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An improved adaptive memetic differential evolution optimization algorithms for data clustering problems.

Hossam M J Mustafa1, Masri Ayob1, Mohd Zakree Ahmad Nazri1

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Summary
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This study introduces an adaptive memetic differential evolution optimisation algorithm (AMADE) for improved data clustering. AMADE enhances the balance between exploration and exploitation, outperforming existing methods on real-world datasets.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Data clustering algorithm performance relies on balancing search exploration and exploitation.
  • Existing algorithms show limitations in real-world dataset performance.
  • Improved optimization strategies are needed for effective data clustering.

Purpose of the Study:

  • To propose an adaptive memetic differential evolution optimisation algorithm (AMADE) for data clustering.
  • To enhance the balance between exploration and exploitation in the search process.
  • To improve the performance of data clustering on real-life datasets.

Main Methods:

  • Developed an adaptive memetic algorithm (MA) incorporating a differential evolution (DE) mutation strategy.
  • Hybridized the adaptive DE mutation operator with the MA to create AMADE.
  • Evaluated AMADE on several real-life benchmark datasets.

Main Results:

  • AMADE demonstrated faster convergence compared to standalone MA and DE.
  • The proposed AMADE algorithm achieved superior performance over other clustering algorithms.
  • Statistical analysis confirmed the effectiveness of AMADE on benchmark datasets.

Conclusions:

  • Hybridizing memetic algorithms with adaptive differential evolution is effective for data clustering.
  • AMADE successfully balances global exploration and local exploitation in optimization.
  • The proposed approach offers a significant improvement for data clustering problems.