Abstract: In this paper a hybrid technique of Genetic Algorithm
and Simulated Annealing (HGASA) is applied for Fractal Image
Compression (FIC). With the help of this hybrid evolutionary
algorithm effort is made to reduce the search complexity of matching
between range block and domain block. The concept of Simulated
Annealing (SA) is incorporated into Genetic Algorithm (GA) in order
to avoid pre-mature convergence of the strings. One of the image
compression techniques in the spatial domain is Fractal Image
Compression but the main drawback of FIC is that it involves more
computational time due to global search. In order to improve the
computational time along with acceptable quality of the decoded
image, HGASA technique has been proposed. Experimental results
show that the proposed HGASA is a better method than GA in terms
of PSNR for Fractal image Compression.
Abstract: The main goal of this work is to propose a way for
combined use of two nontraditional algorithms by solving topological
problems on telecommunications concentrator networks. The
algorithms suggested are the Simulated Annealing algorithm and the
Genetic Algorithm. The Algorithm of Simulated Annealing unifies
the well known local search algorithms. In addition - Simulated
Annealing allows acceptation of moves in the search space witch lead
to decisions with higher cost in order to attempt to overcome any
local minima obtained. The Genetic Algorithm is a heuristic approach
witch is being used in wide areas of optimization works. In the last
years this approach is also widely implemented in
Telecommunications Networks Planning. In order to solve less or
more complex planning problem it is important to find the most
appropriate parameters for initializing the function of the algorithm.