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Image Registration Using the Arithmetic optimization Algorithm for Robotic Visual Servoing

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2025年第1期18卷 1-12页

作者： Kmich, Mohamed Harrade, Inssaf Karmouni, Hicham Sayyouri, Mhamed Askar, S. S. Abouhawwash, Mohamed Sidi Mohamed Ben Abdellah Univ Natl Sch Appl Sci Engn Syst & Applicat Lab Fes Morocco Cadi Ayyad Univ Natl Sch Appl Sci Marrakech Morocco King Saud Univ Coll Sci Dept Stat & Operat Res POB 2455 Riyadh 11451 Saudi Arabia Michigan State Univ Dept Computat Math Sci & Engn CMSE E Lansing MI 48824 USA Mansoura Univ Fac Sci Dept Math Mansoura 35516 Egypt

Visual servoing using image registration is a method employed in robotics to control the movement of a system using visual information. In this context, we propose a new intensity-based image registration algorithm (IBIR) that uses information derived from images acquired at different times or from different views to determine the parameters of the geometric transformations needed to align these images. The Arithmetic optimization Algorithm (AOA) is used to optimize these parameters, minimizing the difference between the images to be aligned. The proposed algorithm, Intensity-Based Image Registration via Arithmetic Optimisation Algorithm (IBIRAOA), is robust to image data fluctuations and perturbations and can avoid local optima. Simulation results prove the importance and efficiency of the proposed algorithm in terms of computation time and similarity of aligned images compared to other methods based on various metaheuristics. In addition, our results confirm a significant improvement in the trajectory of the wheeled mobile robot, thus reinforcing the overall effectiveness of our method in practical navigation and robotic control applications.

关键词： Arithmetic optimization Algorithm Image Registration metaheuristic optimization algorithms Robotic Visual Servoing

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Thinking Innovation Strategy (TIS): A novel mechanism for metaheuristic algorithm design and evolutionary update

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APPLIED SOFT COMPUTING 2025年 175卷

作者： Jia, Heming Zhou, Xuelian Zhang, Jinrui Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China

The metaheuristic optimization algorithm(MHS) is a global optimization method inspired by natural phenomena, demonstrating superior performance in specific application scenarios. Traditional optimization algorithms utilize two main concepts: exploration, to expand the search range, and exploitation, to enhance solution accuracy. However, as problem complexity and application scenarios increase, MHS struggles to balance exploration and exploitation to find the optimal solution. Therefore, this paper introduces innovative characteristics of individual thinking and proposes a new Thinking Innovation Strategy (TIS). TIS does not aim for an optimal solution but seeks global optimization based on successful individuals, enhancing algorithm performance through survival of the fittest. This paper applies TIS strategies to improve various MHS algorithms and evaluates their performance on 57 engineering problems and the IEEE CEC2020 benchmarks. Experimental results indicate that the TISenhanced algorithms outperform the original versions across 57 engineering problems, according to Friedman ranking and Wilcoxon rank-sum test results. Some algorithms show significant improvement, demonstrating the feasibility and practicality of TIS for optimization problems. The TIS (LSHADE_SPACMA) of the source code can be accessed through the following ways: https://***/LIANLIAN-Serendipity/TIS

关键词： Thinking transformation strategy metaheuristic optimization algorithms Intelligent balanced exploration-exploitation Constrained engineering design problems

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Optimizing hybrid energy systems for remote Australian communities: The role of tilt angle in cost-effective green hydrogen production

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APPLIED ENERGY 2025年 391卷

作者： Roy, Tushar Kanti Saha, Sajeeb Than, Amanullah Maung Macquarie Univ Sch Engn Sydney NSW 2109 Australia Univ Sunshine Coast Sch Sci & Engn Moreton Bay Qld 4502 Australia

This study investigates hybrid energy systems (HESs) integrating photovoltaic (PV) panels, batteries, fuel cells (FCs), electrolyzers (ELs), and hydrogen tanks (HTs) to address the energy needs of remote Australian communities. Two configurations are analyzed: Type-A (PV/Batt/FC/EL/HT) and Type-B (PV/FC/EL/HT), focusing on cost-efficiency, energy reliability, and hydrogen production. Several optimization techniques, including the cuckoo search algorithm, non-dominated sorting genetic algorithm-II (NSGA-II), and sequential quadratic programming algorithm (SQPA), flower pollination algorithm, constrained PSO , and harmony search algorithm, are employed to determine optimal system configurations. Type-A emerges as the most cost-effective configuration when optimized with NSGA-II, achieving a net present cost (NPC) of $226,500, a levelized cost of electricity (LCOE) of $0.193/kWh, and a levelized cost of hydrogen (LCOH) of $4.88/kg. Battery integration in Type-A enhances both cost-efficiency and energy reliability. For hydrogen-focused applications, SQPA yields the highest hydrogen production at 4737 kg/year, supported by higher EL (14 kW) and FC (18.63 kW) capacities. System efficiency is found to be highly sensitive to PV tilt angle, with 30 degrees identified as optimal. Increasing the tilt to 70 degrees can raise system costs by up to 75 %. Sensitivity analyses reveal that improving component efficiencies dramatically impacts costs. For example, increasing fuel cell efficiency from 40 % to 60 % reduces NPC, LCOE, and LCOH by $40,000, $0.04/kWh, and $0.1/kg, respectively, especially in Type-A systems. Collectively, adjustments to PV tilt angles and component efficiencies can reduce overall costs by up to 40 %. These insights offer a strategic foundation for designing HESs that balance electricity and hydrogen generation, tailored for sustainable operation in off-grid and remote settings.

关键词： Energy storage optimization Electrolyzers and hydrogen storage system Hybrid renewable energy integration Hybrid energy system Levelized cost metrics (LCOE LCOH) metaheuristic optimization algorithms Remote energy solutions

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Predicting Elastic Modulus of Rocks Using metaheuristic-Optimized Ensemble Regression Models

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ROCK MECHANICS AND ROCK ENGINEERING 2025年 1-17页

作者： Shahani, Niaz Muhammad Zheng, Xigui Wei, Xin Wei, Yue China Univ Min & Technol Sch Mines Xuzhou 221116 Peoples R China

The elastic modulus (E) of rocks is an essential parameter in mining and rock engineering projects, as it directly affects their stability and structural integrity. This study investigates the application of metaheuristic optimization algorithms, specifically Cuckoo Search (CS) and Harris Hawks optimization (HHO), to fine-tune the hyperparameters of ensemble regression models, including extreme gradient boosting (XGBoost), decision tree (DT), and adaptive boosting (AdaBoost). A dataset of 122 rock samples, including input parameters such as wet density, moisture, dry density, Brazilian tensile strength, and uniaxial compressive strength, was used to predict E. A dataset was split into training and testing datasets with a 70:30 ratio. Model performance was evaluated using metrics like coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) in %, and severity index (SI). The results show that CS-HHO-optimized models significantly outperformed the unoptimized models, with the optimized stacking model providing superior prediction accuracy for predicting E. Both the unoptimized and optimized Stacking Models exhibited superior performance on the test data. The unoptimized Stacking Model achieved an R2 of 0.980, RMSE of 0.0483, MAE of 0.0223, MAPE of 12.412%, and SI of 0.1737, while the CS-HHO-optimized Stacking Model yielded a similar R2 of 0.980, with slight variations in RMSE (0.0497), MAE (0.0234), MAPE (13.5736%), and SI (0.1786). This study provides a robust predictive framework for rock behavior analysis, contributing to the field of mining and rock engineering project design.

关键词： Elastic modulus Ensemble regression models Stacking method metaheuristic optimization algorithms Mining and rock engineering project design

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Advanced Parameter Identification in Electric Vehicles Lithium-Ion Batteries With Marine Predators Algorithm-Based optimization

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2025年第1期2025卷

作者： Ghadbane, Houssam Eddine Rezk, Hegazy Alhumade, Hesham Univ 8 Mai 1945 Dept Electrotech & Automatique Lab Genie Elect Guelma LGEG Guelma 24000 Algeria Prince Sattam bin Abdulaziz Univ Coll Engn Wadi Alddawasir Dept Elect Engn Al Kharj Saudi Arabia Minia Univ Fac Engn Elect Engn Dept Al Minya 61517 Egypt King Abdulaziz Univ Fac Engn Chem & Mat Engn Dept Jeddah Saudi Arabia King Abdulaziz Univ Ctr Excellence Desalinat Technol Jeddah 21589 Saudi Arabia

Accurate parameter identification of lithium-ion (Li-ion) battery models is critical for understanding battery behavior and optimizing performance in electric vehicle (EV) applications. Traditional methods often rely on manual adjustments or trial-and-error processes, leading to inefficiencies and suboptimal outcomes. This study introduces a novel parameter identification approach using the marine predators algorithm (MPA), applied to a Shepherd model for EV applications. The proposed technique was validated under various dynamic test conditions, including the urban dynamic driving cycle (UDDC), the new European driving cycle (NEDC), and the worldwide harmonized light vehicles test procedure (WLTP). The MPA-based method systematically identifies optimal parameters, achieving a voltage error of 2.743 x 10-3, a state of charge (SOC) error of 0.7693 x 10-3, and a root mean square error (RMSE) of 8.37 x 10-3 between the model and real data. Compared to other optimization techniques, the MPA demonstrated superior performance, achieving an optimization efficiency of 97.69%. These results validate the robustness and reliability of the method for accurately capturing battery dynamics under realistic driving conditions. These results highlight the potential of the MPA-based approach in improving the accuracy of Li-ion battery parameter identification, leading to more efficient energy management in EVs and contributing to enhanced battery performance and reliability.

关键词： Li-ion battery marine predators algorithm metaheuristic optimization algorithms parameter identification

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Optimal Power Flow: A Review of State-of-the-Art Techniques and Future Perspectives

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IEEE ACCESS 2025年 13卷 60012-60039页

作者： Babiker, Ahmed Ahmad, Sulaiman S. Ahmed, Ijaz Khalid, Muhammad Abido, Mohammad A. Saleh Al-Ismail, Fahad King Fahd Univ Petr & Minerals KFUPM Elect Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals KFUPM Interdisciplinary Res Ctr Sustainable Energy Syst Dhahran 31261 Saudi Arabia

The Optimal Power Flow (OPF) problem has become increasingly pivotal in the planning and operation of modern power systems. With the expansion of the grid scale, the advent of smart grid technologies, and the unpredictable nature of renewable energy sources (RESs), interest in OPF has surged. These challenges with new energy storage have introduced a heightened level of uncertainty into the power system's operation as well as planning. Because of this, OPF is seen as an important tool for achieving different goals, such as optimizing the distribution of resources, making electrical networks more efficient, and so on. However, the OPF problem is inherently difficult to solve because of its non-linear characteristics. Different constraints and limitations intrinsic to real power system grids further accentuate this complexity. Moreover, modern power systems have incorporated new constraints, which make the OPF problem more complex in terms of mathematical formulation and solution. This paper offers a comprehensive and foundational review of OPF, covering the main concept, mathematical formulation, OPF types, comprehensive OPF optimization problem concepts, and the various methods developed to solve it. Additionally, it explores the evolution of these methods from conventional approaches to advanced and recent techniques, including mathematical methods and artificial intelligence methods, which include metaheuristic (search-based) and machine learning algorithms (data-driven). The paper also discusses various types of convex relaxation methods in depth. Ultimately, the paper highlights key gaps, challenges, and opportunities for future research.

关键词： Power system stability Load flow Machine learning algorithms Renewable energy sources Reactive power Power systems Generators Power system dynamics Uncertainty Stochastic processes Optimal power flow (OPF) machine learning metaheuristic optimization algorithms OPF convex relaxation artificial intelligence methods renewable energy systems economic dispatch formulation

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Comparative Analysis of Hybrid and Ensemble Machine Learning Approaches in Predicting Football Player Transfer Values

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COGNITIVE COMPUTATION 2025年第2期17卷 1-25页

作者： Zhang, Wenjing Cao, Dan LiaoNing Finance & Trade Coll Inst Phys Educ Huludao 125105 Liaoning Peoples R China Shenyang Med Coll Phys Educ Dept Shenyang 110032 Liaoning Peoples R China

In football economics, a player's transfer market value extends beyond performance metrics, with popularity playing a crucial role in clubs' decisions. Reputation indexes, reflecting a player's standing in the industry, are derived from various sources. Traditional metrics include goals, assists, and defensive prowess, while social media activity (likes on Facebook and Instagram), press citations, and Wikipedia page views add a new dimension. This study utilized F & eacute;d & eacute;ration Internationale de Football Association 19 data and a real-world statistical dataset, encompassing 54 features for 491 players across various leagues. After adding valuable data and removing ineffective features and outliers, two filtering-based feature selection methods identified the 20 most critical features for predicting market value. The study applied Extreme Gradient Boosting and Adaptive Boosting regression models, along with their hybrid forms optimized by metaheuristic algorithms. The Extreme Gradient Boosting optimized with the Ali Baba and Forty Thieves algorithm model showed the best performance, with a 99% match to actual values and a misestimation of around 1.9 million. Ensemble models, averaging predictions from all hybrid models, provided reliable market value estimates. These insights help managers make informed decisions to improve team performance and secure financial benefits for the club.

关键词： Market valuation International reputation index Football superstars Filtering feature selection Boosting Tree Regression metaheuristic optimization algorithms

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Optimal loop power flow control of power distribution system using advanced meta-heuristic algorithms

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ELECTRICAL ENGINEERING 2025年第1期107卷 419-431页

作者： Vy, Huynh Tuyet Anh, Ho Pham Huy Ind Univ Ho Chi Minh City Fac Elect Technol Ho Chi Minh City Vietnam Ho Chi Minh City Univ Technol HCMUT 268 Ly Thuong Kiet St Dist 10 Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City VNU HCM Linh Trung Ward Ho Chi Minh City Vietnam

This study uses advanced metaheuristic algorithms to solve energy-saving problems in the loop distribution network. An initiative energy management system (EMS) is suggested to deliver optimal commands to the loop power controller (LPC) whose operation is optimized by combining a metaheuristic algorithm with a ladder iterative technique. Here, the performance of state-of-the-art metaheuristic algorithms applied in the proposed EMS-LPC solution is compared and evaluated by simulation in a real-world case study. Simulation results show that all metaheuristic algorithms meet the optimization objective of reducing distribution loss in which the artificial ecosystem-based optimization (AEO) algorithm outperforms with both the lowest optimum value and the fastest convergence time. Consequently, the proposed EMS-LPC method results in a daily energy savings of 18% in comparison with the base case. Then, the proposed solution has potential for real-time applications to save grid power and reduce operating expenses due to the perfect performance of the suggested EMS-LPC approach.

关键词： Loop power controller (LPC) metaheuristic optimization algorithms Power distribution system Ladder iterative technique Energy management system (EMS) Proposed EMS-LPC controller Grid energy loss saving

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A physics-data-driven method for predicting surface and building settlement induced by tunnel construction

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COMPUTERS AND GEOTECHNICS 2025年 179卷

作者： Wang, You Fan, Qianjun Dai, Fang Wang, Rui Ding, Bosong Cent South Univ Sch Civil Engn Changsha 410075 Peoples R China

Surface and surrounding building settlement is frequently caused by soil disturbance during subway tunnel construction, significantly impacting construction safety and structural stability. Traditional machine learning models have shown some effectiveness in settlement prediction but often fail to capture the underlying physical mechanisms. This study proposed a novel physics-informed optimized extreme learning machine (PIOELM) to enhance prediction accuracy and physical interpretability. Based on the extreme learning machine (ELM), the model integrated the chaos adaptive sparrow search algorithm (CASSA) for parameter optimization and incorporated the Pasternak foundation model using automatic differentiation. The model's accuracy was validated using precise engineering data and compared against the physics-informed neural network (PINN), physicsinformed extreme learning machine (PIELM), and traditional data-driven models. The results show that the PIOELM model outperforms others in handling extreme values and maintains high accuracy across various scales of data prediction. Prediction accuracy improved by up to 85.29%, with a minimum improvement of 30.68%, demonstrating strong stability and generalization capabilities.

关键词： Physics-data-driven Physics informed optimized extreme learning Pasternak foundation model metaheuristic optimization algorithms Shield tunnelling machine (PIOELM)

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Optimal parameter identification strategy applied to lithium-ion battery model for electric vehicles using drive cycle data

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ENERGY REPORTS 2024年 11卷 2049-2058页

作者： Ghadbane, Houssam Eddine Rezk, Hegazy Ferahtia, Seydali Barkat, Said Al-Dhaifallah, Mujahed Univ 8 Mai 1945 Electrotech & Automat Engn Dept Elect Engn Lab Guelma LGEG Guelma 24000 Algeria Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Alddawasir Dept Elect Engn Al Kharj Saudi Arabia Univ Nantes Ind Engn Dept IREENA Lab Nantes France Univ Msila Elect Engn Lab Elect Engn Dept Msila Algeria King Fahd Univ Petr & Minerals Control & Instrumentat Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals IRC Sustainable Energy Syst IRC SES Dhahran 31261 Saudi Arabia

The optimal parameter identification of lithium -ion (Li -ion) battery models is essential for accurately capturing battery behavior and performance in electric vehicle (EV) applications. Traditional methods for parameter identification often rely on manual tuning or trial-and-error approaches, which can be time-consuming and yield suboptimal results. In recent years, metaheuristic optimization algorithms have emerged as powerful tools for efficiently searching and identifying optimal parameter values. This paper proposes an optimal parameter identification strategy using a metaheuristic optimization algorithm applied to a Shepherd model for EV applications. The identification technique that was based on the Self -adaptive Bonobo Optimizer (SaBO) performed extremely well when it came to the process of identifying the battery's unidentified properties. Because of this, the overall voltage error of the suggested identification technique has been lowered to 4.2377 x 10-3, and the root mean square error (RMSE) between the model and the data has been calculated to be 8.64 x 10-3. In addition, compared to the other optimization methods, the optimization efficiency was able to attain 96.6%, which validated its efficiency.

关键词： Electric vehicles Li-ion battery metaheuristic optimization algorithms Parameters identification

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