A Modified and Efficient Shuffled Frog Leaping Algorithm (MSFLA) for Unsupervised Data Clustering

Chittineni, Suresh; Godavarthi, Dinesh; Pradeep, Arun; Satapathy, Suresh Chandra; Reddy, P. Jayarama

doi:10.1007/978-3-642-22720-2_57

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…It assumes that the greater inertia weight offers exploration while a smaller one raises the local exploration. Instead of considering a fixed inertia weight value, it is decreased repeatedly from a greater to smaller specified value [19,20]. , where m and n represents the number of documents and sentences in each document respectively.…”

Section: Modified Sflamentioning

confidence: 99%

Performance analysis of Modified Shuffled Frog leaping Algorithm for Multi-document Summarization Problem

Rautray

Dash

2019

IJCAI

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Due to massive growth of Web information, handling useful information has become a challenging issue in now-a-days. In the past few decades, text summarization is considered as one of the solution to obtained relevant information from extensive collection of information. In this paper, a novel approach using modified shuffled frog leaping algorithm (MSFLA) to extract the important sentence from multiple documents is presented. The effectiveness of MSFLA algorithm for summarization model is evaluated by comparing the ROUGE score and statistical analysis of the model with respect to results of other summarization models. The models are demonstrated by the simulation results over DUC datasets. In the present work, it elucidates that MSFLA based model improves the results and find advisable solution for summary extraction. Povzetek: Na bazah dokumentov je bil uporabljen nov algoritem MSFLA za generiranje povzetkov dokumentov.

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Section: Modified Sflamentioning

confidence: 99%

Performance analysis of Modified Shuffled Frog leaping Algorithm for Multi-document Summarization Problem

Rautray

Dash

2019

IJCAI

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Field Development Optimization Under Geological Uncertainty

Yousefzadeh¹,

Kazemi²,

Ahmadi³

et al. 2023

SpringerBriefs in Petroleum Geoscience &Amp; Engineering

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The role of basic, modified and hybrid shuffled frog leaping algorithm on optimization problems: a review

2014

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Shuffled frog leaping algorithm (SFLA) is a meta-heuristic to handle different large-scale optimization problems. SFLA is a population-based algorithm that combines the advantages of memetic algorithm and particle swarm optimization. This paper compares previous researches on SFLA and its effectiveness, with the most applied optimization algorithms reviewed and analyzed. Based on the literature, many efforts by previous researchers on SFLA denote the next generations of basic SFLA with diverse structures for modified SFLA or hybrid SFLA. As well, an attempt is made to highlight these structures, their enhancements and advantages. Moreover, this paper considers top improvements on SFLA for solving multi-objective optimization problems, enhancing local and global exploration, avoiding being trapped into local optima, declining computational time and improving the quality of the initial population. The measured enhancements in SFLA are based on the statistical results obtained from 89 published papers and by considering the most common and effective modifications done by a large number of researchers. Finally, the quantitative validations address the SFLA as a robust algorithm employed in various applications which outperforms the other optimization algorithms.

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A Modified and Efficient Shuffled Frog Leaping Algorithm (MSFLA) for Unsupervised Data Clustering

Cited by 4 publications

References 6 publications

Performance analysis of Modified Shuffled Frog leaping Algorithm for Multi-document Summarization Problem

Performance analysis of Modified Shuffled Frog leaping Algorithm for Multi-document Summarization Problem

Field Development Optimization Under Geological Uncertainty

The role of basic, modified and hybrid shuffled frog leaping algorithm on optimization problems: a review

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