  We develop an optimization algorithm, using simulated annealing for the
quantification of patterns in astronomical data based on techniques developed
for robotic vision applications. The methodology falls in the category of cost
minimization algorithms and it is based on user-determined interaction - among
the pattern elements - criteria that define the properties of the sought
structures. We applied the algorithm on a large variety of mock images and we
constrained the free parameters; {\alpha} and k, which express the amount of
noise in the image and how strictly the algorithm seeks for cocircular
structures, respectively. We find that the two parameters are interrelated and
also that, independently of the pattern properties, an appropriate selection
for most of the images would be log(k) = -2 and 0 < {\alpha} \lesssim 0.04. The
width of the effective {\alpha}-range, for different values of k, is reduced
when more interaction coefficients are taken into account for the definition of
the patterns of interest. Finally, we applied the algorithm on N-body
simulation dark-matter halo data and on the HST image of the lensing Abell 2218
cluster to conclude that this versatile technique could be applied for the
quantification of structure and for identifying coherence in astronomical
patterns.
