Ant colony learning algorithm for optimal control

Reference:
J.M. van Ast, R. Babuska, and B. De Schutter, "Ant colony learning algorithm for optimal control," in Interactive Collaborative Information Systems (R. Babuska and F.C.A. Groen, eds.), vol. 281 of Studies in Computational Intelligence, Berlin, Germany: Springer, ISBN 978-3-642-11687-2, pp. 155-182, 2010.

Abstract:
Ant Colony Optimization (ACO) is an optimization heuristic for solving combinatorial optimization problems and it is inspired by the swarming behavior of foraging ants. ACO has been successfully applied in various domains, such as routing and scheduling. In particular, the agents, called ants here, are very efficient at sampling the problem space and quickly finding good solutions. Motivated by the advantages of ACO in combinatorial optimization, we develop a novel framework for finding optimal control policies that we call Ant Colony Learning (ACL). In ACL, the ants all work together to collectively learn optimal control policies for any given control problem for a system with nonlinear dynamics. In this chapter, we will discuss the ACL framework and its implementation with crisp and fuzzy partitioning of the state space. We demonstrate the use of both versions in the control problem of two-dimensional navigation in an environment with variable damping and discuss their performance.