Enhancement of Genetic Algorithm and Ant Colony Optimization Techniques using Fuzzy Systems

To improve the speed and accuracy of numerical optimization methods, this paper proposes a new technique, using fuzzy systems. Although the proposed method is employed to improve the efficiency of the genetic algorithm and ant colony optimization, it can be applied to any swarm intelligence methods. The main idea of this method is to control positive and negative feedbacks to achieve a suitable trade-off between them depending on convergence rate of the algorithm. In order to demonstrate the performance of the proposed method, it is applied to simulation examples.