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dwarf mongoose optimization algorithm

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022年 391卷 114570-114570页

作者： Agushaka, Jeffrey O. Ezugwu, Absalom E. Abualigah, Laith Univ KwaZulu Natal Sch Math Stat & Comp Sci King Edward Rd ZA-3201 Kwa Zulu South Africa Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

This paper proposes a new metaheuristic algorithm called dwarf mongoose optimization algorithm (DMO) to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The DMO mimics the foraging behavior of the dwarf mongoose. The restrictive mode of prey capture (feeding) has dramatically affected the mongooses' social behavior and ecological adaptations to compensate for efficient family nutrition. The compensatory behavioral adaptations of the mongoose are prey size, space utilization, group size, and food provisioning. Three social groups of the dwarf mongoose are used in the proposed algorithm, the alpha group, babysitters, and the scout group. The family forage as a unit, and the alpha female initiates foraging, determines the foraging path, the distance covered, and the sleeping mounds. A certain number of the mongoose population (usually a mixture of males and females) serve as the babysitters. They remain with the young until the group returns at midday or evening. The babysitters are exchanged for the first to forage with the group (exploitation phase). The dwarf mongooses do not build a nest for their young;they move them from one sleeping mound to another and do not return to the previously foraged site. The dwarf mongoose has adopted a seminomadic way of life in a territory large enough to support the entire group (exploration phase). The nomadic behavior prevents overexploitation of a particular area. It also ensures exploration of the whole territory because no previously visited sleeping mound is returned. The performance of the proposed DMO algorithm is compared with seven other algorithms to show its effectiveness in terms of different performance metrics and statistics. In most cases, the near-optimal solutions achieved by the DMO are better than the best solutions obtained by the current state-of-the-art algorithms. Matlab codes of DMO are available at https://*** ***/matlabcentral/fi

关键词： dwarf mongoose optimization algorithm Metaheuristic Nature-inspired algorithms Global optimization Engineering design problems

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Improved dwarf mongoose optimization algorithm based on hybrid strategy for global optimization and engineering problems

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JOURNAL OF SUPERCOMPUTING 2025年第3期81卷 1-61页

作者： He, Fuchun Fu, Chunming He, Youwei Huo, Shaoyong Tang, Jiachang Long, Xiangyun Univ South China Sch Mech Engn Hengyang City 421001 Peoples R China Hunan Univ Technol Dept Mech Engn Zhuzhou City 412007 Peoples R China Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China

The dwarf mongoose optimization algorithm is a metaheuristic approach designed to solve single-objective optimization problems. However, DMO has certain limitations, including slow convergence rates and a tendency to get stuck in local optima, particularly when applied to multimodal and combinatorial problems. This paper introduces an enhanced version of the DMO, referred to as HDMO, which is based on a hybrid strategy. Firstly, a sine chaotic mapping function is integrated to enhance the diversity of the initial population. Secondly, the study aims to improve the algorithm's performance through the integration of nonlinear control, adaptive parameter tuning, hybrid mutation strategies, and refined exploration-exploitation mechanisms. To evaluate the performance of the proposed HDMO, we conducted tests on the CEC2017, CEC2020, and CEC2022 benchmark problems, as well as 19 engineering design problems from the CEC2020 real-world optimization suite. The HDMO algorithm was compared with various algorithms, including (1) highly cited algorithms such as PSO, GWO, WOA and SSA;(2) recently proposed advanced algorithms, namely, BOA, GBO, HHO, SMA and STOA;and (3) high-performance algorithms like LSHADE and LSHADE_SPACMA. Experimental results demonstrate that, compared to other algorithms, HDMO exhibits superior convergence speed and accuracy. Wilcoxon rank-sum test statistics confirm the significant performance improvement of HDMO, highlight its potential in practical engineering optimization and design problems.

关键词： Metaheuristics dwarf mongoose optimization algorithm Chaos theory Engineering design problems

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Improved dwarf mongoose optimization algorithm for Feature Selection:Application in Software Fault Prediction Datasets

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Journal of Bionic Engineering 2024年第4期21卷 2000-2033页

作者： Abdelaziz I.Hammouri Mohammed A.Awadallah Malik Sh.Braik Mohammed Azmi Al-Betar Majdi Beseiso Department of Computer Science Al-Balqa Applied UniversityAl-Salt19117Jordan Department of Scientific Research and Graduate Studies University of Prince Mugrin42241MedinaSaudi Arabia Department of Computer Science Al-Aqsa UniversityGaza4051Palestine Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Information Technology Al-Huson University CollegeAl-Balqa Applied UniversityAl-HusonIrbid21110Jordan

Feature selection(FS)plays a crucial role in pre-processing machine learning datasets,as it eliminates redundant features to improve classification accuracy and reduce computational *** paper presents an enhanced approach to FS for software fault prediction,specifically by enhancing the binary dwarf mongoose optimization(BDMO)algorithm with a crossover mechanism and a modified positioning updating *** proposed approach,termed iBDMOcr,aims to fortify exploration capability,promote population diversity,and lastly improve the wrapper-based FS process for software fault prediction *** gained superb performance compared to other well-esteemed optimization methods across 17 benchmark *** ranked first in 11 out of 17 datasets in terms of average classification ***,iBDMOcr outperformed other methods in terms of average fitness values and number of selected features across all *** findings demonstrate the effectiveness of iBDMOcr in addressing FS problems in software fault prediction,leading to more accurate and efficient models.

关键词： dwarf mongoose optimization algorithm optimization Feature selection Classification

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Enhanced dwarf mongoose optimization algorithm with deep learning-based attack detection for drones

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ALEXANDRIA ENGINEERING JOURNAL 2024年 93卷 59-66页

作者： Alsariera, Yazan A. Awwad, Waleed Fayez Algarni, Abeer D. Elmannai, Hela Gamarra, Margarita Escorcia-Gutierrez, Jose Northern Border Univ Coll Sci Dept Comp Sci Ar Ar Saudi Arabia Tafila Tech Univ Coll Informat & Commun Technol Dept Comp Sci Tafila 66110 Jordan Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Univ Norte Dept Syst Engn Barranquilla 081007 Colombia Univ Costa Dept Computat Sci & Elect CUC Barranquilla 080002 Colombia

Security in smart cities is a challenging issue in urban environments as they depend upon interconnected technologies and data for effective services. To address security challenges, smart cities implement robust cybersecurity measures, including network monitoring, encryption, and intrusion detection systems. Detecting and mitigating possible security risks in drone network B5G is a crucial aspect of ensuring reliable and safe drone operation. It is necessary to establish sophisticated and robust attack detection techniques to defend against security threats as the use of drones becomes increasingly widespread and their applications diversify. This is due to the lack of privacy and security consideration in the drone's system, including an inadequate computation capability and unsecured wireless channels to perform advanced cryptographic algorithms. Intrusion detection systems (IDS) and anomaly detection systems can identify suspicious activities and monitor network traffic, such as anomalous communication patterns or unauthorized access attempts. Therefore, the study presents an enhanced dwarf mongoose optimization algorithm with deep learning-based attack detection (EDMOA-DLAD) in Networks B5G for the purpose of Drones technique. The presented EDMOA-DLAD technique aims to recognize the attacks and classifies them on the drone network B5G. Primarily, the EDMOA-DLAD technique designs a feature selection (FS) approach using EDMOA. To detect attacks, the EDMOA-DLAD technique uses a deep variational autoencoder (DVAE) classifier. Finally, the EDMOA-DLAD technique applies the beetle antenna search (BAS) technique for the optimum hyperparameter part of DVAE model. The outcome of EDMOA-DLAD approach can be verified on benchmark datasets. A wide range of simulations inferred that the EDMOA-DLAD method obtains enhanced performance of 99.79% over other classification techniques.

关键词： Security Smart cities attacks Deep learning dwarf mongoose optimization algorithm

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MULTI-head self-attention-based recurrent neural network with dwarf mongoose optimization algorithm-espoused QRS detector design

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第5期18卷 4935-4944页

作者： Malathi, S. R. Kumar, P. Vijay Sri Venkateswara Coll Engn Dept Elect & Commun Engn Chennai Tamil Nadu India Indian Inst Management Calcutta Dept Business Analyt C7VX VVVDiamond Harbour Rd Kolkata 700104 W Bengal India

QRS detection is the essential electrocardiogram (ECG) analysis procedure. A reliable QRS recognition system that achieves high accuracy despite a typical QRS morphologies and significant noise is required for ECG data gathered by wearable devices. Most contemporary systems seek fast execution durations and minimal energy consumption while attaining high prediction rates. To minimize these issues, this research article presents an approach of multi-head self-attention-based recurrent neural network with dwarf mongoose optimization algorithm-espoused QRS detector design (MHSARNN-QRS) is proposed. Initially, ECG data are taken from MIT/BIH arrhythmia dataset (MIT-AD). Every disintegrated signal is transmitted to multi-head self-attention-based recurrent neural network (MHSARNN) to examine morphologies and predict QRS like correct and incorrect. Then, the QRS wave is located through dwarf mongoose optimization algorithm by reducing probability of neglected identification improves the detection performance. The performance of proposed MHSARNN-QRS method is evaluated using accuracy, sensitivity, specificity, detection error rate, computation time, f1-score, positive prediction, and time for processing a single record (s) and single beat (ms) are analyzed. Performance of the MHSARNN-QRS approach attains high sensitivity, lower single record, lower single beat, and greater accuracy compared with existing methods.

关键词： dwarf mongoose optimization algorithm Master-slave adaptive notch filter MIT/BIH arrhythmia dataset Multi-head self-attention-based recurrent neural network

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Improved prairie dog optimization algorithm by dwarf mongoose optimization algorithm for optimization problems

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第11期83卷 32613-32653页

作者： Abualigah, Laith Oliva, Diego Jia, Heming Gul, Faiza Khodadadi, Nima Hussien, Abdelazim G. Al Shinwan, Mohammad Ezugwu, Absalom E. Abuhaija, Belal Abu Zitar, Raed Al Al Bayt Univ Prince Hussein Bin Abdullah Fac Informat Technol Dept Comp Sci Mafraq 25113 Jordan Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Lebanese Amer Univ Dept Elect & Comp Engn Byblos 135053 Lebanon Middle East Univ MEU Res Unit Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Sunway Univ Malaysia Sch Engn & Technol Petaling Jaya 27500 Malaysia Univ Guadalajara Depto Innovac Basada Informac & El Conocimiento CUCEI Guadalajara Jal Mexico Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China Air Univ Dept Elect Engn Aerosp & Avion Campus Kamra Kamra Pakistan Florida Int Univ Dept Civil & Environm Engn Miami FL 33199 USA Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden Fayoum Univ Fac Sci Faiyum Egypt Appl Sci Private Univ Fac Informat Technol Amman 11931 Jordan Northwest Univ Unit Data Sci & Comp 11 Hoffman St ZA-2520 Potchefstroom South Africa Wenzhou Kean Univ Dept Comp Sci Wenzhou 325015 Peoples R China Sorbonne Univ Abu Dhabi Sorbonne Ctr Artificial Intelligence Abu Dhabi 38044 U Arab Emirates

Recently, optimization problems have been revised in many domains, and they need powerful search methods to address them. In this paper, a novel hybrid optimization algorithm is proposed to solve various benchmark functions, which is called IPDOA. The proposed method is based on enhancing the search process of the Prairie Dog optimization algorithm (PDOA) by using the primary updating mechanism of the dwarf mongoose optimization algorithm (DMOA). The main aim of the proposed IPDOA is to avoid the main weaknesses of the original methods;these weaknesses are poor convergence ability, the imbalance between the search process, and premature convergence. Experiments are conducted on 23 standard benchmark functions, and the results are compared with similar methods from the literature. The results are recorded in terms of the best, worst, and average fitness function, showing that the proposed method is more vital to deal with various problems than other methods.

关键词： Prairie dog optimization algorithm dwarf mongoose optimization algorithm Meta-heuristics Benchmark functions optimization problems

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Improved dwarf mongoose optimization algorithm using novel nonlinear control and exploration strategies

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期233卷

作者： Fu, Shengwei Huang, Haisong Ma, Chi Wei, Jianan Li, Yiting Fu, Youfa Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Guizhou Peoples R China Chongqing Vocat & Tech Univ Mechatron Informat Engn Inst Chongqing 402760 Peoples R China GuiZhou Univ Finance & Econ Coll Big Data Stat Guiyang 550025 Peoples R China Guizhou Equipment Mfg Digital Workshop Modeling & Guiyang 550025 Guizhou Peoples R China

The dwarf mongoose optimization algorithm (DMO) is a popular metaheuristic algorithm utilized to solve real-world problems. However, DMO has limitations such as slow convergence rate, susceptibility to local optima, and poor performance in high-dimensional problems. Aiming at the defects of the DMO, we propose an improved version called IDMO. This paper introduces the best leader and proposes a novel nonlinear control strategy based on the sine function, significantly enhancing the convergence rate while ensuring accuracy. Moreover, we propose a novel exploration strategy to solve the global optimization and high-dimensional problem. To demonstrate the performance of the proposed IDMO, we test on 65 test functions, including CEC2017, CEC2020, CEC2022, and CEC2013 large-scale global optimization. IDMO is compared with three classes of widely recognized algorithms: (1) highly-cited algorithms, namely, GSA, GWO, WOA, and HHO, (2) advanced algo-rithms, such as CPSOGSA, CSA, AVOA, and SO, (3) high-performance optimizers including L-SHADE, AL-SHADE, LSHADE-SPACMA, and LSHADE-cnEpSin. Moreover, we also compare with the original DMO and variants such as GDNNMOA, CO-DWO, and BDMOSAO. Meanwhile, we apply IDMO to solve 19 engineering design problems from the CEC2020 real-world optimization suite. Experimental results demonstrate that IDMO outperforms other algorithms, exhibiting excellent convergence rate, stability, and accuracy. The effectiveness of IDMO is confirmed by the Friedman mean ranking, showcasing its potential for metaheuristic optimization and real-world engineering design problems.

关键词： dwarf mongoose optimization algorithm High -dimensional problems Metaheuristics Exploration strategy Engineering design problems

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dwarf mongoose gannet optimization algorithm-enabled deep neuro-fuzzy network for detection of shockable ventricular cardiac arrhythmias

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2024年第13期238卷 6314-6330页

作者： Kavya, Lakkakula Karuna, Yepuganti Vellore Inst Technol Sch Elect Engn Vellore Tamil Nadu India Vellore Inst Technol Sch Elect Engn Vellore 632014 Tamil Nadu India

One of the main sources of the Sudden Cardiac Death (SCD) is termed as Fatal arrhythmia. The electric shock treatment retrieves the regular electrical and mechanical functions of the heartbeat by controlling Ventricular fibrillation (VF) and Ventricular Tachycardia (VT). Shockable arrhythmia is easily controlled by providing electrical shock treatments. On the other hand, non-shockable arrhythmia is not controlled by electrical shock treatment. It is a very complex task to accurately discriminate these two kinds of arrhythmia using human assessment of Electrocardiogram (ECG) signals within a limited span and there may be a chance to occur faults during manual inspection. An accurate ECG diagnosis is very significant as it saves the life of the patient in advance by delivering proper therapy. To address this emerging problem, an automated model using the proposed dwarf mongoose Gannet optimization algorithm-Deep Neuro-Fuzzy Network (DMGOA-DNFN) is invented for detecting the shockable ventricular cardiac arrhythmias (SVCA). The classification is performed effectively utilizing DNFN and the weight of this classifier is optimally adjusted employing a newly developed algorithm named DMGOA, which is a consolidation of dwarf mongoose optimization (DMO) and Gannet optimization algorithm (GOA). The proposed DMGOA-DNFN has surpassed other classical models with respect to accuracy of 93.2%, sensitivity of 95.8%, and specificity of 91.7%.

关键词： Electrocardiogram signal shockable ventricular cardiac arrhythmias dwarf mongoose optimization algorithm deep neuro-fuzzy network gannet optimization algorithm

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Enhanced Multi-Object dwarf mongoose algorithm for optimization Stochastic Data Fusion Wireless Sensor Network Deployment

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Computer Modeling in Engineering & Sciences 2025年第2期142卷 1955-1994页

作者： Shumin Li Qifang Luo Yongquan Zhou College of Artificial Intelligence Guangxi University for NationalitiesNanning530006China Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis Nanning530006China

Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic ***,the current research on wireless sensor network deployment problems uses overly simplistic models,and there is a significant gap between the research results and actual wireless sensor *** scholars have now modeled data fusion networks to make them more suitable for practical *** paper will explore the deployment problem of a stochastic data fusion wireless sensor network(SDFWSN),a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information *** deployment problem of SDFWSN is modeled as a multi-objective optimization *** network life cycle,spatiotemporal coverage,detection rate,and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network *** paper proposes an enhanced multi-objective mongoose optimization algorithm(EMODMOA)to solve the deployment problem of ***,to overcome the shortcomings of the DMOA algorithm,such as its low convergence and tendency to get stuck in a local optimum,an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA *** EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor(KNN)*** verify the effectiveness of the proposed algorithm,the EMODMOA algorithm was tested at CEC 2020 and achieved good *** the SDFWSN deployment problem,the algorithm was compared with the Non-dominated Sorting Genetic algorithm II(NSGAII),Multiple Objective Particle Swarm optimization(MOPSO),Multi-Objective Evolutionary algorithm based on Decomposition(MOEA/D),and Multi-Objective Grey Wolf Optimizer(MOGWO).By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objec

关键词： Stochastic data fusion wireless sensor networks network deployment spatiotemporal coverage dwarf mongoose optimization algorithm multi-objective optimization

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Combinatorial test case prioritization using hybrid Energy Valley dwarf mongoose optimization approach

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 271卷

作者： Kanagaraj, Kamaraj Nithiyanandam, Prasath Sekar, Saradha Shanmugam, Sangeetha KPR Inst Engn & Technol Dept Comp Sci & Engn Coimbatore 641010 Tamil Nadu India SRM Inst Sci & Technol Dept Networking & Commun Chennai 603203 Tamil Nadu India Sri Eshwar Coll Engn Dept Comp Sci & Engn Coimbatore 642102 Tamil Nadu India Karpagam Coll Engn Dept Comp Sci & Engn Coimbatore 641032 Tamil Nadu India

Combinatorial Test Case Prioritization is a technique used in software testing to improve the efficiency and effectiveness of test suites. It involves selecting and ordering test cases based on their ability to detect faults, especially those caused by interactions between multiple parameters. Artificial Intelligence (AI) has made significant contributions to Combinatorial Test Case Prioritization (TCP) by introducing advanced techniques to enhance the efficiency of the testing process. However, managing dependencies between test cases and adjusting the prioritization accordingly can be complex and time-consuming in most of the previous techniques. Therefore, an Energy Valley dwarf mongoose optimization algorithm (EVDMOA) is devised for Combinatorial TCP. Initially, the software programs are collected from the dataset. Then, test case generation is performed to create the test suites. Next, the combinatorial TCP is performed. Here, the fitness parameters such as Average Percentage of Fault Detected (APFD), Average Percentage of Branch Coverage (APBC), and weight are considered for fitness evaluation. Moreover, the weights in the fitness function are computed by the Deep Q Net (DQN), which is trained by the proposed EVDMOA. At last, the prioritized test cases are obtained. The EVDMOA achieves the AFPD, APBC, and fitness values of 0.907, 0.914, and 0.926. Moreover, the EVDMOA helps in maintaining the overall quality and reliability of the software.

关键词： Energy Valley optimization (EVO) Test Case Prioritization (TCP ) dwarf mongoose optimization algorithm (DMOA) Regression Testing Deep Q Net (DQN)

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