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

Meta-heuristic methods are global optimization algorithms which are widely used in the engineering issues, nowadays. The main problem with the classical optimization algorithms is their slow rate of convergence to time-consuming mathematical calculations. In this paper, a new stochastic search for optimization is presented using variable variance Guassian distribution sampling. The main idea of searching in this algorithm is to regenerate new samples around each solution with a Guassian distribution. The proposed algorithm is applied to four popular test functions for optimizations (Griewank, Booth, Rosenbrock, Rastrigin). Numerical simulations have revealed that the new presented algorithm outperformed simulated annealing and genetic algorithms.