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UCPSO: A Uniform Initialized Particle Swarm Optimization Algorithm with Cosine Inertia Weight.

Computational intelligence and neuroscience·2021
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Hybrid Fuzzy Clustering Method Based on FCM and Enhanced Logarithmical PSO (ELPSO).

Jian Zhang1, Zongheng Ma1

  • 1School of Mechanical Engineering, Tongji University, Shanghai 200092, China.

Computational Intelligence and Neuroscience
|April 8, 2020
PubMed
Summary

This study introduces FCM-ELPSO, a novel hybrid fuzzy clustering algorithm. It enhances fuzzy c-means (FCM) with an improved particle swarm optimization (PSO) to achieve faster convergence and better clustering results.

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Area of Science:

  • Computer Science
  • Data Mining
  • Artificial Intelligence

Background:

  • Traditional fuzzy c-means (FCM) clustering can get stuck in local minima, limiting its accuracy.
  • Existing hybrid methods combining FCM with particle swarm optimization (PSO) improve accuracy but suffer from slow execution times and parameter tuning issues.

Purpose of the Study:

  • To introduce FCM-ELPSO, a novel hybrid fuzzy clustering algorithm designed to overcome the limitations of traditional FCM and existing PSO-FCM hybrids.
  • To improve convergence speed and clustering accuracy in data analysis.

Main Methods:

  • The study combines FCM with an enhanced version of PSO (ELPSO).
  • ELPSO utilizes a new enhanced logarithmic inertia weight strategy to balance exploration and exploitation.
  • The PBM(F) index and objective function value are used as cluster validity indexes.

Main Results:

  • The proposed FCM-ELPSO algorithm demonstrates significantly improved convergence speed compared to existing methods.
  • Experiments show enhanced clustering effects and accuracy.
  • The ELPSO component effectively balances exploration and exploitation in the optimization process.

Conclusions:

  • FCM-ELPSO offers a more effective and efficient approach to fuzzy clustering.
  • The enhanced logarithmic inertia weight strategy in ELPSO is key to its improved performance.
  • This hybrid method provides a robust solution for complex dataset organization and classification.