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13th International Conference on Machine Vision

作者： Ghnimi, Oumeima Kharbech, Sofiane Belazi, Akram Bouallegue, Ammar Tunis El Manar Univ Lab SysCom ENIT LR 99 ES21 Tunis 1002 Tunisia Univ Lille Lab IEMN IRCICA UMR CNRS 8520 F-59000 Lille France Tunis El Manar Univ Lab RISC ENIT LR 16 ES07 Tunis 1002 Tunisia

ISBN: (纸本)9781510640412

A machine learning algorithm aims at designing a mathematical model based on a given training data set. Generally, the built model has a set of parameters that need to be adjusted. Since the performance of a given model depends on its settings, the parameters have to be carefully chosen through a fine-tuning step. A good model selection not only boosts performance but also allows a well-generalized model, i.e., a model that works sound on unseen data. In this paper, we assess the effectiveness of some metaheuristic optimization algorithms for support-vector machines (SVM) model selection. Computer simulations show that optimization algorithms that overall outperforms other algorithms using benchmark functions can be, further, definitely used for an efficient SVM model selection for classification. Thus, we show that Teaching-Learning-Based optimization algorithm is faster and also enables the most accurate classification, even against other proposed methods in the literature for SVM model selection.

关键词： metaheuristic optimization algorithms machine learning support-vector machines classification model selection

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Performance Comparison of metaheuristic optimization algorithms Using Water Distribution System Design Benchmarks 4th

Performance Comparison of Metaheuristic Optimization Algorit...

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4th International Conference on Harmony Search, Soft Computing and Applications (ICHSA)

作者： Lee, Ho Min Jung, Donghwi Sadollah, Ali Lee, Eui Hoon Kim, Joong Hoon Korea Univ Res Ctr Disaster Prevent Sci & Technol Seoul South Korea Sharif Univ Technol Dept Mech Engn Tehran Iran Korea Univ Sch Civil Environm & Architectural Engn Seoul South Korea

ISBN: (纸本)9789811307614;9789811307607

Various metaheuristic optimization algorithms are being developed and applied to find optimal solutions of real-world problems. Engineering benchmark problems have been often used for the performance comparison among metaheuristic algorithms, and water distribution system (WDS) design problem is one of the widely used benchmarks. However, only few traditional WDS design problems have been considered in the research community. Thus, it is very challenging to identify an algorithm's better performance over other algorithms with such limited set of traditional benchmark problems of unknown characteristics. This study proposes an approach to generate WDS design benchmarks by changing five problem characteristic factors which are used to compare the performance of metaheuristic algorithms. Obtained optimization results show that WDS design benchmark problems generated with specific characteristic under control help identify the strength and weakness of reported algorithms. Finally, guidelines on the selection of a proper algorithm for WDS design problems are derived.

关键词： metaheuristic optimization algorithms Performance measurement Water distribution systems

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Application of metaheuristic optimization algorithms-based three strategies in predicting the energy absorption property of a novel aseismic concrete material

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SOIL DYNAMICS AND EARTHQUAKE ENGINEERING 2023年第1期173卷

作者： Mei, Xiancheng Li, Chuanqi Cui, Zhen Sheng, Qian Chen, Jian Li, Shaojun Chinese Acad Sci Inst Rock & Soil Mech State Key Lab Geomech & Geotech Engn Wuhan 430071 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Grenoble Alpes Univ Lab 3SR CNRS UMR 5521 F-38000 Grenoble France

This study aims to predict the energy absorption property of a novel aseismic concrete material made of rubber, sand and cement. To investigate the energy absorption property of this novel aseismic concrete material, the energy transmission rate (ETR) was calculated by using the Split-Hopkinson Pressure Bar (SHPB) device. Furthermore, some prediction models were developed to predict the ETR in order to estimate it in the field and other laboratory environments. Therefore, six metaheuristic optimization algorithms-based three strategies (i.e., evolutionary algorithms: Differential evolution (DE) and Human felicity algorithm (HFA);physical algorithms: Nuclear reaction optimization (NRO) and Lightning search algorithm (LSA);swarm intelligence algorithms: Harris Hawks optimization (HHO) and Tunicate swarm algorithm (TSA)) and random forest (RF) model were combined to generate various hybrid prediction models for forecasting the ETR. The results indicated that the TSA-RF model has the best performance for predicting the ETR in both the training phase (RMSE: 1.5388 and R2: 0.9349) and the testing phase (RMSE: 1.6083 and R2: 0.9165). The sensitive analysis results demonstrated that cement is the most important parameter for predicting the ETR, but the rubber showed the largest negative correlation with the ETR. As a result, the application of artificial intelligence in ETR prediction has been proven to be feasible, this work can provide a novel idea for the development of aseismic materials in tunnel engineering.

关键词： Novel aseismic concrete material Energy absorption property Energy transmission rate metaheuristic optimization algorithms Tunnel engineering

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Application of Six metaheuristic optimization algorithms and Random Forest in the uniaxial compressive strength of rock prediction

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APPLIED SOFT COMPUTING 2022年 131卷

作者： Li, Jingze Li, Chuanqi Zhang, Shaohe Cent South Univ Sch Geosci & Infophys Key Lab Metallogen Predict Nonferrous Met & Geol E Minist Educ Changsha 410083 Peoples R China Grenoble Alpes Univ Lab 3SR CNRS UMR 5521 F-38000 Grenoble France

The uniaxial compressive strength (UCS) is one of the most important parameters for judging the mechanical behaviour of rock mass in rock engineering design and excavation such as tunnels, sub-ways, drilling, slopes and mines stability. However, an obvious deficiency of traditional experimental operations to obtain UCS is that it suffers from a lack of efficiency and accuracy. Therefore, the prediction of the UCS of rock is of high practical significance in reducing evaluation time and improving the precision of results. At the same time, breaking the universality problem of traditional empirical models and improving the accuracy of artificial intelligence models need to absorb and accommodate more rock samples. Hence, a total of 226 rock samples with five properties were carried out from four published studies and selected to generate a dataset in this investigation, i.e., Granitic, Caliche, Schist, Sandstone and Grade III granitic. Five individual parameters of rock samples consisting of Schmidt hardness rebound number (SHR), P-wave velocity (Vp), point load strength (Is(50)), porosity (Pn), and density (D) were used to predict UCS. In this paper, six metaheuristic optimization algorithms were utilized to improve the performance of the Random Forest (RF) model, i.e., slime mould algorithm (SMA), chameleon swarm algorithm (CSA), transient search optimization (TSO), equilibrium optimizer (EO), social network search (SNS) and student psychology based optimization algorithm (SPBO). Four performance indices , the root mean square error (RMSE), the determination coefficient (R2), Willmott's index (WI) and the variance accounted for (VAF) were utilized to evaluate the performance of all models in forecasting the UCS of rock. The results of the performance comparison demonstrated that the TSO-RF model has the highest values of R2 (train: 0.9923 and test: 0.9753), WI (train: 0.9980 and test: 0.9937), and VAF (train: 99.2272% and test: 97.6852%), the lowest values of RMSE (

关键词： Uniaxial compressive strength (UCS) metaheuristic optimization algorithms Transient Search optimization (TSO) Random Forest (RF)

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A Combination of metaheuristic optimization algorithms and Machine Learning Methods Improves the Prediction of Groundwater Level

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WATER 2022年第5期14卷 751页

作者： Kayhomayoon, Zahra Babaeian, Faezeh Milan, Sami Ghordoyee Azar, Naser Arya Berndtsson, Ronny Payame Noor Univ Dept Geol Tehran 193954697 Iran Islamic Azad Univ Tehran Sci & Res Branch Dept Water Sci & Engn Tehran *** Iran Univ Tehran Dept Irrigat & Drainage Engn Aburaihan Campus Tehran *** Iran Univ Tabriz Dept Water Engn Fac Agr Tabriz *** Iran Lund Univ Div Water Resources Engn SE-22100 Lund Sweden Lund Univ Ctr Adv Middle Eastern Studies SE-22100 Lund Sweden

Groundwater is a crucial source of water supply in drought conditions, and an auxiliary water source in wet seasons. Due to its increasing importance in view of climate change, predicting groundwater level (GWL) needs to be improved to enhance management. We used adaptive neuro-fuzzy inference systems (ANFIS) to predict the GWL of the Urmia aquifer in northwestern Iran under various input scenarios using precipitation, temperature, groundwater withdrawal, GWL during the previous month, and river flow. In total, 11 input patterns from various combinations of variables were developed. About 70% of the data were used to train the models, while the rest were used for validation. In a second step, several metaheuristic algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization for continuous domains (ACOR), and differential evolution (DE) were used to improve the model and, consequently, prediction performance. The results showed that (i) RMSE, MAPE, and NSE of 0.51 m, 0.00037 m, and 0.86, respectively, were obtained for the ANFIS model using all input variables, indicating a rather poor performance, (ii) metaheuristic algorithms were able to optimize the parameters of the ANFIS model in predicting GWL, (iii) the input pattern that included all input variables resulted in the most appropriate performance with RMSE, MAPE, and NSE of 0.28 m, 0.00019 m, and 0.97, respectively, using the ANIFS-ACOR hybrid model, (iv) results of Taylor's diagram (CC = 0.98, STD = 0.2, and RMSD = 0.30), as well as the scatterplot (R-2 = 0.97), showed that best prediction was achieved by ANFIS-ACOR, and (v) temperature and evaporation exerted stronger influence on GWL prediction than groundwater withdrawal and precipitation. The findings of this study reveal that metaheuristic algorithms can significantly improve the performance of the ANFIS model in predicting GWL.

关键词： ANFIS groundwater machine learning metaheuristic optimization algorithms time series Urmia aquifer

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Recent metaheuristic algorithms for solving some civil engineering optimization problems

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SCIENTIFIC REPORTS 2025年第1期15卷 1-25页

作者： Houssein, Essam H. Gafar, Mohamed Hossam Abdel Fawzy, Naglaa Sayed, Ahmed Y. Minia Univ Fac Comp & Informat Al Minya Egypt Minia Natl Univ Al Minya Egypt Helwan Univ Fac Engn Mataria Phys & Engn Math Dept Cairo Egypt

In this study, a novel hybrid metaheuristic algorithm, termed (BES-GO), is proposed for solving benchmark structural design optimization problems, including welded beam design, three-bar truss system optimization, minimizing vertical deflection in an I-beam, optimizing the cost of tubular columns, and minimizing the weight of cantilever beams. The performance of the proposed BES-GO algorithm was compared with ten state-of-the-art metaheuristic algorithms: Bald Eagle Search (BES), Growth Optimizer (GO), Ant Lion Optimizer, Tuna Swarm optimization, Tunicate Swarm Algorithm, Harris Hawk optimization, Artificial Gorilla Troops Optimizer, Dingo Optimizer, Particle Swarm optimization, and Grey Wolf Optimizer. The hybrid algorithm leverages the strengths of both BES and GO techniques to enhance search capabilities and convergence rates. The evaluation, based on the CEC'20 test suite and the selected structural design problems, shows that BES-GO consistently outperformed the other algorithms in terms of convergence speed and achieving optimal solutions, making it a robust and effective tool for structural optimization.

关键词： metaheuristic optimization algorithms Blade eagle search Welded beam design Civil engineering

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Image Registration Using the Arithmetic optimization Algorithm for Robotic Visual Servoing

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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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