Maximizing the Chaotic Behavior of Fractional Order Chen System by Evolutionary Algorithms

This paper presents the application of three optimization algorithms to increase chaotic behavior fractional order Chen system. is achieved by optimizing maximum Lyapunov exponent (MLE). The applied techniques are evolutionary (EAs), namely: differential evolution (DE), particle swarm (PSO), and invasive weed (IWO). In each algorithm, process performed using 100 individuals generations from 50 500, with a step 50, which makes total ten independent runs. results show that optimized systems have higher exponents than non-optimized system, DE giving highest MLE. Additionally, indicate system multifaceted respect parameter values. dynamical complexity verified properties, such as bifurcation, LE spectrum, equilibrium point, eigenvalue, sample entropy. Moreover, compared hyper-chaotic on basis their prediction times. shorter time suitable for developing secure communication random number generator. Finally, Halstead parameters measure were implemented in MATLAB. reveal has simplest implementation.