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

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:

Optimization , en , Gaussian distribution , Covariance matrix , Stochastic search , Variance reduction , Probability Density Function (PDF , hereafter)