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artificial rabbit optimization-based ANFIS model development for predicting the compressive strength of GGBFS-based concrete

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STRUCTURAL CONCRETE 2024年第1期25卷 334-348页

作者： Sun, Qingmei Zhejiang Guangsha Vocat & Tech Univ Construct Dongyang Zhejiang Peoples R China Zhejiang Guangsha Vocat & Tech Univ Construct Dongyang 322100 Zhejiang Peoples R China

The carbon dioxide emissions associated with the production of conventional Portland cement may be mitigated through the utilization of ground granulated blast furnace slag (GGBFS). The consideration of compressive strength (f (c)) is essential in the design and construction of concrete structures, as it is an essential demand in concrete mixtures. The primary objective of this study is to establish an effective approach for conducting a comprehensive evaluation of machine learning algorithms in predicting the fc of concrete that incorporates GGBFS. The work focuses on using the adaptive neuro-fuzzy inference system (ANFIS) to create predictive models for f(c). The f(c) of the collected data sets ranged from 6.3 to 101.3 MPa. The study included the artificial rabbit optimization (ARO) and Bald Eagle search algorithm (BES) to improve the effectiveness of the ANFIS approaches. The novelty of this study is attributed to several factors, including the utilization of the ARO and BES methodologies, the incorporation of GGBFS in the evaluation of f (c), the comparison with previous research findings, and the utilization of a substantial dataset encompassing multiple input variables. These factors offer a novel method for improving the effectiveness of prediction models and advance our understanding of forecasting the mechanical characteristics of concrete. The integrated ANF-AR and ANF-BA systems showed good estimating skills, according to the findings, which showed R-2 values of 0.9961 and 0.9967 for the ANF-AR's training and testing components and 0.9916 and 0.9946 for the ANF-BA, respectively. Overall, the ANFIS optimized with ARO model is recognized as the outperformed system for prediction purposes.

关键词： adaptive neuro-fuzzy inference system artificial rabbit optimization compressive strength of concrete ground granulated blast furnace slag

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Study of phasor measurement unit placement in wide area monitoring system of radial distribution network using oppositional-based artificial rabbit optimization

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ELECTRICAL ENGINEERING 2025年 1-33页

作者： Sultana, Sneha Paul, Sourav Roy, Provas Kumar Dr B C Roy Engn Coll Dept Elect Engn Durgapur West Bengal India Kalyani Govt Dept Elect Engn Kalyani West Bengal India

In this article, a new metaheuristic bioinspired technique oppositional artificial rabbit optimization (OARO) technique is enhanced and employed, for phasor measurement unit (PMU) placement in distribution system. Based on PMUs, this study emphasizes their advantages for real-time monitoring. Quite differently, the PMU serves as the system's cornerstone. The goal of this kind of technology is to analyse data quickly and automatically arrive at a decision. Four scenarios are taken into consideration in the simulation study for each test system, and the goal is to minimize costs while taking into account fewer PMU. The first case focuses primarily on the number of PMU set as the objective function;the second case uses the wide area monitoring system (WAMS) data traffic index and installation cost to observe comprehensive observability. The third case focuses on how many PMU are used in conjunction with zero injection bus (ZIB). Finally, ZIB and WAMS are implemented together to reduce data traffic and, eventually, the fitness function. There is artificial rabbit optimization (ARO), inspired by the survival of rabbits, such as detour foraging and random hiding. The behaviour of the rabbits in random searching for food in other region neglecting its own region followed by random hiding amongst all borrowings to reduce the chances for the predators to search them to kill. In this study, an oppositional strategy of rabbits' finding the tunnels is applied for finding the food search space. In addition, the rabbit energy shrink strategy is implemented to transmit rabbits from detour foraging and random hiding. The concept of oppositional-based learning applied to rabbit survival strategy has been mathematically modelled and tested in PMU placement problems in the radial distribution system. The methods are evaluated in radial distribution systems with 33, 69, 85, 118, and 141 buses. In each case, it is found that OARO provides better results on PMU placement for phasor measu

关键词： Radial distribution system Phasor measurement unit artificial rabbit optimization Oppositional artificial rabbit optimization Wide area monitoring system Zero injection bus

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An improved artificial rabbit optimization for structural damage identification

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LATIN AMERICAN JOURNAL OF SOLIDS AND STRUCTURES 2024年第1期21卷

作者： Huu, Quyet Nguyen Ngoc, Lan Nguyen Tien, Thanh Bui Ngoc, Hoa Tran Tran, Hieu Nguyen Xuan, Tung Nguyen Univ Transportat & Commun Ltd Co UCT DX Lab Hanoi Vietnam Univ Transport & Commun Fac Civil Engn Dept Bridge Engn & Underground Infrastruct Hanoi Vietnam Univ Transport & Commun Fac Informat Technol Hanoi Vietnam Univ Transport & Commun Struct Engn Dept Hanoi Vietnam

This paper presents an enhanced version of the artificial rabbit optimization (ARO) algorithm designed for identifying structural damage in bridge structures. The original ARO draws inspiration from survival observed in wild rabbits. However, it demands a substantial investment of computational time. Therefore, in this paper, the Improved ARO (IARO) algorithm incorporating elements of the Grey Wolf Optimizer (GWO) through hybridization, is employed to deal with optimization problems. The central concept of this approach involves infusing predator -hunting characteristics into the prey -rabbit during the hunting process, thereby enabling more effective predator evasion. The proposed method is evaluated through a series of simulations related to two real bridges: a simple supported beam structure and a steel truss bridge. The results show a significant improvement in accuracy and efficiency in determining structural damage while considering factors such as damage location, severity, and computation time. These findings underscore the potential of the proposed approach for real -world applications in structural health monitoring and damage detection.

关键词： artificial rabbit optimization bridge damage identification Structural health monitoring predator-hunting trait

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Hybrid artificial rabbit optimization and Perturb & Observe MPPT for Grid-Connected PV System

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2024年第11期52卷 2008-2029页

作者： Kumar, G. Gabriel Santhosh Titus, S. K Ramakrishnan Coll Engn Dept Elect & Elect Engn Trichy Tamil Nadu India K Ramakrishnan Coll Engn Dept Elect & Elect Engn Trichy 621112 Tamil Nadu India

The Perturb & Observe for MPPT algorithm is frequently utilized in solar systems. A faces significant challenges in accurately tracking peak power under changing temperature and irradiance objectives. An essay suggests a fresh idea for hybrid MPPT based on an algorithm that combines the advantages of artificial rabbit optimization (ARO) as well as P & O algorithms. A proposed algorithm overcomes the limitations of traditional P & O algorithms by capturing multiple peak power points, enabling it to adapt to rapidly changing conditions. Comparative analysis with the standard P & O technique demonstrates the suggested algorithm's advantage in terms of accuracy, efficiency, as well as reduced near-steady-state oscillations. In suggested model achieves an MPPT efficiency of approximately 96% and an overall system efficiency of around 99%. Furthermore, it exhibits a settling time of 0.1 s and a rise time of approximately 0.05 s. These impressive performance metrics highlight the potential of the hybrid ARO and considerably improve the P & O MPPT-based algorithm performance for PV systems by varying the conditions of environments.

关键词： MPPT PV system artificial rabbit optimization hybrid MPPT grid connected PV

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A hybrid model integrating GRU with artificial rabbit optimization for stock price prediction

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INTERNATIONAL JOURNAL OF INTELLIGENT COMPUTING AND CYBERNETICS 2025年第2期18卷 418-443页

作者： Zeng, Xiaohua Chen, Wanling Guangdong Univ Foreign Studies Sch Econ & Trade Guangzhou Peoples R China Guangzhou Xinhua Univ Sch Econ & Trade Guangzhou Peoples R China

PurposeThis study aims to enhance stock price prediction accuracy by integrating a gated recurrent unit (GRU) with an artificial rabbit optimization (ARO) algorithm. The objective is to address the issues in hyperparameter optimization and deliver a high-performance predictive model for stock market trends tested on the Dow Jones Industrial Average (DJIA) ***/methodology/approachThe proposed ARO-GRU hybrid model uses a GRU for time-series stock price prediction and an ARO to dynamically optimize the model's parameters. ARO-GRU was benchmarked against various models, including single-layer and multi-layer GRU, BiLSTM and long short-term memory (LSTM) models optimized by genetic algorithms (GA) or ARO. Performance was assessed using metrics such as the mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and R-squared (R2).FindingsThe experimental results showed that the ARO-GRU model significantly outperformed its counterparts. Compared to the best alternative model (LSTM-ARO), ARO-GRU reduced the MSE by 81.8% (from 22.731 to 1.864 for the AAPL stock) and the MAPE by 64% (from 0.025 to 0.009). It achieved an average R2 score improvement of 5.3% across all tested stocks, demonstrating a better model fit. In addition, the ARO-GRU model required 83% less computational time than the LSTM-ARO model, further validating its ***/valueThis study introduces the integration of the ARO algorithm with the GRU for stock market prediction, marking a novel combination of efficiency and optimization. By demonstrating significant improvements in prediction accuracy and computation time, this study provides a robust and scalable solution for dynamic stock-trading systems.

关键词： Gated recurrent unit artificial intelligence artificial rabbit optimization Metaheuristic algorithm Deep learning Stock market prediction

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Hybrid algorithm for directly detecting and classification of multiple power quality disturbances

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 242卷

作者： Altintasi, Cagri Erzincan Binali Yildirim Univ Dept Elect & Elect Engn TR-24002 Erzincan Turkiye

Detecting potential power quality (PQ) events that may occur in the network is an important issue to provide safe electricity to customers. In this study, anew hybrid algorithm named adaptive fitness distance balance hybrid artificial hummingbird algorithm-artificial rabbit optimization (AFDB-AHAARO) algorithm is introduced for the detection and classification of PQ events. A hybrid algorithm is developed to improve the early convergence of the AHA algorithm by expanding the search to avoid local minima. The proposed method, unlike the optimization algorithms used in the literature, is directly applied to the detection and classification of PQ events. The performance of the proposed algorithm is investigated by 6000 different single and multiple PQ events in the Matlab in noisy and noiseless environments, considering the IEEE-1159 standards. The obtained results are compared with the AHA algorithm and the methods proposed in the literature, and it is shown that the proposed method outperforms in noisy environments. It also provides information about the time interval of PQ events. The validity of the proposed algorithm in real systems is demonstrated by testing it on the voltage signal with flicker disturbance obtained from the electrical network and on experimentally generated sag and swell events.

关键词： Power quality disturbance artificial hummingbird algorithm artificial rabbit optimization Hybrid artificial optimization algorithm Adaptive fitness distance balance

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An Improved artificial rabbits optimization Algorithm with Chaotic Local Search and Opposition-Based Learning for Engineering Problems and Its Applications in Breast Cancer Problem

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Computer Modeling in Engineering & Sciences 2024年第11期141卷 1067-1110页

作者： Feyza AltunbeyÖzbay ErdalÖzbay Farhad Soleimanian Gharehchopogh Department of Software Engineering Firat UniversityElazig23119Turkey Department of Computer Engineering Firat UniversityElazig23119Turkey Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia44867-57159Iran

artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in ***,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local *** overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)*** adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space *** maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive *** proposed COARO algorithm has been tested using thirty-three distinct benchmark *** outcomes have been compared with the most recent optimization ***,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other *** study also introduces a binary variant of the continuous COARO algorithm,named *** performance of BCOARO was evaluated on the breast cancer *** effectiveness of BCOARO has been compared with different feature selection *** proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness *** experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.

关键词： artificial rabbit optimization binary optimization breast cancer chaotic local search engineering design problem opposition-based learning

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Evaluation of renewable and energy storage system-based interlinked power system with artificial rabbit optimized PI(FOPD) cascaded controller

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AIN SHAMS ENGINEERING JOURNAL 2024年第2期15卷

作者： Saha, Arindita Dash, Puja Bhaskar, M. S. Almakhles, Dhafer Elmorshedy, Mahmoud F. Regent Educ & Res Fdn Grp Inst Dept Elect Engg Kolkata 700121 India Power Res & Dev Consultants Pvt Ltd Dept Operat & Res Bengaluru India Prince Sultan Univ Coll Engn Renewable Energy Lab Riyadh 11586 Saudi Arabia Tanta Univ Fac Engn Elect Power & Machines Engn Dept Tanta 31733 Egypt

This research work addresses automatic generation control of multiple arenas and causes under various system disturbances. Sources within arena-1 comprises thermal, bio-diesel, in arena-2 sources represent thermal ele-ments, in arena-3 sources are thermal and hydro units. An innovative endeavor has been undertaken to employ conglomerate controller with the amalgamation of proportional-integral (PI) (integer-order) in addition frac-tional order proportional-derivative (FOPD). Various examination manifests the fineness of PI(FOPD) overtop additional integer order controllers from perspective concerning reduced grade of crown overshoot, expanse-of-instabilities, crown undershoot in addition to subsiding period. In determination to attain the controller's fea-tures biologically enthused meta-heuristic artificial rabbit optimization is pragmatic. The minimum cost function value has been achieved as 0.000309 for the artificial rabbit optimization in comparison to various established optimization technique along with the proposed PI(FOPD) controller. It is likewise detected that occurrence of inexhaustible sources like realistic dish-Stirling thermal system in arena-1, geothermal power plant and precise wind turbine system styles the structure meaningfully improved related to origin structure when judged exclusively or all together. Accomplishment in presence of interline power flow controller besides combination of energy stowage elements alike redox flow battery in addition solid oxide fuel cell is as well inspected using PI (FOPD) controller, that offers with notable consequence in dynamic presentation in each cases separately. It has been clearly observed that in presence of all the renewables, in case of frequency deviation crown overshoot, crown undershoot, and subsiding period are 0.0053, 0.0131, and 24.32 respectively. These values are showing improvements in comparison to the individual presence of renewables in each arena. Similarly, with the presence of both SOF

关键词： Automatic Generation Control Geothermal Power Plants artificial rabbit optimization Precise Wind Turbine System Redox Flow Battery Solid Oxide Fuel Cell

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Design Hybrid ARO-SVR Analysis To Predict The Pile Bearing Capacity

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2025年第7期28卷 1397-1410页

作者： Liu, Yafan Ma, Wenjun Shijiazhuang Univ Appl Technol Dept Architectural Engn Shijiazhuang 050000 Hebei Peoples R China Shijiazhuang Tiedao Univ Shijiazhuang 050000 Hebei Peoples R China Hebei Prov Acad Emergency Management Scieces Shijiazhuang 050000 Hebei Peoples R China

The assessment of the load-bearing capacity of piles holds significant importance in the design of pile foundations. This paper presents hybridized support vector regression (SVR) models by utilizing the artificial rabbit optimization (ARO) and the Black Widow optimization Algorithm (BWOA) to predict the bearing capacity of concrete piles. A repository comprising 472 reports on static load tests conducted on driven piles was employed for the study. The dataset was allocated into three parts: the training set (70%), the validation set ( 15% ), and the testing set ( 15%). Multiple criteria for assessing quality were utilized to evaluate the effectiveness of the models. The first rank belonged to the SVR model integrated with the ARO algorithm, where it could gain the higher value of R2 in all of training (R2 = 0.9876), validating (R2 = 0.9778), and testing sections (R2 = 0.9874), and the lowest value of RMSE in all the training ( RMSE = 39.393), validating ( RMSE = 53.727) and testing sections ( RMSE = 38.082). The findings indicate that the suggested model is highly appropriate for predicting the capacity of concrete piles.

关键词： Concrete piles Bearing capacity Estimation Regression analysis artificial rabbit optimization

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Multi-Strategy Improved artificial rabbit Algorithm for QoS-Aware Service Composition in Cloud Manufacturing

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ALGORITHMS 2025年第2期18卷 107-107页

作者： Deng, Le Shu, Ting Xia, Jinsong Zhejiang Sci Tech Univ Sch Comp Sci & Technol Hangzhou 310018 Peoples R China

Cloud manufacturing represents a pioneering service paradigm that provides flexible, personalized manufacturing services to customers via the Internet. Service composition plays a crucial role in cloud manufacturing, which focuses on integrating dispersed manufacturing services in the cloud platform into a complete composite service to form an efficient and collaborative manufacturing solution that fulfills the customer's requirements, having the highest service quality. This research presents the multi-strategy improved artificial rabbit optimization (MIARO) technique, designed to overcome the limitations with the original method, which often risks converging to local optima and have poor solution quality when dealing with optimization problems. MIARO helps the algorithm escape local optimality with L & eacute;vy flights, extends local search with the golden sine mechanism, and expands variability with Archimedean spiral mutations. MIARO is experimented on 23 benchmark functions, 3 engineering design problems, and QoS-aware cloud service composition (QoS-CSC) issues at various sizes, and the experimental findings indicate that MIARO delivers outstanding performance and offers a viable solution to the QoS-CSC problem.

关键词： artificial rabbit optimization meta-heuristic algorithm cloud manufacturing engineering design combination optimization

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