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

  • Ali Namadchian University of Tafresh, Tafresh, Iran
  • Mehdi Ramezani University of Tafresh, Tafresh, Iran
  • Navid Razmjooy University of Tafresh, Tafresh, Iran http://orcid.org/0000-0002-0102-1482
Keywords: Optimization, Gaussian distribution, Covariance matrix, Stochastic search, Variance reduction, Probability Density Function (PDF, hereafter)

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.

Author Biographies

Ali Namadchian, University of Tafresh, Tafresh, Iran
Department of Electrical Engineering, University of Tafresh, Tafresh, Iran
Mehdi Ramezani, University of Tafresh, Tafresh, Iran
Department of Mathematics, University of Tafresh, Tafresh, Iran
Navid Razmjooy, University of Tafresh, Tafresh, Iran
Department of Electrical Engineering, University of Tafresh, Tafresh, Iran

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Published
2016-12-03
How to Cite
Namadchian, A., Ramezani, M., & Razmjooy, N. (2016). A New Meta-Heuristic Algorithm for Optimization Based on Variance Reduction of Gaussian Distribution. Majlesi Journal of Electrical Engineering, 10(4). Retrieved from http://mjee.iaumajlesi.ac.ir/index/index.php/ee/article/view/2017
Section
Articles