AMOS-a new hybrid evolutionary algorithm for continuous time systems

Abstract

A novel evolutionary algorithm called atomic metaphor optimization strategy (AMOS) is proposed, which is designed for real-time analog optimization problems. This new evolutionary algorithm is integrated with the continuous time adaptive observer algorithm based on the Lyapunov stability theory, developed for classes of approximating functions with linear parametrization. The combined hybrid algorithm is applied to the online modeling of continuous-time nonlinear systems, via a nonlinearly parametrized neural approximation of the system dynamics.