A New Meta-Heuristic Algorithm for Optimization Based on Variance Reduction of Gaussian Distribution

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  • Ali Namadchian1,
  • Mehdi Ramezani2,
  • Navid Razmjooy3,
  1. University of Tafresh, Tafresh, Iran
  2. Department of Mathematics, University of Tafresh, Tafresh, Iran.
  3. Department of Electrical Engineering, University of Tafresh, Tafresh, Iran.

Published in Issue 2024-02-20

How to Cite

Namadchian, A., Ramezani, M., & Razmjooy, N. (2024). A New Meta-Heuristic Algorithm for Optimization Based on Variance Reduction of Gaussian Distribution. Majlesi Journal of Electrical Engineering, 10(4). https://oiccpress.com/mjee/article/view/4767

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Abstract

Meta-heuristic methods are global optimization algorithms which are widely used in the engineering issues, nowadays. In this paper, a new stochastic search for optimization is presented using variable variance Gaussian distribution sampling. The main idea in searching for algorithm is to regenerate new samples around each solution with a Guassian distribution. Numerical simulations have revealed that the new presented algorithm outperformed some evolutionary algorithms.

Keywords

  • Covariance matrix,
  • Gaussian distribution,
  • Optimization,
  • Probability Density Function (PDF,
  • Stochastic search. Variance reduction
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