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

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

Automated summary

This paper applies three optimization algorithms to enhance the behavior of the fractional order chaotic Chen system, achieving higher maximum Lyapunov exponents with DE algorithm performing the best. The chaotic behavior of the fractional order Chen system is found to be multifaceted with respect to parameter and fractional order values, and the optimized systems show shorter prediction times compared to a hyper-chaotic system, suitable for secure communication and random number generation applications. Halstead parameters measure the complexity of the implemented optimization algorithms, with IWO having the simplest implementation.