Comparison among seven meta-heuristic algorithms for optimizing ten benchmark mathematical functions with large-scale

Evolutionary algorithms such as PSO, ICA, GA and etc. These algorithms are taken from biological or social evolutionary methods. Such algorithms for optimization problems with a large-scale of nonlinear variables are more appropriate than traditional methods. Traditional methods often rely on the computational power of the computer and did not use smart techniques and most of these methods failed to solve nonlinear problems with a large number of variables. In this paper, 10 mathematical benchmark functions are compared with 7 methods of meta-heuristic optimization algorithms. Algorithms such as PSO, ICA, GA. A brief description of each of the algorithms is presented and these algorithms are compared with different functions of the benchmark and with a large number of variables (large scale) in terms of computational time and convergence rate of the answers. Based on these analyses and comparisons, it will be determined which of these algorithms will be improved with which operators

Comparison among seven meta-heuristic algorithms for optimizing ten benchmark mathematical functions with large-scale Keywords:

Mathematical functions , optimization , meta-heuristic algorithms , large scale