Permuting Convergence Overcomingof Genetic Algorithm Using Arnold Cat Map

Today the evolutionary algorithms (EA) such as Genetic algorithm (GA) are used to solve nonlinear optimization problems and find optimum solution. The fundamental of GA is the random where many processes used randomness in their operating. However, the most important disadvantages in GA are permuting convergence which is known to increase the number of iteration for reaching a global optimum. This paper use Arnold Cat Map (ACM) to improve the performance of the GA and to overcome this shortcoming. The experimental results show that the proposed method improves the ability of GA for reaching optimal/near optimal solutions with less number of iterations and significantly improves the solutions quality of the basic GA.