Application of computational intelligence techniques to maximize unpredictability in multiscroll chaotic oscillators

Abstract

This chapter applies and compares three computational intelligence algorithms-genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO)-to maximize the positive Lyapunov exponent in a multiscroll chaotic oscillator based on a saturated nonlinear function series based on the modification of the standard settings of the coefficient values of the mathematical description, and taking into account the correct distribution of the scrolls drawing the phase-space diagram.The experimental results show that the DE and PSO algorithms help to maximize the positive Lyapunov exponent of truncated coefficients over the continuous spaces.