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arithmetic optimization algorithm with deep learning enabled airborne particle-bound metals size prediction model

CHEMOSPHERE 2022年第Part1期303卷 134960页

作者： Almalawi, Abdulmohsen Khan, Asif Irshad Alsolami, Fawaz Alkhathlan, Ali Fahad, Adil Irshad, Kashif Alfakeeh, Ahmed S. Qaiyum, Sana King Abdulaziz Univ Comp Sci Dept Jeddah 21589 Saudi Arabia Al Baha Univ Coll Comp Sci & Informat Technol Dept Comp Sci Al Baha 65527 Saudi Arabia King Fahd Univ Petr & Minerals Res Inst Interdisciplinary Res Ctr Renewable Energy & Power Dhahran 31261 Saudi Arabia KACARE Energy Res & Innovat Ctr Dhahran Dhahran Saudi Arabia King Abdulaziz Univ Dept Informat Syst Jeddah 21589 Saudi Arabia Univ Teknol PETRONAS Ctr Res Data Sci Seri Iskandar 32610 Perak Malaysia

Recently, heavy metal air pollution has received significant interest in computing the total concentration of every toxic metal. Chemical fractionation of possibly toxic substances in airborne particles becomes a vital element. Among the primary and secondary air pollutants, airborne particulate matter (APM) received considerable internet among research communities owing to the adversative impact on human health. Hence, size distribution details of airborne heavy metals are important in assessing the adverse health effects over the globe. Recently, deep learning models have gained significant interest over the mathematical and statistical prediction models. In this view, this paper presents a novel arithmetic optimization algorithm (AOA) with multi-head attention based bidirectional long short-term memory (MABLSTM) model for predicting the size fractionated airborne particle bound metals. The proposed AOA-MABLSTM technique focuses on the forecasting of the size-fractionated airborne particle bound matter. The presented model intends to examine the concentration of PM and distinct sized-fractionated APM. The proposed model establishes MABLSTM based accurate predictive approaches for atmospheric heavy 83 metals is used for determining temporal trend of heavy metal. The proposed model employs AOA based hyperparameter tuning process to optimally tune the hyperparameters included in the MABLSTM method. To demonstrate the improved outcomes of the AOA-MABLSTM approach, a comparison study is performed with recent models. The stimulation results emphasized the betterment of the presented model over the other methods. Aluminum metal had an RMSE of 73.200 for AOA-MABLSTM. On Cu metal, the AOA-MABLSTM approach had an RMSE of 6.747. On Zn metal, the AOA-MABLSTM system lowered the RMSE by 45.250.

关键词： Air pollution Deep learning arithmetic optimization algorithm Airborne particle bound metals LSTM model

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arithmetic optimization algorithm Quantization Optimized With Energy Detection Using Nonparametric Amplitude

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第1期38卷

作者： Nesakumar, Darwin A. Devi, Rukmani S. Inbamalar, T. M. Pavithra, K. N. RMK Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India RMD Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India RMK Coll Engn & Technol Dept Elect & Commun Engn Chennai Tamil Nadu India RMD Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India

The spectrum sensing is a major significant task in cognitive radio networks (CRNs) to avoid the unacceptable interference to primary users (PUs). Here, the threshold value determines the effectiveness of spectrum sensing and regarded as a sensing system. The fixed threshold used by the current energy detection-based spectrum sensing (SS) techniques does not provide sufficient safety for the main users. The threshold is determined by lowering the complete probability of decision error in addition to these guidelines. Therefore, an energy detection using nonparametric amplitude quantization optimized with arithmetic optimization algorithm for enhanced spectrum sensing in CRNs (ED-NAQ-AOA-SS CRN) is proposed in this paper to acquire the ideal threshold for decreasing the total error probability. The proposed method achieves greater probability of detection of 99.67%, 98.38%, 92.34%, and 97.45%, lower settling time of 98.33%, 89.34%, 83.12%, and 88.96%, and lower error rate of 93.15%, 91.25%, 79.90%, and 92.88% compared with existing techniques, like intelligent spectrum sharing and sensing in CRN with adaptive rider optimization algorithm (AROA), a novel technique for spectrum sensing in CRN utilizing fractional gray wolf optimization with the cuckoo search optimization (GWOCS), and adaptive neuro-fuzzy inference scheme depending on cooperative spectrum sensing optimization in CRNs (ANFIS). This paper presents a novel method using energy detection and nonparametric amplitude quantization, optimized with the arithmetic optimization algorithm (AOA), for spectrum sensing in cognitive radio networks (CRNs). The proposed ED-NAQ-AOA-SS method achieves superior detection probability, lower error rates, and reduced settling time compared to traditional techniques, enhancing the safety and performance of primary users in ***

关键词： arithmetic optimization algorithm cognitive radio network nonparametric amplitude quantization-based energy detection

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Comparison of the arithmetic optimization algorithm, the slime mold optimization algorithm, the marine predators algorithm, the salp swarm algorithm for real-world engineering applications

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MATERIALS TESTING 2021年第5期63卷 448-452页

作者： Gurses, Dildar Bureerat, Sujin Sait, Sadiq M. Yildiz, Ali Riza Uludag Univ Dept Mech Engn Bursa Turkey Khon Kaen Univ Dept Mech Engn Khon Kaen Thailand KFUPM Comp Engn Dhahran Saudi Arabia KFUPM Ctr Commun & IT Res Dhahran Saudi Arabia Bursa Uludag Univ Dept Automot Engn Bursa Turkey

This paper focuses on a comparision of recent algorithms such as the arithmetic optimization algorithm, the slime mold optimization algorithm, the marine predators algorithm, and the salp swarm algorithm. The slime mold algorithm (SMA) is a recent optimization algorithm. In order to strengthen its exploitation and exploration abilities, in this paper, a new hybrid slime mold algorithm-simulated annealing algorithm (HSMA-SA) has been applied to structural engineering design problems. As a result of the rules and practices that have become mandatory for fuel emissions by international organizations and governments, there is increasing interest in the design of vehicles with minimized fuel emissions. Many scientific studies have been conducted on the use of metaheuristic methods for the optimum design of vehicle components, especially for reducing vehicle weight. With the inspiration obtained from the above-mentioned methods, the HSMA-SA has been studied to solve the shape optimization of a design case to prove how the HSMA-SA can be used to solve shape optimization problems. The HSMA-SA provides better results as an arithmetic optimization algorithm than the slime mold optimization algorithm, the marine predators algorithm, and the salp swarm algorithm.

关键词： Design optimization arithmetic optimization algorithm slime mould optimization algorithm marine predators algorithm salp swarm algorithm

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A new binary arithmetic optimization algorithm for uncapacitated facility location problem

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NEURAL COMPUTING & APPLICATIONS 2024年第8期36卷 4151-4177页

作者： Bas, Emine Yildizdan, Gulnur Konya Tech Univ Fac Engn & Nat Sci Dept Software Engn TR-42075 Konya Turkiye Selcuk Univ Kulu Vocat Sch Konya Turkiye

arithmetic optimization algorithm (AOA) is a heuristic method developed in recent years. The original version was developed for continuous optimization problems. Its success in binary optimization problems has not yet been sufficiently tested. In this paper, the binary form of AOA (BinAOA) has been proposed. In addition, the candidate solution production scene of BinAOA is developed with the xor logic gate and the BinAOAX method was proposed. Both methods have been tested for success on well-known uncapacitated facility location problems (UFLPs) in the literature. The UFL problem is a binary optimization problem whose optimum results are known. In this study, the success of BinAOA and BinAOAX on UFLP was demonstrated for the first time. The results of BinAOA and BinAOAX methods were compared and discussed according to best, worst, mean, standard deviation, and gap values. The results of BinAOA and BinAOAX on UFLP are compared with binary heuristic methods used in the literature (TSA, JayaX, ISS, BinSSA, etc.). As a second application, the performances of BinAOA and BinAOAX algorithms are also tested on classical benchmark functions. The binary forms of AOA, AOAX, Jaya, Tree Seed algorithm (TSA), and Gray Wolf optimization (GWO) algorithms were compared in different candidate generation scenarios. The results showed that the binary form of AOA is successful and can be preferred as an alternative binary heuristic method.

关键词： Binary optimization arithmetic optimization algorithm Logic gate Uncapacitated facility location problem

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BAOA: Binary arithmetic optimization algorithm With K-Nearest Neighbor Classifier for Feature Selection

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IEEE ACCESS 2023年 11卷 94094-94115页

作者： Khodadadi, Nima Khodadadi, Ehsan Al-Tashi, Qasem El-Kenawy, El-Sayed M. Abualigah, Laith Abdulkadir, Said Jadid Alqushaibi, Alawi Mirjalili, Seyedali Univ Miami Dept Civil & Architectural Engn Coral Gables FL 33146 USA Univ Arkansas Dept Chem & Biochem Fayetteville AR 72701 USA Univ Texas MD Anderson Canc Ctr Dept Imaging Phys Houston TX 77094 USA Albaydha Univ Fac Adm & Comp Sci Albaydha Yemen Delta Higher Inst Engn & Technol Dept Commun & Elect Mansoura 35111 Egypt Al al Bayt Univ Prince Hussein Bin Abdullah Fac Informat Technol Comp Sci Dept Mafraq 25113 Jordan Lebanese Amer Univ Dept Elect & Comp Engn Byblos 11022801 Lebanon Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ MEU Res Unit Amman 11831 Jordan Appl Sci private Univ Appl Sci Res Ctr Amman 11931 Jordan Univ Teknol PETRONAS Dept Comp & Informat Sci Seri Iskandar 32610 Malaysia Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane SA 4006 Australia Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary

The arithmetic optimization algorithm (AOA) is a recently proposed metaheuristic algorithm that has been shown to perform well in several benchmark tests. The AOA is a metaheuristic that uses the main arithmetic operators' distribution behavior, such as multiplication, division, subtraction, and addition. This paper proposes a binary version of the arithmetic optimization algorithm (BAOA) to tackle the feature selection problem in classification. The algorithm's search space is converted from a continuous to a binary one using the sigmoid transfer function to meet the nature of the feature selection task. The classifier uses a method known as the wrapper-based approach K-Nearest Neighbors (KNN), to find the best possible solutions. This study uses 18 benchmark datasets from the University of California, Irvine (UCI) repository to evaluate the suggested binary algorithm's performance. The results demonstrate that BAOA outperformed the Binary Dragonfly algorithm (BDF), Binary Particle Swarm optimization (BPSO), Binary Genetic algorithm (BGA), and Binary Cat Swarm optimization (BCAT) when various performance metrics were used, including classification accuracy, selected features as well as the best and worst optimum fitness values.

关键词： Feature extraction Machine learning algorithms Classification algorithms arithmetic Heuristic algorithms Task analysis Computational modeling Binary codes optimization methods Feature selection binary optimization arithmetic optimization algorithm classification

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Accelerated fuzzy min-max neural network and arithmetic optimization algorithm for optimizing hyper-boxes and feature selection

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NEURAL COMPUTING & APPLICATIONS 2024年第4期36卷 1553-1568页

作者： Alzaqebah, Malek Ahmed, Eman A. E. Imam Abdulrahman Bin Faisal Univ Coll Sci Dept Phys POB 1982 Dammam 31441 Saudi Arabia Imam Abdulrahman Bin Faisal Univ Basic & Appl Sci Res Ctr POB 1982 Dammam 31441 Saudi Arabia

The fuzzy min-max (FMM) neural network effectively solves classification problems. Despite its success, it has been observed recently that FMM has overlapping between hyper-boxes in some datasets which certainly the overall classification performance, as well as FMM has a high compactional complexity, especially when dealing with high-dimensional datasets. a hybrid model combining arithmetic optimization algorithm (AOA) and Accelerated fuzzy min-max (AFMM) neural network is proposed to produce an AFMM-AOA model, where AFMM is used to speed up the hyper-boxes contraction process and to reduce the number of hyper-boxes, then AOA is employed for selecting the optimal feature set in each hyper-box, which results in lowering the compactional complexity and overcoming the overlapping problem. Furthermore, the AOA algorithm has been modified (MAOA) to enhance the exploiting ability of the original AOA algorithm for handling the high dimensionality in hyper-box representation by introducing both random and neighbor search methods. The performance of the proposed methods is evaluated using twelve datasets, as a result, the neighbor search method shows better performance than the random search. In addition, both methods showed superior performance compared with the original AOA and some state-of-the-art algorithms.

关键词： Fuzzy minimum maximum arithmetic optimization algorithm Hyperbox optimization Feature selection

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Optimal Volterra-based nonlinear system identification using arithmetic optimization algorithm assisted with Kalman filter

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EVOLVING SYSTEMS 2023年第1期14卷 117-139页

作者： Singh, Sandeep Ashok, Alaknanda Rawat, Tarun Kumar Uttarakhand Tech Univ Dehra Dun Uttarakhand India Maharaja Surajmal Inst Technol New Delhi India GB Pant Univ Agr & Technol Pantnagar Uttar Pradesh India Netaji Subhas Univ Technol New Delhi India

This paper presents the handling of nonlinear system identification problem based on Volterra-type nonlinear systems. An efficient arithmetic optimization algorithm (AOA) along with the Kalman filter (KF) is being used for the estimation/identification purpose. The KF is proved to be a good state estimator in estimation theory. It is used to estimate the unknown variables with some given measurements observed over time. However, the performance of KF technique degrades while dealing with real-time state estimation problems. To overcome the problem encountered in KF technique, two steps are followed for nonlinear system identification. The first one involves evaluation of the KF parameters using the AOA algorithm by taking a considerable fitness function. The second step is to estimate the parameters of Volterra model using the KF method utilizing the optimal KF parameters attained in first step. In order to prove the efficiency of the proposed KF assisted AOA algorithm is further tested on various benchmark unknown Volterra models. Simulated results are reported in terms of mean square error (MSE), mean square deviation (MSD), Volterra coefficients estimation error, and fitness percentage. The results are compared with other similar algorithms such as sine cosine algorithm (SCA) assisted KF (SCA-KF), cuckoo search algorithm (CSA) assisted KF (CSA-KF), particle swarm optimization (PSO) assisted KF (PSO-KF) and genetic algorithm (GA) assisted KF (GA-KF). The reported results reveal that AOA-KF algorithm is the right choice for nonlinear system identification problem compared to the SCA-KF, CSA-KF, PSO-KF and GA-KF.

关键词： Nonlinear system identification Volterra model Kalman Filter arithmetic optimization algorithm Sine cosine algorithm Cuckoo search algorithm Particle swarm optimization algorithm Genetic algorithm

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An improved arithmetic optimization algorithm for task offloading in mobile edge computing

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第2期27卷 1667-1682页

作者： Li, Hongjian Liu, Jiaxin Yang, Lankai Liu, Liangjie Sun, Hu Chongqing Univ Posts & Telecommun Dept Comp Sci & Technol Chongqing 400065 Peoples R China

The emergence of Mobile Edge Computing (MEC) not only provides low-latency computing services for the User Equipment (UE), but also extends the battery life of the UE. However, the computational resources of MEC servers are usually limited, and how to efficiently offload UE's task and allocate the resources of MEC servers has become a research hotspot in MEC. In this paper, we develop an improved arithmetic optimization algorithm (IAOA) to optimize the convergence speed and convergence accuracy of the arithmetic optimization algorithm. Then a task offloading algorithm based on IAOA is designed to reduce the cost of offloading tasks in the framework including a single MEC server and multi-UE. The proposed algorithm jointly optimizes the task offloading strategy of the UEs and the resource allocation of the MEC server, meanwhile, models the weighted sum of delay and energy consumption as the system cost, with the goal of minimizing the system cost while satisfying the delay and energy consumption constraints of the tasks. Simulation results show that the proposed algorithm can effectively reduce the system cost and achieve a performance improvement of up to 20% compared with the benchmark algorithm.

关键词： Mobile edge computing Task offloading Limited computational resources Energy arithmetic optimization algorithm

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An improved arithmetic optimization algorithm with forced switching mechanism for global optimization problems

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第1期19卷 473-512页

作者： Zheng, Rong Jia, Heming Abualigah, Laith Liu, Qingxin Wang, Shuang Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Hainan Univ Sch Comp Sci & Technol Haikou 570228 Hainan Peoples R China

arithmetic optimization algorithm (AOA) is a newly proposed meta-heuristic method which is inspired by the arithmetic operators in mathematics. However, the AOA has the weaknesses of insufficient exploration capability and is likely to fall into local optima. To improve the searching quality of original AOA, this paper presents an improved AOA (IAOA) integrated with proposed forced switching mechanism (FSM). The enhanced algorithm uses the random math optimizer probability (RMOP) to increase the population diversity for better global search. And then the forced switching mechanism is introduced into the AOA to help the search agents jump out of the local optima. When the search agents cannot find better positions within a certain number of iterations, the proposed FSM will make them conduct the exploratory behavior. Thus the cases of being trapped into local optima can be avoided effectively. The proposed IAOA is extensively tested by twenty-three classical benchmark functions and ten CEC2020 test functions and compared with the AOA and other wellknown optimization algorithms. The experimental results show that the proposed algorithm is superior to other comparative algorithms on most of the test functions. Furthermore, the test results of two training problems of multi-layer perceptron (MLP) and three classical engineering design problems also indicate that the proposed IAOA is highly effective when dealing with real-world problems.

关键词： arithmetic optimization algorithm meta-heuristic algorithm global optimization exploration and exploitation high-dimensional optimization problems

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Improved arithmetic optimization algorithm for patient admission scheduling problem

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SOFT COMPUTING 2023年第7-8期28卷 5853页

作者： Alawad, Noor Aldeen Abed-alguni, Bilal H. Saleh, Israa I. Yarmouk Univ Dept Comp Sci Irbid Jordan

The patient admission scheduling problem (PASP) has been studied for many years as one of the most important scheduling problems in the health sector. The primary goal of PASP is to assign patients to appropriate hospital beds while considering some hard and soft constraints. PASP is an NP-hard problem, which implies that the optimization approach is one of the best approaches that can be used to solve it. The arithmetic optimization algorithm (AOA) is a new optimization algorithm that can effectively solve both continuous and discrete optimization problems. However, it may suffer from poor exploration and premature convergence to sub-optimal solutions due to some problems with its exploration operators. In this paper, we propose a new method for the PASP using an improved AOA algorithm called the island-based AOA (iAOA). The new algorithm is based on a structured population model called the island model. This model distributes the population of candidate solutions among islands that periodically exchange some of the candidate solutions among each other based on a migration protocol. In iAOA, some modifications were applied to AOA's parameters to make it capable of handling discrete space. We evaluated iAOA using a public benchmark for the PASP and compared our results with those of other baseline algorithms (DFPA, SA, HS, GD-NLGD, HTS, TS, I-BBO, and MBBO). The simulation results revealed that iAOA achieved the minimum average execution time on 5 out of 6 instances of the public benchmark. It also showed that iAOA achieved the second minimum average violation of the objective function over 10 independent runs. Finally, the statistical examination of the experimental results using Friedman and Wilcoxon tests confirms the significance of the results of iAOA compared with the other baseline algorithms.

关键词： Patient admission scheduling problem arithmetic optimization algorithm Island-based Discrete optimization problems PASP benchmark

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