A Genetic-Frog Leaping Algorithm for Large Dataset Document Clustering

Alhenaki, Lubna; Hosny, Manar

doi:10.1109/aiccsa47632.2019.9035266

Cited by 2 publications

(2 citation statements)

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“…Alhenaki and hosny presented a hybrid geneticshuffled frog leaping algorithm for clustering text documents [9]. Genetic algorithm is used for features selection task, while a shuffled frog-leaping algorithm is used to handle the clustering task in this approach.…”

Section: Related Workmentioning

confidence: 99%

“…In order to overcome local optima problems, several nature inspired algorithms such as, cuckoo search (CS) [2], firefly algorithm (FFA) [2], particle swarm optimization (PSO) [3], artificial bee colony (ABC) [4], grey wolf optimizer (GWO) [5], bat optimization (BAT) [2], multi-verse optimizer (MVO) [6], salp swarm algorithm (SSA) [7], ant colony optimization (ACO) [8], genetic algorithm [9] etc. have been used.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Shuffled Frog-Simulated Annealing Algorithm for Clustering

Sureja¹,

Vasant²,

Chaudhari³

2021

IJIES

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Data clustering is a collection of data objects similar to one another within the same cluster and dissimilar to the objects in other clusters. The shuffled frog-leaping algorithm is a nature-inspired algorithm that mimics the natural biological evolution process of frogs. This algorithm also consists of elements like local search and exchanging information globally. This algorithm faces the problem of converging in local optima due to the limitations of the local search method used to explore search space. In this paper, a hybrid shuffled frog-leaping algorithm is introduced for clustering. The proposed algorithm uses a simulated annealing search method instead of a simple local search to improve the search behavior for selecting fitter solutions required in each iteration. Six benchmark datasets are used to validate the performance of the proposed algorithm. Quality measures used are purity, entropy, completeness score (CS), homogeneity score (HS), and FMeasure (FM). Fitness functions used to optimize are total within-cluster variance (TW) and the Silhouette coefficient (SC). Results obtained are compared with the results of twelve other state of the art algorithms. Results stored in the tables clearly shows that our proposed algorithm outperforms other algorithms in terms of quality. Results also prove that the proposed algorithm converges in the significantly less amount of time and eliminates local optima problem also.

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Section: Related Workmentioning

confidence: 99%