A novel evolutionary membrane algorithm for global numerical optimization

The contribution of this paper is to present a novel heuristic global intelligent evolutionary algorithm based on membrane systems for solving global optimization problems. The proposed algorithm is inspired by cellular particles with Brownian motion. To simulate the motion process of cellular particles, the cellular automata and the chaotic search are introduced. The effectiveness of this method is demonstrated using four benchmark optimization problems. Finally, computational experiments on benchmark problems show that the proposed algorithm outperforms the classical methods. These indicate that the algorithm not only converges faster, but also effectively avoids falling into local extreme points and keeps the diversity of solutions.