Enhanced asynchronous differential evolution using trigonometric mutation

Abstract

The Asynchronous Differential Evolution (ADE) is based on Differential Evolution (DE) with some variations. In ADE the population is updated as soon as a vector with better fitness is found hence the algorithm works asynchronously. ADE leads to stronger exploration and supports parallel optimization. In this paper ADE is embedded with the trigonometric mutation operator (TMO) to enhance the convergence rate of basic ADE. The proposed hybridized algorithm is termed as ADE-TMO. The algorithm is verified over widely used 10 benchmark functions referred from the literature. The simulated results show that ADE-TMO perform better than basic ADE and other state-of-art algorithms.