Fuzzy Ant Colony Optimization for optimal control

Abstract

Ant Colony Optimization (ACO) has proven to be a very powerful optimization heuristic for Combinatorial Optimization Problems. While being very successful for various NP-complete optimization problems, ACO is not trivially applicable to control problems. In this paper a novel ACO algorithm is introduced for the automated design of optimal control policies for continuous-state dynamic systems. The so called Fuzzy ACO algorithm integrates the multi-agent optimization heuristic of ACO with a fuzzy partitioning of the state space of the system. A simulated control problem is presented to demonstrate the functioning of the proposed algorithm.

Applications of Ant Colony Optimization in Control Systems, Robotics and Vision

“…This extension of routing path is a feature of ant colony algorithm for data aggregation suggested in [23]. It was concluded on the basis of simulation results that the presented mechanism in [22] consumes less energy than the traditional data aggregation method such as DD and GIT method.…”

“…The amount of data to be transmitted is reduced. [22] proposes an ant colony algorithm for data aggregation in wireless sensor networks with the help of constructing data aggregation tree in a wireless sensor network for a group of source nodes to send sensory data to a single sink node. This method proposes extension of routing paths which in turn increases the probability of intersection of routing paths.…”

Ant Colony Optimization with Parameter Adaptation Using Fuzzy Logic for TSP Problems

“…There are also some other improvements in ant colony optimization in the literature, for example: Van Ast et al [19] proposed an ACO algorithm that has a fuzzy partitioning of the state space of the system, Yu et al [20] proposed an ACO with fuzzy pheromone laying mechanism, Einipour [7] a fuzzy-ACO method for detect breast cancer, Elloumi et al [8] proposed an hybridation of fuzzy PSO and fuzzy ACO applied to TSP problems, Khan and Engelbrecht [12] proposed a fuzzy ant colony optimization for topology design of distributed local area networks.…”

Ant colonies for Temporal Logic falsification of hybrid systems

“…These PDFs represent the pheromone distribution over the solution space. In [23], the authors present Fuzzy ACO for optimal control. In Fuzzy ACO, the continuous optimization variables are also split into a number of regions.…”

“…In Fuzzy ACO, the continuous optimization variables are also split into a number of regions. However, in [23], the authors use fuzzy functions in order to parameterize the search space while we use splines.…”