Electrical Search Algorithm: A New Metaheuristic Algorithm for Clustering Problem

Demirci, Hüseyin; Yurtay, Nilüfer; Yurtay, Yüksel; Zaimoğlu, Esin Ayşe

doi:10.1007/s13369-022-07545-3

Cited by 8 publications

(3 citation statements)

References 41 publications

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“…This method is operated within the MapReduce framework to manage extensive datasets. Demirci et al [12] proposed an electrical search algorithm (ESA) based on the movement of electricity in high-resistive areas such as wood, glass, and gases and applied it to the clustering problem. Premkumar et al [26] focus on enhancing the grey wolf optimizer using a new weight factor and the concepts of the k means algorithm to increase variety and avoid premature convergence.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Hybrid Mountain Gazelle Optimization and Particle Swarm Optimization Algorithms to Improve Clustering

Mosavi,

Fazeli,

Abbasi

et al. 2024

Preprint

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Clustering plays a crucial role in data mining and machine learning, with the primary objective being the identification of cohesive and distinct data groups, enabling the extraction of valuable information. However, clustering algorithms often encounter the challenge of getting trapped in local optima, hindering their ability to achieve optimal results. To address this issue, researchers have turned to using meta-heuristic algorithms. This article proposes an enhanced approach for clustering by combining the particle swarm optimization algorithm and the mountain gazelle algorithm. The utilization of this combined algorithm has shown superior performance compared to relying solely on the particle swarm algorithm. By using the strengths of both algorithms, our method overcomes the limitations posed by local optima, leading to more accurate and robust clustering results. The proposed algorithm utilizes the minimum fitness measure to locate the optimal centroid, which is determined based on three constraints: intra-cluster distance, inter-cluster distance, and cluster density. The data is then clustered using the optimal centroid corresponding to the minimal value of the fitness. The performance of our proposed approach has been evaluated on real-world datasets such as Iris, Wine, and Vowel. Our method and PSO and MGO algorithms have been compared on these datasets. The results of the experiments indicate that our proposed method outperforms the PSO and MGO algorithms in terms of clustering quality and convergence speed.

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Section: Literature Reviewmentioning

confidence: 99%

Using Hybrid Mountain Gazelle Optimization and Particle Swarm Optimization Algorithms to Improve Clustering

Mosavi,

Fazeli,

Abbasi

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…According to Zafar et al [21], the experimentation was conducted using data based on motor imagery and mental arithmetic tasks for the evaluation and enhancement of braincomputer interface applications. These algorithms can be considered a black box, giving the most suitable variable values for the most suitable solution [23].…”

Section: Ddos Attacks and The Consideration Of Metaheuristic Search M...mentioning

confidence: 99%

“…According to Palaniswamy and Kandhasamy [12], the datasets represented real-world time series of human gene information collected from different scenarios. Demirci et al [23] proposed a metaheuristic technique referred to as the Electrical Search Algorithm, based on electricity movement, in being able to realize exact solutions and being efficient. In another study by Saw and Oo [10], the incorporation of the particle swarm optimization technique was able to achieve relatively high performance in contrast to the genetic algorithm, ant colony optimization, and evolutionary algorithm.…”

Section: Ddos Attacks and The Consideration Of Metaheuristic Search M...mentioning

confidence: 99%

Comparing Metaheuristic Search Techniques in Addressing the Effectiveness of Clustering-Based DDoS Attack Detection Methods

Zeinalpour,

McElroy

2024

Electronics

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Distributed Denial of Service (DDoS) attacks have increased in frequency and sophistication over the last ten years. Part of the challenge of defending against such attacks requires the analysis of very large volumes of data. Metaheuristic algorithms can assist in selecting relevant features from the network traffic data for use in DDoS detection models. By efficiently exploring different combinations of features, these methods can identify subsets that are informative for distinguishing between normal and attack traffic. However, identifying an optimized solution in this area is an open research question. Tuning the parameters of metaheuristic search techniques in the optimization process is critical. In this study, a switching approximation is used in a variety of metaheuristic search techniques. This approximation is used to find the best solution for the analysis of the network traffic features in either lower or upper values between 0 and 1. We compare the fine-tuning of this parameter against standard approaches and find that it is not substantially better than the BestFirst algorithm (a standard default approach for feature selection). This study contributes to the literature by testing and eliminating various fine-tuning strategies for the metaheuristic approach.

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An Effective Crow Search Algorithm and Its Application in Data Clustering

Ranjan,

Chhabra

2024

J Classif

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Electrical Search Algorithm: A New Metaheuristic Algorithm for Clustering Problem

Cited by 8 publications

References 41 publications

Using Hybrid Mountain Gazelle Optimization and Particle Swarm Optimization Algorithms to Improve Clustering

Using Hybrid Mountain Gazelle Optimization and Particle Swarm Optimization Algorithms to Improve Clustering

Comparing Metaheuristic Search Techniques in Addressing the Effectiveness of Clustering-Based DDoS Attack Detection Methods

An Effective Crow Search Algorithm and Its Application in Data Clustering

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