Bat-Cluster: A Bat Algorithm-based Automated Graph Clustering Approach

Boulouard, Zakaria; Haddadi, Amine El; Bouhafer, Fadwa; Haddadi, Anass El; Koutti, Lahcen; Dousset, Bernard

doi:10.11591/ijece.v8i2.pp1122-1130

Cited by 5 publications

(5 citation statements)

References 23 publications

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“…In the classification, clustering, and forecasting domain, the BA is successfully applicable in several problems such as prediction and classification [ 18 , 20 , 74 , 123 , 125 , 183 , 185 , 195 , 205 , 212 , 245 , 285 , 294 , 307 ], data clustering [ 17 , 27 , 46 , 98 , 223 , 234 , 238 , 295 , 301 , 345 , 346 ], data forecasting [ 39 , 168 , 243 , 335 ], and train the feed forward neural network [ 75 , 171 , 213 ]. These applications are shown in Fig 15 .…”

Section: Applications Of Bat-inspired Algorithmmentioning

confidence: 99%

Recent advances of bat-inspired algorithm, its versions and applications

Alyasseri

Alomari

Al-Betar

et al. 2022

Neural Comput & Applic

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Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. The critical analysis of the limitations and shortcomings of BA is also mentioned. The open-source codes of BA code are given to build a wealthy BA review. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related contributions.

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Section: Applications Of Bat-inspired Algorithmmentioning

confidence: 99%

Recent advances of bat-inspired algorithm, its versions and applications

Alyasseri

Alomari

Al-Betar

et al. 2022

Neural Comput & Applic

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“…Hence, λi and fi can be used to adjust the velocity of the bats. Moreover, the value of fmin and fmax are set depending on user requirements (generally, the values are 0 and 1) [21][22][23].…”

Section: Pi Controllermentioning

confidence: 99%

“…The value of α and γ can be set with the same value between 0 to 1, for simplicity. In this paper bat position is described as the PI controller parameters [21][22][23]…”

Section: Pi Controllermentioning

confidence: 99%

“…To use the behavior of bats forInt J Elec & Comp Eng ISSN: 2088-8708  Design of spark ignition engine speed control using bat algorithm (Herlambang Setiadi)797 solving optimization problems, describing the bats activity via mathematical representation is crucial. The mathematical representation of bat position and velocity can be described as(7),(8) and(9) [21][22][23].…”

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confidence: 99%

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Design of spark ignition engine speed control using bat algorithm

Setiadi

Jones

Nugroho

et al. 2021

IJECE

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The most common problem in spark ignition engine is how to increase the speed performance. Commonly researchers used traditional mathematical approaches for designing speed controller of spark ignition engine. However, this solution may not be sufficient. Hence, it is important to design the speed controller using smart methods. This paper proposes a method for designing speed controller of a spark ignition engine using the bat algorithm (BA). The simulation is carried out using the MATLAB/SIMULINK environment. Time domain simulation is carried out to investigate the efficacy of the proposed method. From the simulation results, it is found that by designing speed controller of spark ignition engine using PI based bat algorithm, the speed performance of spark ignition engine can be enhanced both in no load condition and load condition compared to conventional PI controler.

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“…Artificial Intelligence (AI) encompasses technologies that are expanded from the word 'technology,' and technology is a word originating from 'techne'; a Greek word that carries the meaning of "art" and "skill" [1][2][3]. Meanwhile, a refined technology encompasses a growing establishment of well-read and well-refined processes and skill.…”

Section: Introductionmentioning

confidence: 99%

Local search algorithms based on benchmark test functions problem

Bassel

Haglan

Mahmoud

2020

IJ-AI

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<p>Optimization process is normally implemented to solve several objectives in the form of single or multi-objectives modes. Some traditional optimization techniques are computationally burdensome which required exhaustive computational times. Thus, many studies have invented new optimization techniques to address the issues. To realize the effectiveness of the proposed techniques, implementation on several benchmark functions is crucial. In solving benchmark test functions, local search algorithms have been rigorously examined and employed to diverse tasks. This paper highlights different algorithms implemented to solve several problems. The capacity of local search algorithms in the resolution of engineering optimization problem including benchmark test functions is reviewed. The use of local search algorithms, mainly Simulated Annealing (SA) and Great Deluge (GD) according to solve different problems is presented. Improvements and hybridization of the local search and global search algorithms are also reviewed in this paper. Consequently, benchmark test functions are proposed to those involved in local search algorithm.</p>

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Bat-Cluster: A Bat Algorithm-based Automated Graph Clustering Approach

Cited by 5 publications

References 23 publications

Recent advances of bat-inspired algorithm, its versions and applications

Recent advances of bat-inspired algorithm, its versions and applications

Design of spark ignition engine speed control using bat algorithm

Local search algorithms based on benchmark test functions problem

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