Ant Colony Optimization: Principle, Convergence and Application

Abstract

Ant Colony Optimization (ACO) is a meta-heuristic algorithm for the approximate solution of combinatorial optimization problems that has been inspired by the foraging behaviour of real ant colonies. In this Chapter, we present a novel approach to the convergence proof that applies directly to the basic ACO model, and a kind of parameters tuning strategy for nonlinear PID(NLPID) controller using a grid-based ACO algorithm is also presented in detail. A series of simulation experimental results are provided to verify the performance the whole control system of the flight simulator with the grid-based ACO algorithm optimized NLPID.