Ant Colony Learning Algorithm for Optimal Control

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.

A parallel numerical method for solving optimal control problems based on whale optimization algorithm

“…Then the ACO is used to solve the resulting quasi-assignment problem. Further works was documented in [22], where ACO has been implemented to design state feedback controllers. Parallel processing version of the method of [3] was also introduced in [11] for message passing and for shared memory systems.…”

“…3-They restricted to some special forms of dynamical systems or input signals ( [18,22,4,5] for example).…”

Solving Optimal Control Problem Using Max-Min Ant System

“…A novel framework called Ant Colony Learning (ACL) proposed for finding optimal control policies. In this method ants work with each other to learn optimal control policies collectively [5].…”