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.
Abstract: This paper presents the optimal controller design of
the generator control unit in the aircraft power system. The adaptive
tabu search technique is applied to tune the controller parameters
until the best terminal output voltage of generator is achieved. The
output response from the system with the controllers designed by the
proposed technique is compared with those from the conventional
method. The transient simulations using the commercial software
package show that the controllers designed from the adaptive tabu
search algorithm can provide the better output performance compared
with the result from the classical method. The proposed design
technique is very flexible and useful for electrical aircraft engineers.
Abstract: Optimal routing in communication networks is a
major issue to be solved. In this paper, the application of Tabu Search
(TS) in the optimum routing problem where the aim is to minimize
the computational time and improvement of quality of the solution in
the communication have been addressed. The goal is to minimize the
average delays in the communication. The effectiveness of Tabu
Search method is shown by the results of simulation to solve the
shortest path problem. Through this approach computational cost can
be reduced.
Abstract: The aim of this paper is to express the input-output
matrix as a linear ordering problem which is classified as an NP-hard
problem. We then use a Tabu search algorithm to find the best
permutation among sectors in the input-output matrix that will give
an optimal solution. This optimal permutation can be useful in
designing policies and strategies for economists and government in
their goal of maximizing the gross domestic product.
Abstract: This paper demonstrates the application of craziness based particle swarm optimization (CRPSO) technique for designing the 8th order low pass Infinite Impulse Response (IIR) filter. CRPSO, the much improved version of PSO, is a population based global heuristic search algorithm which finds near optimal solution in terms of a set of filter coefficients. Effectiveness of this algorithm is justified with a comparative study of some well established algorithms, namely, real coded genetic algorithm (RGA) and particle swarm optimization (PSO). Simulation results affirm that the proposed algorithm CRPSO, outperforms over its counterparts not only in terms of quality output i.e. sharpness at cut-off, pass band ripple, stop band ripple, and stop band attenuation but also in convergence speed with assured stability.
Abstract: Motion estimation is the most computationally
intensive part in video processing. Many fast motion estimation
algorithms have been proposed to decrease the computational
complexity by reducing the number of candidate motion vectors.
However, these studies are for fast search algorithms themselves while
almost image and video compressions are operated with software
based. Therefore, the timing constraints for running these motion
estimation algorithms not only challenge for the video codec but also
overwhelm for some of processors. In this paper, the performance of
motion estimation is enhanced by using Intel's Streaming SIMD
Extension 2 (SSE2) technology with Intel Pentium 4 processor.