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A Comparative Study on the Performance of Fuzzy Logic, Particle Swarm Optimization, Firefly Algorithm and Cuckoo Search Algorithm Using Residual Analysis

  • Shrayasi DattaEmail author
  • J. Pal Choudhury
Conference paper
  • 74 Downloads
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 12)

Abstract

Yeast is considered one of the significant elements for medicines and a wide range of chemical products. Various types of Yeast are available. Based on certain initial characteristics (data values) the type of yeast can be ascertained. In this paper, a hybrid model has been proposed to get proper characteristics data values of yeast data so that proper type of yeast data can be ascertained. Here, 50 selected data samples among 1484 samples of yeast dataset have been taken for case study. Here at first Factor analysis (FA) and principal component analysis (PCA) has been applied to find out the cumulative effect of each sample. Then based on Residual error analysis of FA and PCA, cumulative effect value has been taken from FA and thereafter two soft computing models, viz. Particle Swarm Optimization (PSO), Fuzzy Time Series model (FTS) model and two swarm intelligence models viz. firefly algorithm and cuckoo search algorithm have been applied on that cumulative effect data. Finally, using residual analysis their performance has been evaluated.

Keywords

Fuzzy time series Factor analysis Total effect Classification Particle swarm optimization Cuckoo search algorithm Principal component analysis Firefly algorithm Yeast 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Information TechnologyJalpaiguri Government Engineering CollegeJalpaiguriIndia
  2. 2.Futute Institute of Engineering and ManagementSonarpurIndia

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