Improved seagull optimization algorithm using Lévy flight and mutation operator for feature selection

Ewees, Ahmed A.; Mostafa, Reham R.; Ghoniem, Rania M.; Gaheen, Marwa A.

doi:10.1007/s00521-021-06751-8

Cited by 66 publications

(32 citation statements)

References 83 publications

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“…Here, the confusion matrix is explained in detail, where TP and TN stand for true positive and true negative, respectively; the abbreviations FP and FN stand for false positive and false negative, respectively. These are metrics used to evaluate a classifier’s accuracy, specificity, and sensitivity [ 27 ].

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Section: Resultsmentioning

confidence: 99%

“…The advancement of networks has always been linked to advances in information technology, but the internet economy is growing due to the Internet of Things. SaiSindhuTheja et al [ 27 ] proposed a denial-of-service detection system that combined the Crow Search Algorithm and opposition-based learning methods into the Oppositional Crow Search Algorithm. The suggested algorithm is used in the feature selection process to detect cyberattacks in the cloud environment.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, Meta-heuristics (MH) algorithms have shown an excellent performance in a variety of optimization scenarios due to their dynamic search behavior and global search capabilities [ 19 ]. It has been widely used in the literature to provide acceptable solutions to FS problems, for example, genetic algorithm [ 20 , 21 , 22 ], particle swarm optimization (PSO) [ 23 ], grey wolf optimizer (GWO) [ 24 , 25 ], harmony search (HS) [ 26 ], and the seagull optimization algorithm (SOA) [ 27 ]. All MH algorithms, on the other hand, need to balance the exploration and exploitation stages in order to avoid getting stuck in local optima or failing to converge [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection

Kareem

Mostafa

Hashim

et al. 2022

Sensors

Self Cite

105

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The increasing use of Internet of Things (IoT) applications in various aspects of our lives has created a huge amount of data. IoT applications often require the presence of many technologies such as cloud computing and fog computing, which have led to serious challenges to security. As a result of the use of these technologies, cyberattacks are also on the rise because current security methods are ineffective. Several artificial intelligence (AI)-based security solutions have been presented in recent years, including intrusion detection systems (IDS). Feature selection (FS) approaches are required for the development of intelligent analytic tools that need data pretreatment and machine-learning algorithm-performance enhancement. By reducing the number of selected features, FS aims to improve classification accuracy. This article presents a new FS method through boosting the performance of Gorilla Troops Optimizer (GTO) based on the algorithm for bird swarms (BSA). This BSA is used to boost performance exploitation of GTO in the newly developed GTO-BSA because it has a strong ability to find feasible regions with optimal solutions. As a result, the quality of the final output will increase, improving convergence. GTO-BSA’s performance was evaluated using a variety of performance measures on four IoT-IDS datasets: NSL-KDD, CICIDS-2017, UNSW-NB15 and BoT-IoT. The results were compared to those of the original GTO, BSA, and several state-of-the-art techniques in the literature. According to the findings of the experiments, GTO-BSA had a better convergence rate and higher-quality solutions.

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…”

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection

Kareem

Mostafa

Hashim

et al. 2022

Sensors

Self Cite

105

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“…Wang et al [ 25 ] analyzed the parameter A of SOA in detail, presented the best advantage set theory and the idea of the Yin-Yang Pair, and proposed an improved seagull fusion algorithm, YYPSOA. Ewees et al [ 26 ] introduced Levy flight strategy and mutation operator to prevent the algorithm from falling into local optimum. Wang et al [ 27 ] introduced the opposite-based learning strategy to initialize the population, and used the quantum optimization method to update the seagull population.…”

Section: Introductionmentioning

confidence: 99%

Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy

Xia

Ding

Zhang

et al. 2022

Entropy

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This paper aims to present a novel hybrid algorithm named SPSOA to address problems of low search capability and easy to fall into local optimization of seagull optimization algorithm. Firstly, the Sobol sequence in the low-discrepancy sequences is used to initialize the seagull population to enhance the population’s diversity and ergodicity. Then, inspired by the sigmoid function, a new parameter is designed to strengthen the ability of the algorithm to coordinate early exploration and late development. Finally, the particle swarm optimization learning strategy is introduced into the seagull position updating method to improve the ability of the algorithm to jump out of local optimization. Through the simulation comparison with other algorithms on 12 benchmark test functions from different angles, the experimental results show that SPSOA is superior to other algorithms in stability, convergence accuracy, and speed. In engineering applications, SPSOA is applied to blind source separation of mixed images. The experimental results show that SPSOA can successfully realize the blind source separation of noisy mixed images and achieve higher separation performance than the compared algorithms.

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“…[18] As more and more swarm intelligence algorithms are put forward, many scholars not only study the traditional intelligent algorithms deeply but also improve them. [19][20][21] For example, Kamel et al proposed a developed version of Electric Charged Particles Optimization (ECPO) to enhance the search capabilities of traditional ECPO and the balance between the development and exploration phases. [22] Kaveh et al proposed an Improved Arithmetic Optimization Algorithm (IAOA), which not only promoted the exploration capability of the search space but also overcome the shortcoming of the premature convergence of the standard Arithmetic Optimization Algorithm (AOA).…”

mentioning

confidence: 99%

Hybrid Multi‐Strategy Improved Wild Horse Optimizer

Yuan

Han

et al. 2022

Advanced Intelligent Systems

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Wild Horse Optimizer (WHO), a new metaheuristic algorithm proposed in recent years, has some weaknesses in solving practical problems, such as low searching accuracy and slow convergence speed. Herein, a Hybrid Multi‐Strategy improved Wild Horse Optimizer (HMSWHO) is proposed, which includes four strategies to improve the optimization capability. The Halton sequence is used to initialize the foal population to make the population more diverse. The adaptive parameter TDR is improved to balance the global exploration and local exploitation. The simplex method is used to improve the worst position of the population. Wild horse escaping behavior is added to improve search efficiency and optimization accuracy. The main innovation strategies are the improvement of TDR and the addition of escaping behavior. To verify the effectiveness of the improved strategies, 12 benchmark test functions, CEC2017, and CEC2021 test functions are selected for simulation experiments. Mechanical design examples are added for optimization, and the optimization results are 16.61%, 1.65%, and 0.21% less than that of WHO. The results show that the improved algorithm has obvious advantages in convergence speed, accuracy, and stability. HMSWHO can be applied to more practical engineering optimization problems and provide new ideas for structural optimization methods.

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Improved seagull optimization algorithm using Lévy flight and mutation operator for feature selection

Cited by 66 publications

References 83 publications

An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection

An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection

Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy

Hybrid Multi‐Strategy Improved Wild Horse Optimizer

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