Article
Computer Science, Artificial Intelligence
Mariusz Oszust
Summary: The paper presents an improved MPA variant using a Local Escaping Operator (LEO) to address the premature convergence issue. Experimental results demonstrate the superiority of LEO-MPA over MPA and recent algorithms, showing the effectiveness of hybridizing meta-heuristics with LEO for optimization problems.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Amrit Singh Bedi, Ketan Rajawat, Vaneet Aggarwal, Alec Koppel
Summary: Optimizing non-convex functions is crucial in modern pattern recognition for training deep networks and nonlinear dimensionality reduction. First-order algorithms have shown effectiveness under suitable randomized perturbations or step-size rules, but their practical convergence is slower than methods exploiting additional structure.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Thermodynamics
Injila Sajid, Adil Sarwar, Mohd Tariq, Farhad Ilahi Bakhsh, Shafiq Ahmad, Adamali Shah Noor Mohamed
Summary: During partial shading conditions, the efficiency of power transfer in a Photovoltaic system decreases significantly, leading to the formation of hotspots. To address this issue, using metaheuristic algorithms for Maximum Power Point Tracking (MPPT) can yield favorable results. This study introduces a novel MPPT approach based on the Archimedes Optimization Algorithm (AOA) and evaluates its performance against other state-of-the-art algorithms.
Article
Multidisciplinary Sciences
Rashwinder Singh, Ranjit Kaur
Summary: In this study, the Levy Flight Archimedes optimizer (LAO) is proposed by combining Levy flight with the Archimedes optimization algorithm (AOA) to address the convergence issues of AOA. The LAO method improves the robustness of the algorithm by enhancing the exploration phase and ignoring local optima. The method is successfully applied to antenna problems and its robustness is demonstrated through simulation results.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2022)
Article
Thermodynamics
Tabbi Wilberforce, Hegazy Rezk, A. G. Olabi, Emmanuel I. Epelle, Mohammad Ali Abdelkareem
Summary: One of the primary challenges in fuel cell modeling is determining specific boundary conditions, which are often not fully provided by the manufacturer. This study proposed using five different algorithms to determine seven unknown parameters that affect the mathematical modeling of the cell. The artificial ecosystem-based algorithm showed the best results compared to other algorithms, indicating its effectiveness in improving accuracy and predicting performance.
Article
Operations Research & Management Science
Coralia Cartis, Lindon Roberts, Oliver Sheridan-Methven
Summary: The study investigates the potential of applying a state-of-the-art local derivative-free solver, Py-BOBYQA, to global optimization problems. It demonstrates the effectiveness of restarts procedures in helping Py-BOBYQA escape local minima and compares its performance with other global optimization methods, showing that Py-BOBYQA with adaptive restarts performs comparably in various settings.
Article
Energy & Fuels
Xunzhao Yu, Ling Zhu, Yan Wang, Dimitar Filev, Xin Yao
Summary: Engine calibration is the process of optimizing engine settings to achieve optimal performance, including minimal fuel consumption, emissions, and maximum power output. With the advancement of technology, modern engines have more adjustable parameters, making the calibration task more complicated. This survey reviews the state-of-the-art applications of optimization approaches in different types of internal combustion engines, covering gasoline, diesel, and hybrid engines.
Article
Automation & Control Systems
Yuhao Ding, Javad Lavaei, Murat Arcak
Summary: A limitation of online algorithms in tracking time-varying nonconvex optimization problems is their focus on specific local minimum trajectories, which can result in poor spurious local solutions. However, this article demonstrates that the natural temporal variation can actually assist online tracking methods in finding and tracking time-varying global minima. By studying a time-varying projected gradient flow system with inertia, the authors show that the inherent temporal variation can reshape the landscape of the Lagrange functional, allowing a proximal algorithm to escape spurious local minimum trajectories if the global minimum trajectory is dominant. Sufficient conditions are also derived for guaranteeing the tracking of a time-varying global solution by any locally initialized search method in problems with twice continuously differentiable objective functions and constraints.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Haiyang Liu, Xingong Zhang, Hanxiao Zhang, Zhong Cao, Zhaohui Chen
Summary: This paper presents an improved arithmetic optimization algorithm that incorporates hybrid elite pool strategies to address the limitations of the arithmetic optimization algorithm (AOA). The proposed algorithm reconstructs a nonlinear MOA function to balance the exploitation and exploration of AOA, and integrates four hybrid elite pool strategies to enhance the ability to escape local optima. Experimental results demonstrate that the proposed algorithm outperforms other compared meta-heuristic algorithms in terms of convergence speed and accuracy on benchmark functions and engineering problems.
Article
Computer Science, Interdisciplinary Applications
Shuhui Hao, Changcheng Huang, Ali Asghar Heidari, Huiling Chen, Lingzhi Li, Abeer D. Algarni, Hela Elmannai, Suling Xu
Summary: Early detection and treatment of fast-growing skin cancers can significantly extend patients' lives. Dermoscopy is a reliable tool for detecting skin cancer in its early stages, and the effective processing of digital dermoscopy images is crucial for improving diagnosis accuracy. This paper proposes an improved salp swarm algorithm (ILSSA) method that combines iterative mapping and local escaping operator to address the issue of local optima frequently encountered in existing meta-heuristic algorithms. Moreover, the ILSSA-based multi-threshold image segmentation approach successfully segments dermoscopic images of skin cancer using 2D Kapur's entropy as the objective function and non-local means 2D histogram to represent image information. Benchmark function test experiments demonstrate that ILSSA effectively alleviates the local optimal problem compared to other algorithms. The skin cancer dermoscopy image segmentation experiment further proves the superiority and adaptability of the proposed ILSSA-based method over other existing approaches at different thresholds.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Computer Science, Information Systems
Abeer S. Desuky, Sadiq Hussain, Samina Kausar, Md Akhtarul Islam, Lamiaa M. El Bakrawy
Summary: Feature selection is crucial in mitigating high dimensional feature space in classification problems. The paper introduces an Enhanced Archimedes Optimization Algorithm (EAOA) to improve exploration and exploitation balance for feature selection. Experimental results demonstrate that EAOA algorithm outperforms basic AOA and other optimization algorithms in terms of improved exploitation, exploration, local optima avoidance, and convergence rate.
Article
Computer Science, Artificial Intelligence
Guiyan Ding, Wentao Wang, Hao Liu, Liangping Tu
Summary: Archimedes Optimization Algorithm (AOA) is a new meta-heuristic algorithm inspired by the buoyancy force based on Archimedes principle. However, the study found that AOA is flawed as it does not completely follow the buoyancy principle in the iterative update. To address this issue, a corrected algorithm named CAOA is proposed. The CAOA outperforms other optimization algorithms in benchmark functions CEC2017, demonstrating its effectiveness and superiority.
Article
Computer Science, Interdisciplinary Applications
Krishna Gopal Dhal, Swarnajit Ray, Rebika Rai, Arunita Das
Summary: The complexity of real-world optimization problems has necessitated proficient and ingenious optimization algorithms, particularly in cases where classical approaches fall short. In recent years, approaches based on nonlinear physics have emerged and flourished, surpassing existing methods in their exploration capabilities. Archimedes Optimization Algorithm (AOA), a physics-inspired optimization algorithm, has shown promising outcomes in the field of image segmentation, especially when using Tsallis entropy as the objective function.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Engineering, Aerospace
Katherine Hendrickson, Prashant Ganesh, Kyle Volle, Paul Buzaud, Kevin Brink, Matthew Hale
Summary: This paper introduces a new approach for distributed, autonomous assignment planning in weapon-target assignment problem, which has guaranteed convergence rate even with asynchronous computations and communications, and exhibits resilience to time-varying scenarios.
JOURNAL OF GUIDANCE CONTROL AND DYNAMICS
(2023)
Article
Engineering, Electrical & Electronic
Ozan Akdag
Summary: An Improved Archimedes Optimization Algorithm (IAOA) is proposed in this paper to solve the Optimal Power Flow problem. By increasing population diversity, improving the balance between exploitation and exploration, and avoiding premature convergence, the IAOA algorithm is shown to be effective. The algorithm is tested and compared on different systems, demonstrating its robustness.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Emre Celik, Nihat Ozturk, Essam H. Houssein
Summary: This paper investigates the use of energy storage devices (ESDs) as back-up sources to escalate load frequency control (LFC) of power systems (PSs). The PS models implemented here are 2-area linear and nonlinear non-reheat thermal PSs besides 3-area nonlinear hydro-thermal PS. PID controller is employed as secondary controller in each control area and ESDs such as battery energy storage system, flywheel energy storage system and ultra-capacitor are employed to assist LFC task during crest load disturbances.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Majdi Mafarja, Thaer Thaher, Jingwei Too, Hamouda Chantar, Hamza Turabieh, Essam H. Houssein, Marwa M. Emam
Summary: This paper proposes an effective feature selection (FS) method, Multi-strategy Gray Wolf Optimizer (MSGWO), for biological data classification. By utilizing multiple exploration and exploitation strategies during the optimization process, MSGWO demonstrates superiority in feature selection and addressing search problems, resulting in better classification accuracy and feature selection performance compared to other algorithms.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Energy & Fuels
Abdul Ghani Olabi, Hegazy Rezk, Mohammad Ali Abdelkareem, Tabbi Awotwe, Hussein M. M. Maghrabie, Fatahallah Freig Selim, Shek Mohammod Atiqure Rahman, Sheikh Khaleduzzaman Shah, Alaa A. Zaky
Summary: In this paper, a modified bald eagle search optimization algorithm is applied for the first time to determine the parameters of the triple diode model of perovskite solar cells. Experimental datasets of standard conditions and a modified PSC are considered, and the root mean square error is used as the cost function. A comparison with other optimization algorithms is conducted to prove the superiority of the modified bald eagle search optimization. Statistical analysis is performed, and the results demonstrate the lead of the recommended algorithm in identifying the parameters of the TDM for PSCs.
Article
Energy & Fuels
Mokhtar Aly, Emad A. Mohamed, Hegazy Rezk, Ahmed M. Nassef, Mostafa A. Elhosseini, Ahmed Shawky
Summary: The concept of green building has gained popularity, and fuel cells have been widely incorporated into green buildings. This paper presents a modified fuzzy logic control-based method for maximum power point tracking (MPPT) in proton exchange membrane fuel cells (PEMFCs). The proposed method utilizes the rate of change of power with current (dP/dI) for fast and stable MPPT operation. The design optimization of the proposed method is achieved using an enhanced version of the success-history-based adaptive differential evolution (SHADE) algorithm. The proposed method shows superiority and effectiveness in various operating scenarios, and can be applied to other renewable energy and fuel cell applications.
Article
Thermodynamics
Tabbi Wilberforce, Hegazy Rezk, A. G. Olabi, Emmanuel I. Epelle, Mohammad Ali Abdelkareem
Summary: One of the primary challenges in fuel cell modeling is determining specific boundary conditions, which are often not fully provided by the manufacturer. This study proposed using five different algorithms to determine seven unknown parameters that affect the mathematical modeling of the cell. The artificial ecosystem-based algorithm showed the best results compared to other algorithms, indicating its effectiveness in improving accuracy and predicting performance.
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Mohammed R. Saad, Abdelmgeid A. Ali, Hassan Shaban
Summary: The multi-objective gorilla troops optimizer (MOGTO) is proposed to address multi-objective optimization issues. It is evaluated using the CEC 2020 test bed and large-scale wireless sensor networks, and outperforms other optimization models in terms of various indicators.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Environmental Sciences
Ahmed M. Nassef, Hegazy Rezk, Ali Alahmer, Mohammad Ali Abdelkareem
Summary: This study combines artificial intelligence and metaheuristics techniques to determine the optimal operating parameters for carbon dioxide (CO2) capture. The aim is to maximize the CO2 capture capacity. By integrating fuzzy modeling with the RUNge Kutta optimizer (RUN), the impact of carbonation temperature, carbonation duration, and H2O-to-CO2 flow rate ratio is analyzed. The proposed method shows superior performance compared to other optimization methods, resulting in a significant increase in CO2 capture capacity.
Article
Multidisciplinary Sciences
Essam H. Houssein, Gaber M. Mohamed, Ibrahim A. Ibrahim, Yaser M. Wazery
Summary: Image segmentation is the process of separating pixels of an image into multiple classes for object analysis. This paper proposes the improved heap-based optimizer (IHBO) to solve the high computational cost problem in multilevel thresholding (MTH) image segmentation. Experimental results demonstrate that the IHBO algorithm outperforms other methods in terms of fitness values, structural similarity index, feature similarity index, and peak signal-to-noise ratio, making it a superior choice for MTH image segmentation.
SCIENTIFIC REPORTS
(2023)
Article
Engineering, Marine
Hegazy Rezk, A. G. Olabi, Mohammad Ali Abdelkareem, Ali Alahmer, Enas Taha Sayed
Summary: The use of green hydrogen as a fuel source in the marine industry has the potential to reduce its carbon footprint. Developing a sustainable method for producing green hydrogen is important, and water electrolysis is the best method for renewable energy production. By using ANFIS modeling and POA optimization, the hydrogen production can be increased.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Mathematics
Hesham Alhumade, Essam H. H. Houssein, Hegazy Rezk, Iqbal Ahmed Moujdin, Saad Al-Shahrani
Summary: Recently, the Artificial Hummingbird Algorithm (AHA) has been proposed as a swarm-based method for optimization problems. In this paper, a modified version of AHA called mAHA is proposed, combining genetic operators. Experimental results demonstrate that mAHA improves convergence speed and search results. mAHA is then used for the first time to find the global maximum power point (MPP) in photovoltaic (PV) systems with shading.
Article
Biology
Essam H. Houssein, Diego Oliva, Nagwan Abdel Samee, Noha F. Mahmoud, Marwa M. Emam
Summary: This paper introduces a new bio-inspired optimization algorithm called the Liver Cancer Algorithm (LCA), which provides efficient search and exploration methods by simulating the growth and spread of liver tumors. Experimental results show that the LCA algorithm outperforms other methods in handling mathematical benchmark problems and feature selection.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Gang Hu, Wenlong Jing, Essam H. Houssein
Summary: This paper proposes an improved Artificial Rabbits Optimization (IARO) algorithm, which increases population diversity and the ability to escape local minima by incorporating mutation strategy, adaptive group strategy, and Elite-feedback strategy. The superiority of IARO is verified through comparisons with other algorithms and statistical testing. The IARO is used to solve the multi-degree reduction optimization models for ball NURBS curves, and the optimal approximate curves are obtained.
Article
Engineering, Chemical
Hegazy Rezk, Ali Alahmer, Rania M. Ghoniem, Samer As'ad
Summary: This study explores the application of artificial intelligence and the Marine Predators Algorithm in maximizing CO2 absorption from waste concrete powder. The study utilizes the adaptive neuro-fuzzy inference system model and optimization algorithm, resulting in improved modeling accuracy and CO2 uptake.
Review
Mathematics, Interdisciplinary Applications
Ahmed M. Nassef, Mohammad Ali Abdelkareem, Hussein M. Maghrabie, Ahmad Baroutaji
Summary: Metaheuristic optimization algorithms play a significant role in improving the performance of systems, especially when traditional analytical methods fail. This paper provides a systematic review of the role of metaheuristic optimization algorithms in determining the optimal parameters for fractional-order controllers. The study shows that Particle Swarm Optimization is the most commonly used optimizer, and the Integral of the Time-Weighted Absolute Error is the best nominated cost function.
FRACTAL AND FRACTIONAL
(2023)
Article
Energy & Fuels
Ahmed Fathy, Hegazy Rezk, Seydali Ferahtia, Rania M. Ghoniem, Reem Alkanhel
Summary: This study proposes an energy management scheme for microgrid (MG) using the recent metaheuristic honey badger algorithm (HBA) to reduce energy costs, mitigate pollutant emissions, and maximize the use of renewable energy resources. The HBA is compared with other reported approaches and proves to achieve the best operation of MG in all studied operating conditions.
Article
Automation & Control Systems
Carmen Bisogni, Lucia Cimmino, Michele Nappi, Toni Pannese, Chiara Pero
Summary: This paper presents a gait-based emotion recognition method that does not rely on facial cues, achieving competitive performance on small and unbalanced datasets. The proposed approach utilizes advanced deep learning architecture and achieves high recognition and accuracy rates.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Soung Sub Lee
Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jian Wang, Xiuying Zhan, Yuping Yan, Guosheng Zhao
Summary: This paper proposes a method of user recruitment and adaptation degree improvement via community collaboration to solve the task allocation problem in sparse mobile crowdsensing. By matching social relationships and perception task characteristics, the entire perceptual map can be accurately inferred.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen
Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li
Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou
Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kemal Ucak, Gulay Oke Gunel
Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dexuan Zou, Mengdi Li, Haibin Ouyang
Summary: In this study, a photovoltaic thermal collector is integrated into a combined cooling, heating, and power system to reduce primary energy consumption, operation cost, and carbon dioxide emission. By applying a novel genetic algorithm and constraint handling approach, it is found that the CCHP scenarios with PV/T are more efficient and achieve the lowest energy consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Abhinav Pandey, Litton Bhandari, Vidit Gaur
Summary: This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zahra Ramezanpoor, Adel Ghazikhani, Ghasem Sadeghi Bajestani
Summary: Time series analysis is a method used to analyze phenomena with temporal measurements. Visibility graphs are a technique for representing and analyzing time series, particularly when dealing with rotations in the polar plane. This research proposes a visibility graph algorithm that efficiently handles biological time series with rotation in the polar plane. Experimental results demonstrate the effectiveness of the proposed algorithm in both synthetic and real world time series.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
ChunLi Li, Qintai Hu, Shuping Zhao, Jigang Wu, Jianbin Xiong
Summary: Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial. However, the nonlinear and non-stationary vibration signals generated in harsh environments pose challenges in distinguishing fault signals from normal ones. This paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model to address these challenges. The effectiveness of the proposed method in fault diagnosis is demonstrated through validation and comparative analysis with a published method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Fatemeh Chahkoutahi, Mehdi Khashei
Summary: The classification rate is the most important factor in selecting an appropriate classification approach. In this paper, the influence of different cost/loss functions on the classification rate of different classifiers is compared, and empirical results show that cost/loss functions significantly affect the classification rate.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jicong Duan, Xibei Yang, Shang Gao, Hualong Yu
Summary: The study proposes a novel partition-based imbalanced multi-label learning algorithm, MLHC, which divides the original label space into disconnected subspaces using hierarchical clustering. It successfully tackles the class imbalance problem in multi-label data and outperforms other class imbalance multi-label learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Review
Automation & Control Systems
Qing Qin, Yuanyuan Chen
Summary: This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
