1 research outputs found
Infinite-Horizon Optimal Control of Switched Boolean Control Networks with Average Cost: An Efficient Graph-Theoretical Approach
This study investigates the infinite-horizon optimal control problem for
switched Boolean control networks with an average-cost criterion. A primary
challenge of this problem is the prohibitively high computational cost when
dealing with large-scale networks. We attempt to develop a more efficient and
scalable approach from a graph-theoretical perspective. First, a weighted
directed graph structure called the
(OSTG) is established, whose edges encode the optimal action for each one-step
transition between states reachable from a given initial state subject to
various constraints. Then, we reduce the infinite-horizon optimal control
problem into a minimum mean cycle (MMC) problem in the OSTG. Finally, we
develop a novel algorithm that can quickly find a particular MMC by resorting
to Karp's algorithm in graph theory and construct afterward an optimal
switching and control law based on state feedback. Time complexity analysis
shows that our algorithm can outperform all existing methods in terms of time
efficiency. A 16-node signaling network in leukemia is used as a benchmark to
test its effectiveness. Results show that the proposed graph-theoretical
approach is much more computationally efficient: it runs hundreds or even
thousands of times faster than existing methods