An immune-based ant colony algorithm for static and dynamic optimization

This paper proposes a hybrid optimization method based on the ant colony and clonal selection algorithms, in which the cloning and mutation operations are embedded in the ant colony to enhance its search capability. The novel algorithm is employed to deal with a few benchmark optimization problems under both static and dynamic environments. Simulation results demonstrate the remarkable advantages of our approach in diverse optimal solutions, closely tracking varying optimum, as well as improved convergence speed.