Reference
J. M. van Ast, R. Babuška, and B.
De Schutter, "Novel ant colony optimization approach to optimal control,"
International Journal of Intelligent Computing and
Cybernetics, vol. 2, no. 3, pp. 414-434, 2009.
Abstract
Purpose - In this paper, a novel Ant Colony Optimization (ACO) approach
to optimal control is proposed. The standard ACO algorithms have proven to be
very powerful optimization metaheuristic for combinatorial optimization
problems. They have been demonstrated to work well when applied to various
NP-complete problems, such as the traveling salesman problem. In this paper,
ACO is reformulated as a model-free learning algorithm and its properties are
discussed.
Design/methodology/approach - First, it is described how quantizing the
state space of a dynamic system introduces stochasticity in the state
transitions and transforms the optimal control problem into a stochastic
combinatorial optimization problem, motivating the ACO approach. The algorithm
is presented and is applied to the time-optimal swing-up and stabilization of
an underactuated pendulum. In particular, the effect of different numbers of
ants on the performance of the algorithm is studied.
Findings - The simulations show that the algorithm finds good control
policies reasonably fast. An increasing number of ants results in increasingly
better policies. The simulations also show that although the policy converges,
the ants keep on exploring the state space thereby capable of adapting to
variations in the system dynamics.
Research limitations/implications - This research introduces a novel
ACO approach to optimal control and as such marks the starting point for more
research of its properties. In particular, quantization issues must be studied
in relation to the performance of the algorithm.
Originality/value - The work presented is original as it presents the
first application of ACO to optimal control problems.
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BibTeX
@article{vanBab:09-009,
author = {van Ast, Jelmer M. and Babu{\v{s}}ka, Robert and De Schutter,
Bart},
title = {Novel Ant Colony Optimization Approach to Optimal Control},
journal = {International Journal of Intelligent Computing and Cybernetics},
volume = {2},
number = {3},
pages = {414--434},
year = {2009}
}