Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review

Internet of Things and Cyber-Physical Systems, Год журнала: 2024, Номер 4, С. 258 - 267

Опубликована: Янв. 1, 2024

The significance of intrusion detection systems in networks has grown because the digital revolution and increased operations. method classifies network traffic as threat or normal based on data features. Intrusion system faces a trade-off between various parameters such accuracy, relevance, redundancy, false alarm rate, other objectives. paper presents systematic review Internet Things (IoT) using multi-objective optimization algorithms (MOA), to identify attempts at exploiting security vulnerabilities reducing chances attacks. MOAs provide set optimized solutions for process highly complex IoT networks. This identification multiple objectives detection, comparative analysis their approaches, datasets used evaluation. show encouraging potential enhance conflicting detection. Additionally, current challenges future research ideas are identified. In addition demonstrating new advancements techniques, this study gaps that can be addressed while designing

Язык: Английский

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A review of nature-inspired algorithms on single-objective optimization problems from 2019 to 2023

Artificial Intelligence Review, Год журнала: 2024, Номер 57(5)

Опубликована: Апрель 24, 2024

Abstract The field of nature inspired algorithm (NIA) is a vital area research that consistently aids in solving optimization problems. One the metaheuristic classifications has drawn attention from researchers recent decades NIA. It makes significant contribution by addressing numerous large-scale problems and achieving best results. This aims to identify optimal NIA for single-objective discovered between 2019 2023 presented this study with brief description. About 83 distinct NIAs have been studied order address issues. In accomplish goal, we taken into consideration eight real-world problems: 3-bar truss design problem, rolling element bearing, pressure vessel, cantilever beam, I welded spring. Based on comparative bibliographic analysis, determined two algorithms—the flow direction algorithm, prairie dog optimization—give us results solutions all engineering listed. Lastly, some perspectives limitations, difficulties, future course are provided. addition providing guidelines, will assist novice emerging researcher more comprehensive perspective advanced

Язык: Английский

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Dung Beetle Optimization Algorithm Based on Improved Multi-Strategy Fusion

Electronics, Год журнала: 2025, Номер 14(1), С. 197 - 197

Опубликована: Янв. 5, 2025

The Dung Beetle Optimization Algorithm (DBO) is characterized by its great convergence accuracy and quick speed. However, like other swarm intelligent optimization algorithms, it also has the disadvantages of having an unbalanced ability to explore world use local resources, as well being prone settling into optimal search in latter stages optimization. In order address these issues, this research suggests a multi-strategy fusion dung beetle method (MSFDBO). To enhance quality first solution, refractive reverse learning technique expands algorithm space stage. algorithm’s increased adding adaptive curve control population size prevent from reaching optimum. improve balance exploitation global exploration, respectively, triangle wandering strategy subtractive averaging optimizer were later added Rolling Breeding Beetle. Individual beetles will congregate at current position, which near value, during last stage MSFDBO; however, value could not be value. Thus, variationally perturb solution (so that leaps out final MSFDBO) algorithmic performance (generally specifically, effect optimizing search), Gaussian–Cauchy hybrid variational perturbation factor introduced. Using CEC2017 benchmark function, MSFDBO’s verified comparing seven different intelligence algorithms. MSFDBO ranks terms average performance. can lower labor production expenses associated with welding beam reducer design after testing two engineering application challenges. When comes lowering manufacturing costs overall weight, outperforms methods.

Язык: Английский

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GOHBA: Improved Honey Badger Algorithm for Global Optimization

Biomimetics, Год журнала: 2025, Номер 10(2), С. 92 - 92

Опубликована: Фев. 6, 2025

Aiming at the problem that honey badger algorithm easily falls into local convergence, insufficient global search ability, and low convergence speed, this paper proposes a optimization (Global Optimization HBA) (GOHBA), which improves ability of population, with better to jump out optimum, faster stability. The introduction Tent chaotic mapping initialization enhances population diversity initializes quality HBA. Replacing density factor range in entire solution space avoids premature optimum. addition golden sine strategy capability HBA accelerates speed. Compared seven algorithms, GOHBA achieves optimal mean value on 14 23 tested functions. On two real-world engineering design problems, was optimal. three path planning had higher accuracy convergence. above experimental results show performance is indeed excellent.

Язык: Английский

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A Review on the Applications of PSO-Based Algorithm in Demand Side Management: Challenges and Opportunities

IEEE Access, Год журнала: 2023, Номер 11, С. 53373 - 53400

Опубликована: Янв. 1, 2023

The increase in energy consumption, environmental pollution issues, and low-carbon agenda has grown the research area of demand side management (DSM). DSM programs provide feasible solutions significantly enhance efficiency sustainability electrical distribution systems. This paper classifies discusses broad definition based on comprehensive literature study considering response efficiency. implementation Artificial Intelligence algorithms applications been employed many studies to help researchers make optimal decisions achieve predictions by analyzing massive amount historical data. Owing its simplicity consistent performance fast convergence time, Particle Swarm Optimization (PSO) is widely used as a part swarm AI algorithm become prominent technique optimization process exploit full benefit demand-side program. variants PSO have developed overcome limitations original solve high complexity uncertainty process. proposed PSO-based can optimize consumers' consumption curves, reducing peak hence minimizing electricity cost when integrated with DR or EE measures. works seen an increasing trend past decade. Therefore, this reviewed application fields some constraints discussed challenges from previous work. potential for new opportunities identified so that methods be future research.

Язык: Английский

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AFOX: a new adaptive nature-inspired optimization algorithm

Artificial Intelligence Review, Год журнала: 2023, Номер 56(12), С. 15523 - 15566

Опубликована: Июль 10, 2023

Язык: Английский

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Duck swarm algorithm: theory, numerical optimization, and applications

Cluster Computing, Год журнала: 2024, Номер 27(5), С. 6441 - 6469

Опубликована: Март 1, 2024

Язык: Английский

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Chaotic-Based Mountain Gazelle Optimizer for Solving Optimization Problems

International Journal of Computational Intelligence Systems, Год журнала: 2024, Номер 17(1)

Опубликована: Май 6, 2024

Abstract The Mountain Gazelle Optimizer (MGO) algorithm has become one of the most prominent swarm-inspired meta-heuristic algorithms because its outstanding rapid convergence and excellent accuracy. However, MGO still faces premature convergence, making it challenging to leave local optima if early-best solutions neglect relevant search domain. Therefore, in this study, a newly developed Chaotic-based (CMGO) is proposed with numerous chaotic maps overcome above-mentioned flaws. Moreover, ten distinct were simultaneously incorporated into determine optimal values enhance exploitation promising solutions. performance CMGO been evaluated using CEC2005 CEC2019 benchmark functions, along four engineering problems. Statistical tests like t-test Wilcoxon rank-sum test provide further evidence that outperforms existing eminent algorithms. Hence, experimental outcomes demonstrate produces successful auspicious results.

Язык: Английский

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A Sinh–Cosh-Enhanced DBO Algorithm Applied to Global Optimization Problems

Biomimetics, Год журнала: 2024, Номер 9(5), С. 271 - 271

Опубликована: Апрель 29, 2024

The Dung beetle optimization (DBO) algorithm, devised by Jiankai Xue in 2022, is known for its strong capabilities and fast convergence. However, it does have certain limitations, including insufficiently random population initialization, slow search speed, inadequate global capabilities. Drawing inspiration from the mathematical properties of Sinh Cosh functions, we proposed a new metaheuristic Sinh–Cosh Beetle Optimization (SCDBO). By leveraging functions to disrupt initial distribution DBO balance development rollerball dung beetles, SCDBO enhances efficiency exploration through nonlinear enhancements. These improvements collectively enhance performance making more adept at solving complex real-world problems. To evaluate compared with seven typical algorithms using CEC2017 test functions. Additionally, successfully applying three engineering problems, robot arm design, pressure vessel problem, unmanned aerial vehicle (UAV) path planning, further demonstrate superiority algorithm.

Язык: Английский

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A novel opposition-based hybrid cooperation search algorithm with Nelder–Mead for tuning of FOPID-controlled buck converter

Transactions of the Institute of Measurement and Control, Год журнала: 2024, Номер 46(10), С. 1924 - 1942

Опубликована: Янв. 18, 2024

This paper introduces a novel metaheuristic algorithm named the opposition-based cooperation search with Nelder–Mead (OCSANM). enhanced builds upon (CSA) by incorporating learning (OBL) and simplex method. The primary application of this is design fractional-order proportional–integral–derivative (FOPID) controller for buck converter system. A comprehensive evaluation conducted using statistical boxplot analysis, nonparametric tests convergence response comparisons to assess algorithm’s performance confirm its superiority over CSA. Furthermore, FOPID-controlled system based on OCSANM compared two top-performing algorithms: one hybridized approach Lévy flight distribution simulated annealing (LFDSA) other employing improved hunger games (IHGS) algorithm. comparison encompasses transient frequency responses, indices robustness analysis. results reveal notable advantages proposed OCSANM-based system, including 25.8% 8.7% faster rise times, 26% 8.8% settling times best-performing approaches, namely LFDSA IHGS, respectively. In addition, exhibits 34.7% 9.6% wider bandwidth than existing approaches-based systems. Incorporating voltage current responses converter’s switched circuit FOPID further underscores effectiveness. To provide assessment, also compares approach’s time domain those 17 state-of-the-art approaches attempting control systems similarly. These findings affirm effectiveness in designing controllers

Язык: Английский

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