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ESSENT: an arithmetic optimization algorithm with enhanced scatter search strategy for automated test case generation

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INFORMATION SCIENCES 2024年 656卷

作者： Li, Xiguang Feng, Baolu Sun, Yunhe Hawbani, Ammar Alsamhi, Saeed Hammod Zhao, Liang Shenyang Aerosp Univ Sch Comp Sci Shenyang Peoples R China Univ Galway Insight Ctr Data Analyt Galway Ireland

As one of the main research tasks in software testing, automated test case generation based on path coverage (ATCG-PC) aims to achieve maximum path coverage with a minimized set of test cases. In ATCG-PC, the correlation among the dimensions of test cases is widely utilized in academia to minimize the search efforts of the search-based algorithm. Nevertheless, the information related to target path selection is not utilized, which leads to blind decision-making by the search-based algorithm during the target path selection. Therefore, this paper proposes an enhanced scatter search strategy by using opposition-based learning. An arithmetic optimization algorithm is also proposed to solve ATCG-PC based on the enhanced scatter search strategy, namely, ESSENT. The ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path, and generates new test cases that cover the target path by modifying the dimensions of the existing test cases. The performance of the ESSENT algorithm is evaluated on six iFogSim subprograms and six Stanford coreNLP subprograms. Experiment results show that the ESSENT algorithm achieves a higher convergence rate than other state-of-the-art algorithms. Furthermore, it enables maximum path coverage with fewer test cases.

关键词： Automated test case generation Path coverage arithmetic optimization algorithm Path entropy

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An arithmetic optimization algorithm with balanced diversity and convergence for multimodal multiobjective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 91卷

作者： Zhang, Ruyue Mao, Shuhua Zhao, Shangrui Liu, Chang Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China Yantai Huang Bohai New Area Nat Resources & Planni Yantai 265500 Peoples R China

Multimodal multiobjective optimization problems are widely prevalent in real life. Addressing these challenges is crucial as they directly impact the efficiency and effectiveness of solutions across various domains. This paper proposes a novel Multi-Modal Multi-Objective arithmetic optimization algorithm (MMOP-AOA), aimed at achieving a high balance between diversity and convergence in both decision and objective spaces. arithmetic optimization algorithm (AOA) is a highly competitive metaheuristic optimization algorithm with strong exploration and exploitation capabilities. MMOP-AOA extends the AOA for the first time to solve multimodal multiobjective problems, with the following ideas: Firstly, a new exploration and exploitation strategy (NBC-NEE) is designed based on the characteristics of *** strategy utilizes Neighborhood-Based Clustering (NBC) to partition the decision space into multiple clusters, aiding MMOP-AOA in capturing more equivalent Pareto subsets (ePSs). Secondly, a convergence and diversity balance mechanism (CDBM) is developed. This mechanism involves comparing the convergence indicator and diversity indicator to select different mutation strategies. Thirdly, an improved crowding distance (ICD) is proposed to address the deficiencies of existing special crowding distance measures. The effectiveness of CDBM and ICD is demonstrated in the paper through experiments on 22 benchmark functions from CEC-2019 and a real-world problem of signal timing optimization at road intersections. The research also reveals that compared to four other advanced multimodal multiobjective optimization algorithms, MMOP-AOA exhibits superior search capability and stability. Furthermore, MMOP-AOA utilizes Neighborhood-Based Clustering (NBC) to partition the decision space into multiple clusters, aiding MMOP-AOA in capturing more equivalent Pareto subsets (ePSs) and provides a theoretical framework for other metaheuristic optimization algorithms to tackle multimodal multi

关键词： Multimodal optimization arithmetic optimization algorithm Multiobjective optimization Metaheuristic optimization algorithm

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Oppositional arithmetic optimization algorithm for network reconfiguration and simultaneous placement of DG and capacitor in radial distribution networks

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2024年第5期37卷

作者： Dey, Indrajit Roy, Provas Kumar Camellia Inst Technol & Management Dept Elect Engn Bainchi West Bengal India Kalyani Govt Engn Coll Dept Elect Engn Kalyani India

The prime objective of this study is the simultaneous network reconfiguration with distributed generation (DG) and capacitor placement in radial distribution networks (RDN) to get the techno and economic benefits for two separate objectives, which are the minimization of actual power loss and annual economic loss as well as a multi objective combining these two single objectives using an oppositional arithmetic optimization algorithm (OAOA). It is an improved version of the currently suggested arithmetic optimization algorithm (AOA) used in the field of engineering for the optimization task. Though the recently developed AOA shows its efficacy in different optimization tasks but to improve the quality of solutions, convergence behavior, and to avoid the local optima, oppositional behavior is added to AOA. The efficacy and exactness of OAOA are tested on three test systems (33-bus, 69-bus, and 118-bus). For the reduction of power loss and annual economic loss as well as the multi objective optimization, two scenarios with different cases are executed using OAOA in RDNs. In scenario 1, the installation of the capacitor (case 1), the installation of unity power factor (UPF) based DG (case 2), and the placement of optimal power factor (OPF) based DG (case 3) have been executed. In scenario 2, allocation of UPF based DG and capacitors simultaneously (case 1), placement of OPF based DG and capacitors simultaneously (case 2) and simultaneous reconfiguration with installation of OPF based DG and capacitor (case 3) has been executed. This recommended OAOA algorithm provides the percentage improvement in real power loss and yearly economic loss for all cases of 33-bus, and 69-bus systems (34.28%, 65.50%, 94.43%, 93.26%, 94.89%, and 95.11%), (28.54%, 56.69%, 83.42%, 79.62%, 83.65%, and 83.71%), and (35.51%, 69.16%, 98.10%, 97.52%, 98.22%, and 98.25%), (30.26%, 61.68%, 88.75%, 85.42%, 88.81%, and 88.98%), respectively. The results and comparative study reveal that the OAOA is b

关键词： arithmetic optimization algorithm decremented power loss distribution system reconfiguration economic benefit oppositional arithmetic optimization algorithm optimum position and capacity of DG and capacitor

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Optimizing deep neuro-fuzzy classifier with a novel evolutionary arithmetic optimization algorithm

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JOURNAL OF COMPUTATIONAL SCIENCE 2022年第0期64卷

作者： Talpur, Noureen Abdulkadir, Said Jadid Alhussian, Hitham Hasan, Mohd Hilmi Abdullah, Mohd Hafizul Afifi Univ Teknol PETRONAS Ctr Res Data Sci CeRDaS Comp Informat Sci Dept Seri Iskandar Perak Malaysia

Deep Neuro-Fuzzy System has been successfully employed in various applications. But, the model faces two issues: (i) dataset with many features exponentially increases the fuzzy rule-base, (ii) parameters in the fuzzy rule-base are optimized using the gradient descent approach, which has the drawback of local minima. Therefore, this study aims on improving the model's accuracy by proposing arithmetic optimization algorithm. The outcomes using the arithmetic optimization algorithm for feature selection have not only reduced the burden of implementing a huge dataset, but the arithmetic optimization-based deep neuro-fuzzy system has outperformed with 95.14% accuracy compared to the standard method with 94.52%.

关键词： Deep neural network Deep neuro-fuzzy system arithmetic optimization algorithm Metaheuristics Classification problems

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optimization of Cash Management at ATMs Using Fuzzy AHP Weighted arithmetic optimization algorithm

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JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING 2023年第1-2期41卷 21-37页

作者： Tunc, Ali Tasdemir, Sakir Architecht Informat Syst Istanbul Turkiye Selcuk Univ Fac Technol Dept Comp Engn Konya Turkiye

Cash management is a set of procedures designed to keep the quantity of money required in branches and ATMs at an optimal level. Cash Management optimization for ATM studies tries to retain the most suitable quantity of cash in ATMs based on the types and models of ATMs. ATM cash management is seen as an optimization issue in this study, and the values of the decision variables are computed to reduce cash amounts and operational expenses. The study's primary goal is to estimate the most suitable quantity of proper banknotes to be found at ATMs on any given day and to offer recommendations to system owners on how to better manage the amount of cash available at ATMs. In the literature, metaheuristic multiobjective optimization algorithms are frequently used. In this investigation, one of these algorithms was used to try to get the best possible outcome. First and foremost, a thorough investigation was conducted to discover the decision factors and key features of ATM data. It was determined to create results using the optimization method for the stated data criteria, which is a novel technique in the literature. One of the newly proposed metaheuristic algorithms, the arithmetic optimization algorithm (AOA), was chosen as the optimization algorithm to find the solution. The fuzzy analytical hierarchy process (Fuzzy AHP) algorithm was utilized to determine the weighting coefficients of the variables given as AOA algorithm input parameters. The Fuzzy AHP algorithm was used to weigh the influence of the features on the output. The proposed study aims to achieve an optimal value using the obtained weight information in the AOA method as input. The most acceptable quantity of monetary worth is provided based on the answer discovered as a consequence of this investigation. It is hoped that this strategy will result in a successful outcome. The outcomes were compared to the real values, and the success rates were attempted to be disclosed.

关键词： AHP ATM AOA arithmetic optimization algorithm cash management optimization

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Design of novel UPFC based damping controller for solar PV integrated power system using arithmetic optimization algorithm

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2024年

作者： Bohidar, Sankalpa Mallick, Ranjan Kumar Nayak, Pravati Mishra, Sairam Nahak, Narayan Panda, Gayadhar Gouda, Pramod Kumar SOA Univ Dept Elect & Elect Engn Bhubaneswar 751030 Odisha India SOA Univ Dept Elect Engn Bhubaneswar 751030 Odisha India NIT Meghalaya Dept Elect & Elect Engn Shillong 793003 India Aditya Inst Technol & Management Elect & Elect Engn Tekkal 532201 Andhra Pradesh India

Integrating renewable energy sources like solar power into traditional power systems poses challenges. One such challenge is the effect of renewable power plants, which use power electronics, on the grid's stability. Specifically, these plants can impact small-signal stability by either damping or exacerbating low-frequency oscillations. This paper introduces a novel Unified Power Flow Controller (UPFC) based damping controller specifically designed for Solar Photovoltaic (PV) integrated power systems. It employs an arithmetic optimization algorithm (AOA) to optimize the UPFC damping controller parameters and mitigate low-frequency oscillations in the power system. The objective function minimizes the Integral Time Absolute Error (ITAE) of speed deviations under varying loading conditions. The proposed technique is utilized simultaneously to control the modulation index of series and phase angle of shunt converters of UPFC. The MATLAB/simulation results obtained effectively from the proposed technique which is actualized and identify both detrimental and beneficial impacts of increased PV penetration for small signal stability performance. The study reveals both the small-signal stability of the system and its response to large disturbances that alter the active power balance and frequency stability. The results of the analysis demonstrated with single and multimachine environment by comparing with the other optimizations like PSO, DE, DE-PSO and GWO, the proposed one is effective for damping out the oscillations. The effectiveness of the proposed damping controller is further confirmed through real-time validation using the OPAL-RT setup.

关键词： arithmetic optimization algorithm unified power flow controller damping controller solar PV

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Intrusion detection system for large-scale IoT NetFlow networks using machine learning with modified arithmetic optimization algorithm

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INTERNET OF THINGS 2023年 22卷

作者： Fraihat, Salam Makhadmeh, Sharif Awad, Mohammed Al-Betar, Mohammed Azmi Al-Redhaei, Anessa Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC POB 346 Ajman U Arab Emirates Amer Univ Ras Al Khaimah Dept Comp Sci & Engn POB 72603 Ras Al Khaymah U Arab Emirates

With the rapid expansion of Internet of Things (IoT) networks, the need for robust security measures to detect and report potential threats is becoming more urgent. In this paper, we propose a Network Intrusion Detection System (NIDS), as a security measure, for large-scale IoT NetFlow-based networks. The proposed NIDS employs Machine Learning (ML) boosted by a modified version of the arithmetic optimization algorithm (AOA) to determine the most suitable set of features. The selected seven features are used to train several ML models, including Random Forest and Extra Trees. Our research utilized four large datasets, released in 2021, containing IoT traffic data represented in a standard set of 43 NetFlow-based features. Reducing the number of features from 43 to 7 enhanced the prediction time and, consequently, the performance in the real world. Interestingly, the proposed NIDS exhibited a very accurate and robust detection model for IoT NetFlow data, which can be generalized for other Intrusion Detection datasets. Our proposed NIDS achieved up to 99% and 98% accuracy for binary and multi-classification, respectively. These scores were similar to those achieved by the state-of-the-art systems despite decreasing the number of utilized features by up to 84%.

关键词： Intrusion detection system NetFlow Internet of Things arithmetic optimization algorithm Machine learning Prediction Classification Feature selection optimization

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Enhanced multimodal medical image fusion via modified DWT with arithmetic optimization algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-15页

作者： Alzahrani, Ahmad A. King Abdulaziz Univ Fac Comp & Informat Technol Jeddah 21589 Saudi Arabia

Medical image fusion (MIF) techniques are proficient in combining medical images in distinct morphologies to obtain a reliable medical analysis. A single modality image could not offer adequate data for an accurate analysis. Therefore, a novel multimodal MIF-based artificial intelligence (AI) method has been presented. MIF approaches fuse multimodal medical images for exact and reliable medical recognition. Multimodal MIF improves diagnostic accuracy and clinical decision-making by combining complementary data in different imaging modalities. This article presents a new multimodal medical image fusion model utilizing Modified DWT with an arithmetic optimization algorithm (MMIF-MDWTAOA) approach. The MMIF-MDWTAOA approach aims to generate a fused image with the significant details and features from each modality, leading to an elaborated depiction for precise interpretation by medical experts. The bilateral filtering (BF) approach is primarily employed for noise elimination. Next, the image decomposition process uses a modified discrete wavelet transform (MDWT) approach. However, the approximation coefficient of modality_1 and the detailed coefficient of modality_2 can be fused interchangeably. Furthermore, a fusion rule is derived from combining the multimodality data, and the AOA model is enforced to ensure the optimum selection of the fusion rule parameters. A sequence of simulations is accomplished to validate the enhanced output of the MMIF-MDWTAOA technique. The investigational validation of the MMIF-MDWTAOA technique showed the highest entropy values of 7.568 and 7.741 bits/pixel over other approaches.

关键词： Medical images Multimodality Image fusion arithmetic optimization algorithm Fusion rule

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A Novel Hybrid Crow Search arithmetic optimization algorithm for Solving Weighted Combined Economic Emission Dispatch with Load-Shifting Practice

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algorithmS 2024年第7期17卷 313页

作者： Dey, Bishwajit Sharma, Gulshan Bokoro, Pitshou N. Univ Johannesburg Dept Elect Engn Technol ZA-2006 Johannesburg South Africa

The crow search arithmetic optimization algorithm (CSAOA) method is introduced in this article as a novel hybrid optimization technique. This proposed strategy is a population-based metaheuristic method inspired by crows' food-hiding techniques and merged with a recently created simple yet robust arithmetic optimization algorithm (AOA). The proposed method's performance and superiority over other existing methods is evaluated using six benchmark functions that are unimodal and multimodal in nature, and real-time optimization problems related to power systems, such as the weighted dynamic economic emission dispatch (DEED) problem. A load-shifting mechanism is also implemented, which reduces the system's generation cost even further. An extensive technical study is carried out to compare the weighted DEED to the penalty factor-based DEED and arrive at a superior compromise option. The effects of CO2, SO2, and NOx are studied independently to determine their impact on system emissions. In addition, the weights are modified from 0.1 to 0.9, and the effects on generating cost and emission are investigated. Nonparametric statistical analysis asserts that the proposed CSAOA is superior and robust.

关键词： combined economic emission dispatch (CEED) load shifting demand side management crow search algorithm arithmetic optimization algorithm

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Predicting soil moisture content of tea plantation using support vector machine optimized by arithmetic optimization algorithm

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JOURNAL OF algorithmS & COMPUTATIONAL TECHNOLOGY 2023年 17卷

作者： Yin, Dongxiao Wang, Yanhua Huang, Ying Luoding Polytech Shenzhen Guangdong Peoples R China Liuzhou Railway Vocat Tech Coll Intelligent Mfg Coll Liuzhou Peoples R China Liuzhou Railway Vocat Tech Coll Commun & Signal Coll Liuzhou Peoples R China Liuzhou Railway Vocat & Tech Coll Commun & Signal Coll 2 Wenyuan Rd Liuzhou 545616 Guangxi Peoples R China

Soil moisture content (SMC) is an important parameter that affects tea growth. Reasonable soil moisture content improves tea quality and ensures tea yield. Therefore, it is necessary to regularly monitor the soil water content. However, the traditional soil moisture content prediction algorithm has the problems of low accuracy and low efficiency. This paper constructs and evaluates the performance of a hybrid arithmetic optimization algorithm (AOA) and support vector machine (SVM) prediction model (AOA-SVM) for predicting SMC in tea plantations. Grey relation analysis (GRA) and Pearson correlation analysis are adopted to select features for soil moisture content prediction model, and then the correlation of SMC with soil temperature (ST), atmospheric temperature (AT), and soil electrical conductivity (SEC) was analyzed. The optimal penalty parameter (c) and the parameter of the kernel function (g) of SVM model are determined by AOA. The mean square error (MSE) and coefficient of determination (R-2), mean absolute error (MAE), and mean error (ME) were calculated to evaluate the performance of the model. Meanwhile, the performance of AOA-SVM model is compared with SVM optimized by the sparrow search algorithm (SSA-SVM), extreme learning machine (ELM), support vector machine (SVM), and convolutional neural networks (CNN). The results showed that AOA-SVM, SSA-SVM and CNN are better, with the coefficient of determination above 93%, the coefficient of determination of SVM and ELM is 81.69% and 89.61%, respectively. The AOA-SVM model has the best R-2 of 95.03%. This indicates that the AOA-SVM model has significant performance with higher R-2, smaller MSE than other models, which has potential implications in precision agriculture.

关键词： Precision irrigation soil moisture content arithmetic optimization algorithm support vector machine normal distribution

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