Recurrent fuzzy system design using mutation-aided elite continuous ant colony optimization

摘要

This paper proposes a new metaheuristic population-based evolutionary optimization algorithm, mutation-aided elite continuous ant colony optimization (MECACO), for the design of TSK-type recurrent fuzzy neural network (TRFN). The basic principle of MECACO is a stochastic search algorithm which combines a new designed elites-based continuous ACO with the mutation technique employing the dynamic mutation probability to exploit and explore the solutions globally at the same time. The MECACO was applied to the reinforcement learning of the TRFN for the tracking control of the nonlinear dynamic plants to demonstrate its effectiveness. The MECACO performance is compared with different continuous ACO-based algorithms through simulations.