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AOAFS: A Malware Detection System Using an Improved arithmetic optimization algorithm

TECHNOLOGIES 2025年第4期13卷 145-145页

作者： Alshorman, Rafat Abed-alguni, Bilal H. Alqudah, Yaqeen E. Yarmouk Univ Dept Comp Sci Irbid 21110 Jordan

Malware detection datasets often contain a huge number of features, many of which are irrelevant, noisy, and duplicated. This issue diminishes the efficacy of Machine Learning models used for malware detection. Feature Selection (FS) is an approach commonly used to reduce the number of features in a malware detection dataset to a smaller set of features to facilitate the ease of the Machine Learning process. The arithmetic optimization algorithm (AOA) is a relatively new efficient optimization algorithm that can be used for FS. This work introduces a new malware detection system called the improved AOA method for FS (AOAFS) that enhances the performance of Machine Learning techniques for malware detection. The AOAFS contains three key enhancements. First, the K-means clustering method is used to improve the initial population of the AOAFS. Second, sixteen Binary Transfer Functions are applied to model the binary solution space for FS in the AOAFS. Finally, Dynamic Opposition-based Learning is utilized to improve the mutation capability of the AOAFS. Several malware datasets were used to compare the AOAFS to four popular Machine Learning algorithms and eight famous wrapper-based optimization algorithms. Statistically, the AOAFS was evaluated using the Friedman Test for ranking the tested algorithms, while the Wilcoxon Signed-Rank Test was employed for pairwise comparisons. The results indicated that the AOAFS achieves the highest classification accuracy with the smallest feature set across all datasets.

关键词： malware detection arithmetic optimization algorithm feature selection k-means clustering dynamic opposition-based learning

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Boosted arithmetic optimization algorithm with elite opposition-based pattern search mechanism and its promise to design microstrip patch antenna for WLAN and WiMAX

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INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION 2025年第1期45卷 264-279页

作者： Ozmen, Hueseyin Ekinci, Serdar Izci, Davut Dicle Univ Dept Elect & Elect Engn Diyarbakir Turkiye Batman Univ Dept Comp Engn Batman Turkiye Middle East Univ MEU Res Unit Amman Jordan

This paper investigates the performance of a novel artificial intelligence optimization technique in terms of designing a small size antenna that can be used for WLAN and WiMAX applications. In this regard, a boosted version of the arithmetic optimization algorithm is constructed as a novel artificial intelligence optimization technique with the aid of pattern search and elite opposition-based learning mechanisms. The proposed boosted arithmetic optimization algorithm is demonstrated for its superior explorative and exploitative behavior using classical fixed-dimensional, multimodal, and unimodal benchmark functions. The performance of the boosted arithmetic optimization algorithm is then presented for a real-world engineering optimization problem. For the latter challenge, a small size antenna that can be used for WLAN and WiMAX applications is designed. The obtained simulation results show that a compact small-sized patch antenna operating at WLAN and WiMAX frequencies can successfully be designed with the proposed boosted arithmetic optimization algorithm. Comparative evaluation against the state-of-the-art shows that efficiency is increased significantly since a bandwidth increase of up to 21% is achieved even with a more than 39% reduction in size.

关键词： arithmetic optimization algorithm elite opposition-based learning pattern search microstrip patch antenna WLAN and WiMAX applications

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Pattern Search Ameliorated arithmetic optimization algorithm for Engineering optimization and Infinite Impulse Response System Identification

ELECTRICA

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ELECTRICA 2024年第1期24卷 119-130页

作者： Ekinci, Serdar Izci, Davut Batman Univ Dept Comp Engn Batman Turkiye Middle East Univ MEU Res Unit Amman Jordan

In the field of science and engineering, significant attention can be observed recently for system identification as a complex optimization problem. As the infinite impulse response (IIR) models can achieve more accurate models of physical plants for real-world applications, they are mostly preferred over finite impulse response models. Despite the latter advantage of the IIR structures, it is not straightforward to minimize the related cost functions as they tend to generate multimodal error surfaces. Metaheuristic algorithms have already been shown for their excellent promise to deal with such difficulties as they operate independent of the nature of the problem. In this regard, this work aims to demonstrate the excellent promise of a novel developed metaheuristic algorithm named pattern search ameliorated arithmetic optimization algorithm. The proposed algorithm integrates the original form of the arithmetic optimization algorithm (for exploration) with the pattern search algorithm (for exploitation) such that a better-performing metaheuristic structure is achieved. The excellent ability of the proposed pattern search ameliorated arithmetic optimization algorithm is demonstrated against the original arithmetic optimization algorithm by using well-known classical benchmark functions and welded beam design problem. A significant improvement is achieved for benchmark functions, and an improvement of up to 25% is obtained for the optimal cost of the welded beam design. Then, different IIR model identification problems are considered, and a comparative assessment is performed using different metaheuristic optimization techniques: particle swarm optimization algorithm, artificial bee colony algorithm, electromagnetism-like optimization algorithm, cuckoo search algorithm, and flower pollination algorithm. The obtained statistical results from different systems confirm that the pattern search ameliorated arithmetic optimization algorithm can achieve better accuracy and r

关键词： arithmetic optimization algorithm digital IIR filters engineering optimization pattern search swarm-based optimization system identification

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Predicting Corporate Financial Failure Using Sigmoidal Opposition-Based arithmetic optimization algorithm

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COMPUTATIONAL ECONOMICS 2024年 1-53页

作者： Khaldi, Mohamed Manita, Ghaith Chhabra, Amit Guesmi, Ramzi Hamrouni, Tarek Univ Manouba ESEN Manouba Tunisia Univ Sousse Lab MARS ISITCom LR17ES05 Sousse Tunisia Guru Nanak Dev Univ Dept Comp Engn & Technol Amritsar 143005 India Univ Jendouba ISLAIB Beja Tunisia Tunis El Manar Univ Fac Sci Tunis LIPAH Univ Campus Tunis Tunisia

This paper concentrates on solving corporate financial failure prediction problems using a novel method. Corporate financial failure prediction is considered as a high complexity problem. It is hard to solve with traditional prediction algorithms. Notwithstanding, metaheuristics are aimed to solve these types of problems. Among them, is the arithmetic optimization algorithm (AOA), which is one of the newest metaheuristics that is characterized by its easy integration, usability and strong computational ability. It is estimated to be one of the most used metaheuristics. In this paper, we propose an improved version of it called Sigmoidal Opposition-based arithmetic optimization algorithm (SOAOA) in which the Opposition-based Learning is applied to improve the local searching capability and boost the intensification phase of the AOA. Whereas, the integration of the sigmoidal function enhances its diversification phase that results in better outcomes. The main purpose of this paper is to present our algorithm, which has proven to provide highly accurate results in predicting bankruptcy. In order to verify the latter, we have applied it to 50 well-known benchmarking functions to see how it deals with global optimization. Then we compared it with the most popular and exact Machine Learning algorithms such as Support Vector Machine (SVM) and Decision Trees (DT) to determine its accuracy in solving the formerly mentioned problem. SOAOA results are prominent in both global optimization and bankruptcy prediction tests. Based on the results, it has shown to be the best algorithm for solving the task evenly with DT.

关键词： Opposition-based learning arithmetic optimization algorithm Metaheuristics Machine learning Corporate financial failure prediction

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Hybrid arithmetic optimization algorithm for a new multi-warehouse joint replenishment and delivery problem under trade credit

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NEURAL COMPUTING & APPLICATIONS 2023年第10期35卷 7561-7580页

作者： Peng, Lu Wang, Lin Wang, Sirui Wuhan Univ Technol Sch Management Wuhan 430070 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China

Trade credit is a significant form of short-term financing in the real business situation. This study proposes a practical multi-warehouse joint replenishment and delivery (MJRD) problem under trade credit in accordance with the realistic situation. The goal of the MJRD is to find the reasonable basic replenishment cycle time, the joint replenishment frequency, the delivery frequency, and the assignment information of suppliers to minimize the total cost. Five intelligent algorithms, which include a differential evolution algorithm, genetic algorithm, adaptive hybrid differential evolution algorithm, arithmetic optimization algorithm (AOA), and hybrid arithmetic optimization algorithm (HAOA), are designed to find a solution to this MJRD problem under trade credit. The results of several experiments show that HAOA is effective in solving the proposed MJRD. Compared with AOA, the best improvement is 46.66%. HAOA is a satisfactory algorithm for the proposed MJRD under trade credit.

关键词： Joint replenishment and delivery problem Trade credit arithmetic optimization algorithm Differential evolution algorithm Genetic algorithm

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Advancements in arithmetic optimization algorithm: theoretical foundations, variants, and applications

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第13期83卷 37621-37664页

作者： Kumar, Vijay Dr B R Ambedkar Natl Inst Technol Dept Informat Technol Jalandhar India

arithmetic optimization algorithm (AOA) is a population-based metaheuristic algorithm that mimics the properties of primitive arithmetic operators. Researchers were attracted to AOA after its development in 2021. It has been extensively applied across several research domains. This study presents a thorough analysis of the AOA. The mathematical modelling of AOA and its inspiration are discussed. The variants of AOA namely improved, binary, chaotic, hybrid, and multi-objective are investigated in detail. The applications of AOA and its variants in numerous research domain are discussed. The possible research directions for AOA are examined. The young researchers will be assisted by this study in comprehending the fundamental ideas of AOA and in applying this knowledge to their own research issues.

关键词： arithmetic optimization algorithm Metaheuristic Population Exploration Exploitation

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Intelligent Intrusion Detection System using Enhanced arithmetic optimization algorithm with Deep Learning Model

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2023年第4期30卷 1217-1224页

作者： Kavitha, S. Maheswari, N. Uma Venkatesh, R. Velammal Coll Engn & Technol Dept Comp Sci & Engn Madurai India PSNA Coll Engn & Technol Dept Comp Sci & Engn Dindigul India PSNA Coll Engn & Technol Dept Informat Technol Dindigul India

The widespread use of interoperability and interconnectivity of computing systems is becoming indispensable for enhancing our day-to-day actions. The susceptibilities deem cyber-security systems necessary for assuming communication interchanges. Secure transmission needs security measures for combating the threats and required developments to security measures that counter evolving security risks. Though firewalls were devised to secure networks, in real-time they cannot detect intrusions. Hence, destructive cyber-attacks put forward severe security complexities, requiring reliable and adaptable intrusion detection systems (IDS) that could monitor unauthorized access, policy violations, and malicious activity practically. Conventional machine learning (ML) techniques were revealed for identifying data patterns and detecting cyber-attacks IDSs successfully. Currently, deep learning (DL) methods are useful for designing accurate and effective IDS methods. In this aspect, this study develops an intelligent IDS using enhanced arithmetic optimization algorithm with deep learning (IIDS-EAOADL) method. The presented IIDS-EAOADL model performs data standardization process to normalize the input data. Besides, equilibrium optimizer based feature selection (EOFS) approach is developed to elect an optimal subset of features. For intrusion detection, deep wavelet autoencoder (DWAE) classifier is applied. Since the proper tuning of parameters of the DWNN is highly important, EAOA algorithm is used to tune them. For assuring the simulation results of the IIDS-EAOADL technique, a widespread simulation analysis takes place using a benchmark dataset. The experimentation outcomes demonstrate the improvements of the IIDS-EAOADL model over other existing techniques

关键词： arithmetic optimization algorithm deep learning feature selection intrusion detection security

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Sand cat arithmetic optimization algorithm for global optimization engineering design problems

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第6期10卷 2122-2146页

作者： Chen, Shuilin Zheng, Jianguo Donghua Univ Glorious Sun Sch Business & Management Shanghai 200051 Peoples R China

Sand cat swarm optimization (SCSO) is a recently introduced popular swarm intelligence metaheuristic algorithm, which has two significant limitations - low convergence accuracy and the tendency to get stuck in local optima. To alleviate these issues, this paper proposes an improved SCSO based on the arithmetic optimization algorithm (AOA), the refracted opposition-based learning and crisscross strategy, called the sand cat arithmetic optimization algorithm (SC-AOA), which introduced AOA to balance the exploration and exploitation and reduce the possibility of falling into the local optimum, used crisscross strategy to enhance convergence accuracy. The effectiveness of SC-AOA is benchmarked on 10 benchmark functions, CEC 2014, CEC 2017, CEC 2022, and eight engineering problems. The results show that the SC-AOA has a competitive performance. Graphical Abstract

关键词： sand cat swarm optimization arithmetic optimization algorithm exploration and exploitation hybrid algorithms

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UAV path planning in mountain areas based on a hybrid parallel compact arithmetic optimization algorithm

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NEURAL COMPUTING & APPLICATIONS 2023年 1-16页

作者： Wang, Ruo-Bin Wang, Wei-Feng Geng, Fang-Dong Pan, Jeng-Shyang Chu, Shu-Chuan Xu, Lin North China Univ Technol Sch Informat Sci & Technol Beijing 100144 Peoples R China North China Univ Technol Beijing Urban Governance Res Ctr Beijing 100144 Peoples R China Univ South Australia STEM Adelaide 5095 Australia Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Flinders Univ S Australia Coll Sci & Engn 1284 South Rd Tonsley SA 5042 Australia

Unmanned Aerial Vehicle (UAV) path planning is one of the core components of its entire autonomous control system. The main challenge lies in efficiently obtaining an optimal flight route in complex environments, especially in mountain areas. To address this, we propose a novel version of arithmetic optimization algorithm (AOA), named parallel and compact AOA (PCAOA). In PCAOA, the compact technique can save the memory of UAV and shorten the calculation time, and the parallel technique can quicken the convergence speed and improve the solution accuracy. In addition, the flight path generated by PCAOA is smoothed with cubic B-spline curves, making the path suitable for a UAV. The performance of PCAOA is demonstrated on 23 benchmark functions. Experimental results show that PCAOA achieves competitive results. Finally, the simulation studies are conducted to verify that PCAOA can successfully acquire a feasible and effective route in different mountain areas.

关键词： UAV path planning Parallel technique Compact technique arithmetic optimization algorithm

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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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