is proposed. Firstly, the minimum comprehensive entropy kurtosis ratio is used as the fitness function, and the marine predator algorithm is used to realize the automatic optimization of the mode number and penalty factor of the multivariate variational mode decomposition. Therefore, with the goal of decomposing the traveling wave characteristic signals with the most significant traveling wave characteristic information and the lowest noise component, the line-mode traveling wave and the zero-mode traveling wave are accurately decomposed. Secondly, the intrinsic mode function component with the smallest entropy kurtosis ratio is selected as the line-mode traveling wave characteristic signal and the zero-mode traveling wave characteristic signal, respectively, and the arrival time of the wave head is accurately calibrated by combining the morphological gradient value. Finally, the fault distance is calculated by the modulus wave velocity difference location formula and compared with the variational mode decomposition-Teager energy operator (VMD-TEO) method and the empirical mode decomposition _first-order difference method. The results show that the proposed method has the highest accuracy of positioning results, and the algorithm time is significantly reduced compared with the VMD-TEO method, and it has strong adaptability to different line types of faults, different fault initial conditions, and noise interference

关键词： fault location multivariate variational mode decomposition small current grounding systems marine predator algorithm modulus wave velocity difference

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NARX modeling and simulation of heave dynamics with application of robust control of an underactuated underwater vehicle

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OCEAN ENGINEERING 2025年 325卷

作者： Ahmad, Sarvat Mushtaq Tanveer, Ahsan King Fahd Univ Petr & Minerals Control & Instrumentat Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Intelligent Mfg & Robot Dhahran 31261 Saudi Arabia

This article, which is an extension of the previous work of the authors (Tanveer and Ahmad (2022)) on yaw dynamics, investigates the modeling and control of heave degree-of-freedom of a compact custom designed ROV. Wherein, nonlinear data-driven modeling strategy is adopted to develop a high-fidelity heave dynamic model. The proposed modeling approach uses open-loop real-time experimental data to derive a high-fidelity NARX model of the vehicle. The resulting model accommodates the dynamics of the system in addition to the tether dynamics. The advantage of this approach is its ability to eliminate the need for intricate controller tuning. The identified model consistently demonstrated fitness scores ranging from 82% to 92% in both self-validation and cross-validation tests conducted on distinct datasets. This relative advantage is exemplified in real-time through the testing of a Genetic algorithm Proportional-Integral (GAPI) controller. The performance of GAPI is subsequently compared with the relatively recent marine predators algorithm (MPA) and the more conventional rootlocus tuned PI controllers. The experimental results demonstrate that GAPI provides the most favorable response, achieving a 35%, 76%, and 44% improvement in rise time, percent overshoot and peak time, respectively. Furthermore, the controller effort required by GAPI running on an ATmega328 chipset is 22% less than its counterparts.

关键词： NARX Nonlinear system identification marine predator algorithm ROV

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A hybrid ultra-short-term wind speed prediction model using adaptive VMD and Time-Series Mixer

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2025年

作者： Wang, Xiaoxia Liu, Pu Liu, Qingyi Zhang, Yongzhuo North China Elect Power Univ Dept Comp Sci 619 Yonghua North St Baoding 071003 Peoples R China North China Elect Power Univ Hebei Key Lab Knowledge Comp Energy & Power Baoding Peoples R China

Accurate prediction of wind speed is essential for ensuring the stable operation of power grids, especially with the increasing proportion of wind power generation in total power generation mix. To address the challenge posed by the significant volatility and randomness of wind speed, this paper proposes a novel hybrid model for ultra-short-term wind speed prediction. The model integrates variational mode decomposition (VMD), marine predator algorithm (MPA), and time-series mixer (TSMixer) to enhance prediction accuracy and address the complexities inherent in wind speed prediction. MPA algorithm optimizes the VMD decomposition process to enhance the extraction of intrinsic components from wind speed data, thereby reducing noise and simplifying the data complexity. Additionally, correlation fuzzy entropy is utilized to quantify the level of fuzziness within subsequences, aiding in the identification of intricate structures and patterns in the wind speed time series. TSMixer network is then employed to capture temporal dependencies and features from the subsequences with low complexity and high efficiency, ensuring precise predictions. Finally, the results from multiple subsequences are integrated to obtain the final prediction. Experimental results demonstrate that the proposed model achieves promising accuracy and robustness across diverse wind speed patterns observed from different locations, demonstrating its effectiveness in ultra-short-term wind speed prediction.

关键词： Hybrid model marine predator algorithm Time-Series Mixer variational mode Decomposition wind speed prediction

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Using Novel Optimization algorithms with Support Vector Regression to Estimate Pile Settlement Rates

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INDIAN GEOTECHNICAL JOURNAL 2025年第1期55卷 79-91页

作者： Sun, Lu Li, Tinghui Huzhou Vocat & Tech Coll Sch Architecture & Engn Huzhou 313000 Zhejiang Peoples R China Huzhou Key Lab Green Bldg Technol Huzhou 313000 Zhejiang Peoples R China China Earthquake Adm Inst Engn Mech Key Lab Earthquake Engn & Engn Vibrat Harbin 150080 Heilongjiang Peoples R China

To ensure structure safety, such as bridge structures, artificial intelligence ways are the most helpful way to predict failure factors such as pile settlement estimation. Various methods are taken into account for evaluating the movement of the piles, which helps to realize the future view of the project during the loading period. The most intelligent mathematical strategy to calculate the movement of the pile is applied. In this regard, support vector regression (SVR), a machine learning method, was used in this study, accompanying two optimizers to accurately determine the key SVR variables. The marine predator algorithm (MPA) and the grasshopper optimization algorithm (GOA) were combined with SVR to create the SVR-MPA and SVR-GOA frameworks. Additionally, several metrics were used to evaluate the model's overall performance. The R2 for SVR-MPA in the training phase was found to be 0.997, showing a desirable modelling operation. At the same time, the RMSE was calculated 0.2843 mm and compared to the SVR-GOA, the differences are 1.16 and 106.38%, respectively, in favour of the former model. The comprehensive index of OBJ, including RMSE, MAE, and R2, was calculated at 0.2791 and 0.581 mm for models optimized by MPA and GOA, alternatively.

关键词： Pile Structure settlement Grasshopper optimization algorithm Machine learning Support vector regression marine predator algorithm

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Enhanced recurrent capsule network with hyrbid optimization model for shrimp disease detection

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SCIENTIFIC REPORTS 2025年第1期15卷 1-24页

作者： Raj, A. Sundar Senthilkumar, S. Radha, R. Muthaiyan, R. EGS Pillay Engn Coll Dept Biomed Engn Nagapattinam 611002 Tamil Nadu India EGS Pillay Engn Coll Dept Elect & Commun Engn Nagapattinam 611002 Tamil Nadu India SRM Inst Sci & Technol Dept Data Sci & Business Syst Kattankulathur 603203 Tamil Nadu India Univ Coll Engn Dept Elect & Commun Engn Thirukkuvalai 610204 Tamil Nadu India

Disease detection plays an important role in shrimp aquaculture to ensure the health and sustainability of farming operations. Specifically, detecting viral infections at early stages can prevent significant losses. Image processing applications have been developed to detect different types of diseases in shrimp. However, theaccuracy of detection models needs improvement to detect various diseases through a single model. Therefore, this research presents a novel disease detection model using an Enhanced Recurrent Capsule Network (ERCN) with a hybrid optimization model for enhanced detection performance. The proposed ERCN utilizes dynamic routing of capsules to extract spatial hierarchies and patterns in shrimp images, while the recurrent layer extracts temporal dependencies. Performance is further improved by incorporating spatial and channel attention models to select optimal regions and features in the images for the fusion process. The dual-level feature fusion procedure combines local and global features, providing a final fused data to classify different types of diseases. Additionally, the proposed work incorporates a hybrid optimization that combines Harris Hawks Optimization (HHO) with the marine predator algorithm (MPA) to fine-tune the classifier model parameters. Experiments evaluate the performance of the proposed disease detection model through various metrics such as accuracy, precision, recall, specificity, Matthews correlation coefficient, and F1-score. The resutls confirms that the performance of the proposed model is superior with precision of 94.9%, recall of 93.5%, F1-score of 94.6% and detection accuracy of 95.2% over conventional Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Long Short Term Memory (LSTM) Networks.

关键词： Shrimp disease detection Deep learning Capsule network Recurrent network Harris Hawks optimization marine predator algorithm

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