30-08-2014, 03:36 PM
Current Search and Applications in Analog Filter Design Problems
Current Search and Applications.pdf (Size: 362.79 KB / Downloads: 32)
Abstract
The AI (artificial intelligent) search techniques have become acceptable worldwide as the most efficient and powerful
tools for various real-world problems. In this paper, a novel meta-heuristic optimization algorithm named the CS (current search) is
proposed. The CS is inspired by an electric current flowing through electric networks. Performance of the CS is evaluated against six
benchmark surface optimization problems. Results obtained by the CS are compared with those obtained by the popular search
techniques widely used to solve combinatorial optimization problems, i.e., the GA (genetic algorithm), the PSO (particle swarm
optimization), and the ATS (adaptive tabu search). As results, it could be concluded that the CS outperforms other algorithms.
Results obtained by the CS are superior within reasonable time consumed. Finally, the proposed CS is applied to solve analog filter
design problems.
Introduction
Many heuristic and meta-heuristic algorithms have
been developed and launched to solve combinatorial
and numeric optimization problems over five decades
[1]. By literature, several intelligent search techniques,
i.e., EP (evolutionary programming) [2], TS (tabu
search) [3], SA (simulated annealing) [4], GA (genetic
algorithm) [5], ACO (ant colony optimization) [6],
HNR (hit-and-run) [7], HNS (hide-and-seek) [8], PSO
(particle swarm optimization) [9], HS (harmony
search) [10], BFO (bacterial foraging optimization)
[11], SFLA (shuffled frog leaping algorithm) [12],
ATS (adaptive tabu search) [13], BCO (bee colony
optimization) [14], KCS (key cutting search) [15], and
HuS (hunting search) [16] etc., have been proposed.
These algorithms can be classified into differentgroups depending on their nature of criteria being
considered, such as population-based (EP, GA, ACO,
PSO, BFO, BCO, and HuS), neighborhood-based (TS
and ATS), iterative-based (SFLA), stochastic (KCS,
HNR, and HNS), and deterministic (SA). Among
them, GA, PSO, and ATS are the popular intelligent
search techniques widely used to solve optimization
and engineering problems.
Current Search Algorithm
In electric circuit theory [29], based on the principle
of current divider, the electric current flows through
all blanch connected in parallel form as can be seen in
Fig. 1. Each blanch connects to a resistor R having
different resistances (assume that 0< R1 < R2 << RN)
to obstruct the current. One of the fundasmental in
circuit theory, KCL (kirchhoff’s current law) stats that
the algebraic sum of currents entering a node is zero.
On the other hand, the sum of the currents entering a
node is equal to the sum of the current leaving the
node. This means that, in Fig. 1, the sum of all
currents in each blanch is equal to the total current
supplied by the current source as expressed in Eq. (1),
where, iT is the total current and ij is the current in
blanch j-thThe behavior of electric current is like a tide that
always flow to lower places. The less the resistance of
blanch, the more the current flows. Referring to Fig. 1,
the thickness of arrows represents the current quantity.
In case of short circuit, the blanch resistance is zero
acted as a conductor, while in case of open circuit, the
blanch resistance is infinity acted as an insulator
Benchmark Surface Optimization
In this section, six well-known benchmark surface
optimization problems collected by Ali et al. [17] are
described as follows. Details of all tested functions are
summarized in Table
CS-Based Analog Filter Design
In circuit theory, a filter is an electrical network that
is designed to pass signals with desired frequencies
and reject or attenuate others [29]. As a
frequency-selective device, a filter is widely used in
circuit electronics, signal processing, and
communication systems. Generally, there are four
classic analogue filter types, i.e. Butterworth,
Chebyshev, Elliptic, and Bessel. All types of filters
mentioned can be performed to be lowpass, highpass,
bandpass, and band stop (or notch) filters. However,
there is no ideal filter. Each type is good in some areas
but poor in others. The Butterworth filter has a flatter
pass band region. This means that it has the least
attenuation over the desired frequency range.
However, it has a poor roll-off rate. The Bessel filter
has the flattest bandpass, but it has the worst roll-off
rate. The Chebyshev filter has a steeper roll-off, while
the Elliptic filter has the steepest roll-off. However
Conclusions
The development of the CS (current search), one of
the novel meta-heuristic optimization algorithms, has
been proposed in this paper. The CS algorithm is
inspired by an electric current flowing through electric
networks. The effectiveness and robustness of the CS
have been evaluated via six benchmark surface
optimization problems. Results obtained by the CS are
compared with those obtained by the GA, PSO, and
ATS. As results, it could be concluded that the CS
outperforms other algorithms. The proposed CS is
then applied to obtain optimum lowpass and highpass
analog filters. Compared with the Butterworth, the
filters obtained by the CS perform better performance
for overall regions. The very satisfactory results
obtained confirm the effectiveness and the usefulness
of the proposed CS algorithms.