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Hybrid arithmetic-Rider optimization algorithm as new intelligent model for travelling salesman problem

International Journal of Information Technology (Singapore) 2024年 1-18页

作者： Krishna, Madugula Murali Majhi, Santosh Kumar Panda, Nibedan Department of Computer Science and Engineering Veer Surendra Sai University of Technology Odisha Burla 768018 India Department of Computer Science and Information Technology Guru Ghasidas Viswavidyalaya C.G. Bilaspur India School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar 751024 India

Travelling Salesman Problem (TSP) is one of the significant NP-hard benchmark problems for performing discrete optimization. In recent times, determining the optimal route mechanism has been implemented and ensured as an important mechanism for solving practical applications. In real-time applications, energy-saving, reaching the destination at the assigned time, and identifying the shortest route is very essential. Route determination is researched by different scientists and engineers. Numerous research is done on this problem, and it is still challenging. Therefore, meta-heuristics are involved in this problem as it is inspired by the biological species. This article constitutes the continuation of the work on adapting the TSP using the hybridized algorithm named arithmetic-ROA (A-ROA). The ultimate aim of the proposed TSP is to reduce the distance travelled by the salesman while focusing on the entire city. To attain this optimal solution, the novel hybrid A-ROA optimizes the number of the city to be travelled. The results acquired by the new hybrid heuristic are compared with other heuristic algorithms. The computational results confirm that the developed algorithm obtained better results when compared with existing algorithms. The developed optimization algorithm also provides enhanced performance within a realistic amount of computational time. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： arithmetic optimization algorithm arithmetic-Rider optimization algorithm Benchmark Datasets Meta-heuristic algorithms Rider optimization algorithm Travelling Salesman Problem

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Improved arithmetic optimization algorithm with Transfer Learning based Arabic Sign Language Identification System

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ACM Transactions on Asian and Low-Resource Language Information Processing 1000年

作者： Muhammad Alzaidi King Saud University Riyadh Saudi Arabia

The Arabic sign language (ArSL) has witnessed ground-breaking research activities to identify hand gestures and signs through the deep learning (DL) model. SL is a unique communication tool that bridges the gaps between people with hearing impairment and ordinary people. The ArSL recognition system is of immense importance for different groups of people since it enables individuals with hearing impairment to communicate effectively. In SLs, signs are characterized by discrepancies in hand positions, shapes, motions, body parts, and facial expressions, posing a crucial threat to visual recognition in computer vision (CV). An automated sign detection technique needs two primary courses of action: the recognition of specific features and the classification of the input dataset. Previously, several approaches for detecting and classifying SLs had been proposed. In this study, an Improved Metaheuristics with Transfer Learning based Arabic Sign Language Identification System (IMTL-ArSL) method is developed. The primary objective of the IMTL-ArSL method is to detect and classify the existence of various signs in the Arabic language. In the IMTL-ArSL model, the bilateral filtering (BF) model is initially used for preprocessing. Besides, the Residual Network (ResNet50v2) model is applied for extracting features, and an improved arithmetic optimization algorithm (IAOA) model is utilized for hyperparameter tuning. Finally, a gated recurrent unit (GRU) network is exploited to identify sign languages. The empirical analysis of the IMTL-ArSL approach is tested using a benchmark dataset. The experimental values of the IMTL-ArSL approach portrayed a superior accuracy value of 93.87% over other techniques.

关键词： Arabic Sign Language arithmetic optimization algorithm Transfer Learning Hand Gesture Machine Learning

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Adaptive hybrid optimization algorithm for numerical computing in engineering applications

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ENGINEERING optimization 2025年第4期57卷 845-883页

作者： Wang, Hongbo Mo, Yuanbin Guangxi Minzu Univ Sch Artificial Intelligence Nanning Peoples R China Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning Peoples R China

A novel hybrid arithmetic optimization algorithm (HAOA) is proposed to address the inherent constraints of traditional numerical computing techniques, including increased computational complexity and excessive reliance on gradient information. First, the Latin hypercube sampling initialization strategy is used to generate higher quality initial candidate solutions. Then, the strategy is used to expand the advantages of leading individuals, boost the local search ability and improve the computational accuracy of the algorithm. Finally, the adaptive t-distribution mutation perturbation strategy is adopted to randomly perturb the position of the current optimal solution, aiming to prevent the algorithm from falling into a local optimal solution. The performance of HAOA is examined in comparison to alternative algorithms using the CEC 2022 data set and Bayesian validation is applied to comprehensively validate the superiority of the HAOA. Numerical experimental results demonstrate that HAOA achieves more accurate extremal and integral results, and has a high solution speed.

关键词： Numerical computing engineering problem arithmetic optimization algorithm Latin hypercube sampling optimal neighbourhood

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Software defect prediction using wrapper-based dynamic arithmetic optimization for feature selection

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CONNECTION SCIENCE 2025年第1期37卷

作者： Anand, Kunal Jena, Ajay Kumar Das, Himansu Askar, S. S. Abouhawwash, Mohamed KIIT Deemed Univ Sch Comp Engn Bhubaneswar 751024 Odisha India King Saud Univ Coll Sci Dept Stat & Operat Res Riyadh Saudi Arabia Michigan State Univ Dept Anim Sci E Lansing MI USA Mansoura Univ Fac Sci Dept Math Mansoura Egypt

Software Defect Prediction (SDP) empowers the creators to diagnose and unscramble defects in the introductory legs of the software evolution process to reduce the effort and cost invested in creating high-quality software. Feature Selection (FS) is critical to pinpoint the most pertinent features for defect prediction. This paper intends to employ a peculiar wrapper-based FS mode, dubbed DAOAFS, rooted on the dynamic arithmetic optimization algorithm (DAOA). Subsequently, this work evaluates the competence of the proposed FS mode using ten benchmark NASA datasets on four supervised learning classifiers, namely NB, DT, SVM, and KNN using accuracy and error curve as the standard performance measure metrics. This paper also correlates the proposed FS mode's conduct with existing FS techniques based on widely utilized meta-heuristic approaches such as GA, PSO, DE, ACO, FA, and SWO. This work employed Friedman and Holm test to ratify the proposed FS mode's statistical connotation. The investigatory outcomes supported the assertion that the recommended DAOAFS mode was effective in enhancing the efficacy of the defect forecasting model by achieving the highest mean accuracy of 94.76%. The findings also revealed that the proposed approach established its supremacy over the other studied FS techniques with bettered veracity in most instances.

关键词： Software defect prediction machine learning feature selection classification algorithm wrapper approach arithmetic optimization algorithm

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Evaluation of driven piles' load capacity by optimization-based prediction algorithms

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年第5期19卷 3277-3288页

作者： Xiao, Lili Du, Kun Xian FanYi Univ Coll Art & Design Xian 710105 Shaanxi Peoples R China China Northwest Architectural Design & Res Inst Co Xian 710018 Shaanxi Peoples R China

In geotechnical engineering, a variety of models-both theoretical and empirical-have been put forward to calculate the load capacity of deep footings. In the present instance, physical hypotheses and the construction of estimations utilizing mathematical concepts are the main driving forces behind the models. The research findings are directly applicable to geotechnical engineering practice. Engineers and practitioners involved in foundation design, construction, and evaluation can use the developed models to estimate the load capacity of driven piles more accurately. The load capacity of driven piles (Qt) using an experimental dataset has been estimated in the current essay using two-hybrid radial basis function (RBF\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$RBF$$\end{document}) and multilayer perceptron (MLP\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MLP$$\end{document}) neural networks optimized with an arithmetic optimization algorithm (AOA\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$AOA$$\end{document}). To ameliorate the modeling of the best network efficiency, the AOA method was used to identify the main components of each model (ARBF and AMLP). By raising the value of R2 from 0.9653 to 0.9931 for the train portion as well as from 0.9516 to 0.9884 for the test portion, with increases of around 2.78% as well as 3.68% each, the AMLP model outperforms the ARBF network among the two constructed systems. The comparis

关键词： Bearing capacity Driven piles Deep foundation Estimation Artificial neural network arithmetic optimization algorithm

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A novel AOA-based MPPT technique for 2S2P configuration of a solar PV system considering complex irradiations

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ELECTRICAL ENGINEERING 2025年 1-14页

作者： Pardhu, B. S. S. Ganesh Kota, Venkata Reddy Aditya Univ Dept Elect & Elect Engn JNTUK Surampalem 533437 Andhra Pradesh India Jawaharlal Nehru Technol Univ Kakinada Dept Elect & Elect Engn Kakinada 533003 Andhra Pradesh India

Harvesting the optimal power of solar photovoltaic (PV) cell under partial-shaded conditions (PSC) and dynamic irradiation conditions plays a vital role in efficiently utilizing solar power. This paper introduces new metaheuristic method named as arithmetic optimization algorithm (AOA) and is applied to the solar PV cell for searching the global maximum power point (GMPP) at variable irradiance. The suggested approach is modelled, and numerical simulations are performed on PV arrangements with various shading patterns using a MATLAB/Simulink environment. The proposed algorithm minimizes the oscillations in power during variable irradiation. It accurately tracks the GMPP at high tracking speed and efficiency for all shading patterns. The simulation results are compared with the P&O, musical chair algorithm (MCA), and squirrel search algorithm (SSA) in terms of tracking time, efficiency, and maximum power extracted from the panel. The simulation results strongly suggest that the AOA has the highest tracking efficiency of > 99.4% and the convergence time is < 0.0018 s under PSC. Statistical analyses are performed to observe the performance of AOA with P&O, MCA, and SSA. The results show superiority of the proposed algorithm in terms of root mean square error, and relative error of the methods during various shading patterns. Further, an experimental setup was developed, and investigations are carried with uniform radiations. Experimental results suggest that AOA enhanced system parameters over MCA technique.

关键词： Double diode model arithmetic optimization algorithm GMPP PSC Statistical analysis

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Performance evaluation of submerged FSW for aluminum 6061 employing AOA-ANN based on experimental data

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2025年

作者： Shrivastava, Ekta Shrivastava, Pankaj Kumar Singh, Pushpendra Kashyap, Akash Arya, Liladhar AKS Univ Dept Engn Math Satna MP India Govt Engn Coll Dept Mech Engn Ara Bihar India AKTU Lucknow Rajkiya Engn Coll Banda Dept Elect Engn Lucknow UP India Shri G S Inst Technol & Sci Dept Elect Engn Indore MP India

Submerged friction stir welding (SFSW) under water is a relatively new solid state joining process, which combines heating and mechanical work for deformation to achieve high quality, defect-free joints. In the present research aluminum-6061 alloy has been welded by using SFSW. Tool rotational speed, feed and water temperature has been taken as important control variables to estimate the joint performance in terms of hardness and tensile strength. It was observed that the average grain size obtained in stir zone is around 3.5 mu m, the maximum hardness obtained is 87 HV and maximum tensile strength is 175 MPa. Predictive models using artificial neural networks (ANNs) were developed for both the hardness and tensile strength, followed by process optimization utilizing four distinct evolutionary optimization techniques: arithmetic optimization algorithm (AOA), Jaya algorithm and Rao-3 algorithm. Among these, the AOA demonstrated superior performance within the present manufacturing environment. As compared to the maximum experimental values of hardness and tensile strength, the optimization by using AOA, show the improvement of 6.33% and 0.35% in hardness and tensile strength, respectively.

关键词： Submerged friction stir welding artificial neural network modeling optimization arithmetic optimization algorithm

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Hybrid Markov chain-based dynamic scheduling to improve load balancing performance in fog-cloud environment

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2025年 45卷

作者： Khaledian, Navid Razzaghzadeh, Shiva Haghbayan, Zeynab Volp, Marcus Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT Esch Sur Alzette Luxembourg Islamic Azad Univ Dept Comp Engn Ardabil Branch Ardebil Iran

Fog computing is a distributed computing paradigm that has become essential for driving Internet of Things (IoT) applications due to its ability to meet the low latency requirements of increasing IoT applications. However, fog servers can become overburdened as many IoT applications need to run on these resources, potentially leading to decreased responsiveness. Additionally, the need to handle real-world challenges such as load instability, makespan, and underutilization of virtual machine (VM) devices has driven an exponential increase in demand for effective task scheduling in IoT-based fog and cloud computing environments. Therefore, scheduling IoT applications in heterogeneous fog computing systems effectively and flexibly is crucial. The limited processing resources of fog servers make the application of ideal but computationally costly procedures more challenging. To address these difficulties, we propose using an arithmetic optimization algorithm (AOA) for task scheduling and a Markov chain to forecast the load of VMs as fog and cloud layer resources. This approach aims to establish an environmentally load-balanced framework that reduces energy usage and delay. The simulation results indicate that the proposed method can improve the average makespan, delay, and Performance Improvement Rate (PIR) by 8.29%, 11.72%, and 4.66%, respectively, compared to the crow, firefly, and grey wolf algorithms (GWA).

关键词： Task scheduling Load balancing arithmetic optimization algorithm Markov chain Fog computing

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A Hybrid Meta-heuristic algorithm for Optimum Micro-robotic Position Control with PID Controller

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2025年第1期18卷 1-27页

作者： Baihan, Abdullah Ghith, Ehab Garg, Harish Mirjalili, Seyedali Izci, Davut Rashdan, Mostafa Salman, Mohammad Saleem, Kashif King Saud Univ Community Coll Comp Sci Dept Riyadh 11437 Saudi Arabia Ain Shams Univ Fac Engn Dept Mechatron Cairo 11566 Egypt Thapar Inst Engn & Technol Dept Math Patiala 147004 Punjab India Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Australia Bursa Uludag Univ Dept Elect & Elect Engn TR-16059 Bursa Turkiye Amer Univ Middle East Coll Engn & Technol Egaila 54200 Kuwait King Saud Univ Coll Appl Studies & Community Serv Dept Comp Sci & Engn Riyadh 11362 Saudi Arabia Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Jadara Univ Res Ctr Irbid Jordan

The present paper aims to propose a novel hybrid algorithm, where the arithmetic optimization algorithm (AOA) and Rat Swarm optimization (RSO) are employed for the proportional-integral-derivative (PID) controller to control the position of a micro-robotics system. In the algorithm proposed, we combine the exploratory mechanisms of AOA with RSO's exploitative behaviors. The proposed algorithm is employed for identifying the PID controller optimal parameters considering six different objective functions. Using CEC 2017 benchmark functions, the proposed hybrid is evaluated, and these functions' performance is compared with the existing multiple algorithms. The statistical results are compared with the AOA, Jellyfish Search optimization, and Harries Hawk optimization algorithm for identifying the optimal PID controller settings considering multiple fitness functions. We consider performance indicators like PID controller parameters, rise time, settling time, and fitness values. The fetched simulation results revealed that, among all investigated fitness functions, the developed controller based on HAOARSO is the most effective algorithm for delivering global optimal solutions with less settling time and rise time, enabling the implementation on such optimization issues. Finally, the validation via MATLAB/Simulink simulations underscores the efficacy of the proposed algorithm.

关键词： PID controller Hybrid algorithm arithmetic optimization algorithm Rat swarm optimization Minimally invasive surgery

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A symbiosis of arithmetic optimizer with slime mould algorithm for improving global optimization and conventional design problem

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JOURNAL OF SUPERCOMPUTING 2022年第5期78卷 6234-6274页

作者： Chauhan, Sumika Vashishtha, Govind Kumar, Anil St Longowal Inst Engn & Technol Dept Elect & Instrumentat Engn Longowal 148106 India St Longowal Inst Engn & Technol Dept Mech Engn Precis Metrol Lab Longowal 148106 India Wenzhou Univ Coll Mech & Elect Engn Wenzhou 325035 Peoples R China

An arithmetic optimizer algorithm (AOA) is hybridized with slime mould algorithm (SMA) to address the issue of less internal memory and slow convergence at local minima which is termed as HAOASMA. Lens opposition-based learning strategy is also integrated with the hybrid algorithm which enhances the population diversity of the hybrid optimizer to accelerate the convergence. The local best (P-best) and global best (g(best)) of SMA initializes the AOA's search process. The Pbest obtained from AOA again initializes the SMA to further exploit the search space. In this way, the developed hybrid algorithm utilizes the exploitation and exploration capabilities of SMA and AOA, respectively. The developed HAOASMA has been compared on twenty-three benchmark functions at different dimensions with basic SMA, AOA and six renowned meta-heuristic algorithms. The HAOASMA has also been applied to classical engineering design problems. The performance of HAOASMA is significantly superior compared to basic SMA, AOA and other meta-heuristic algorithms.

关键词： arithmetic optimization algorithm Slime mould algorithm Lens opposition-based learning Global optimization Hybridization

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