A Modified Grey Wolf Optimizer Based Data Clustering Algorithm

Ahmadi, Ramin; Ekbatanifard, Gholamhossein; Bayat, Peyman

doi:10.1080/08839514.2020.1842109

Cited by 43 publications

(16 citation statements)

References 33 publications

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“…In the classical GWO method, the parameter a has a linear behavior, so it tends to be explored at the beginning of the algorithm execution, and, during the iterations, it tends to more exploit than explore. By converting the linear behavior of this variable from linear to nonlinear, an acceptable balance between exploration and extraction can be created [ 41 ]. The following formula is used to update this variable in each iteration.…”

Section: The Proposed Methodsmentioning

confidence: 99%

A novel hybrid hunger games algorithm for intrusion detection systems based on nonlinear regression modeling

Mohammadi

Babagoli

2023

Int. J. Inf. Secur.

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Along with the advancement of online platforms and significant growth in Internet usage, various threats and cyber-attacks have been emerging and become more complicated and perilous in a day-by-day base. Anomaly-based intrusion detection systems (AIDSs) are lucrative techniques for dealing with cybercrimes. As a relief, AIDS can be equipped with artificial intelligence techniques to validate traffic contents and tackle diverse illicit activities. A variety of methods have been proposed in the literature in recent years. Nevertheless, several important challenges like high false alarm rates, antiquated datasets, imbalanced data, insufficient preprocessing, lack of optimal feature subset, and low detection accuracy in different types of attacks have still remained to be solved. In order to alleviate these shortcomings, in this research a novel intrusion detection system that efficiently detects various types of attacks is proposed. In preprocessing, Smote-Tomek link algorithm is utilized to create balanced classes and produce a standard CICIDS dataset. The proposed system is based on gray wolf and Hunger Games Search (HGS) meta-heuristic algorithms to select feature subsets and detect different attacks such as distributed denial of services, Brute force, Infiltration, Botnet, and Port Scan. Also, to improve exploration and exploitation and boost the convergence speed, genetic algorithm operators are combined with standard algorithms. Using the proposed feature selection technique, more than 80 percent of irrelevant features are removed from the dataset. The behavior of the network is modeled using nonlinear quadratic regression and optimized utilizing the proposed hybrid HGS algorithm. The results show the superior performance of the hybrid algorithm of HGS compared to the baseline algorithms and the well-known research. As shown in the analogy, the proposed model obtained an average test accuracy rate of 99.17%, which has better performance than the baseline algorithm with 94.61% average accuracy.

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Section: The Proposed Methodsmentioning

confidence: 99%

A novel hybrid hunger games algorithm for intrusion detection systems based on nonlinear regression modeling

Mohammadi

Babagoli

2023

Int. J. Inf. Secur.

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“…Researchers have developed numerous metaheuristic algorithms to solve optimization problems more effectively. These methods have found applications in various fields such as dynamic scheduling 10 , construction of multi-classifier systems [11][12] , clustering approach [13][14][15] , IoT-based complex problems [16][17] , and energy carriers and electrical engineering [18][19][20][21][22] .…”

Section: Introductionmentioning

confidence: 99%

Mother Optimization Algorithm: A New Human-Based Metaheuristic Approach for Solving Engineering Optimization

Matoušová

Trojovský

Dehghani

et al. 2023

Preprint

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This paper presents a new metaheuristic method called Mother Optimization Algorithm (MOA) that mimics the human interaction between a mother and her children. The fundamental inspiration of MOA is to simulate the mother's care of children in three phases education, advice, and upbringing. The mathematical model of MOA used in the search process and exploration is presented. The performance of MOA is assessed on a set of 52 benchmark functions, including unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, and the CEC 2017 test suite. The results indicate that MOA's proposed approach, which balances exploration and exploitation, effectively supports the search process and can generate appropriate solutions for optimization problems. The outcomes quality obtained from MOA has been compared with the performance of twelve often-used metaheuristic algorithms. Upon analysis and comparison of the simulation results, it was found that the proposed MOA outperforms competing algorithms with superior and significantly more competitive performance. Specifically, the proposed MOA delivers better results in most objective functions. Furthermore, the application of MOA on four engineering design problems demonstrates the efficacy of the proposed approach in solving real-world optimization problems.

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“…Such an above study was carried out to arouse the interest in the GWO algorithm widely applied in the field of UAVs' path planning. The advantages in terms of simplicity of software implementation, reduced number of the algorithm's control parameters, and convergence fastness make the GWO one of the most extensively used algorithms in the past three years [20][21][22][23]. The increased number of scientific publications on this topic explains the effectiveness of such a stochastic and parameters-free algorithm for solving various optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Parallel Cooperative Coevolutionary Grey Wolf Optimizer for Path Planning Problem of Unmanned Aerial Vehicles

Jarray

Al‐Dhaifallah

Rezk

2022

Sensors

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The path planning of Unmanned Aerial Vehicles (UAVs) is a complex and hard task that can be formulated as a Large-Scale Global Optimization (LSGO) problem. A higher partition of the flight environment leads to an increase in route’s accuracy but at the expense of greater planning complexity. In this paper, a new Parallel Cooperative Coevolutionary Grey Wolf Optimizer (PCCGWO) is proposed to solve such a planning problem. The proposed PCCGWO metaheuristic applies cooperative coevolutionary concepts to ensure an efficient partition of the original search space into multiple sub-spaces with reduced dimensions. The decomposition of the decision variables vector into several sub-components is achieved and multi-swarms are created from the initial population. Each sub-swarm is then assigned to optimize a part of the LSGO problem. To form the complete solution, the representatives from each sub-swarm are combined. To reduce the computation time, an efficient parallel master-slave model is introduced in the proposed parameters-free PCCGWO. The master will be responsible for decomposing the original problem and constructing the context vector which contains the complete solution. Each slave is designed to evolve a sub-component and will send the best individual as its representative to the master after each evolutionary cycle. Demonstrative results show the effectiveness and superiority of the proposed PCCGWO-based planning technique in terms of several metrics of performance and nonparametric statistical analyses. These results show that the increase in the number of slaves leads to a more efficient result as well as a further improved computational time.

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A Modified Grey Wolf Optimizer Based Data Clustering Algorithm

Cited by 43 publications

References 33 publications

A novel hybrid hunger games algorithm for intrusion detection systems based on nonlinear regression modeling

A novel hybrid hunger games algorithm for intrusion detection systems based on nonlinear regression modeling

Mother Optimization Algorithm: A New Human-Based Metaheuristic Approach for Solving Engineering Optimization

Parallel Cooperative Coevolutionary Grey Wolf Optimizer for Path Planning Problem of Unmanned Aerial Vehicles

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