Adaptive Sine-Cosine Optimization Technique for Stability and Domain of Attraction Analysis

In the last few years, researchers have concentrated on estimating and maximizing the Domain of Attraction of autonomous nonlinear systems. Based on the Lyapunov theory, the proposed approach in this paper aims to give an accurate estimation of the Domain of Attraction with high performance against the existing conventional methods. The Adaptive Sine-Cosine Algorithm has been considered one of the most advanced algorithms. It combines a large exploration with a strong local search and provides high-quality convergence conditions. This paper uses the benefits of the Adaptive Sine-Cosine Algorithm to develop a flexible method to estimate the Domain of Attraction by an oriented sampling to guarantee the largest sublevel related to the given Lyapunov function. The approach is applied to some benchmark examples and validates its efficiency and its ability to provide performant results.