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International Journal on Communications Antenna and Propagation (I.Re.C.A.P.), Vol. 6, N. 6
ISSN 2039 – 5086 December 2016
Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved DOI: 10.15866/irecap.v6i6.9737
336
Bio-Inspired Algorithms Applied on Microstrip
Patch Antennas: a Review
Omar A. Saraereh1, Amer A. Al Saraira
2, Qais H. Alsafasfeh
3, Aodeh Arfoa
4
Abstract – Design of small broadband, multiband and high-directivity microstrip patch antennas
(MPAs) is a challenging task for the antenna research community since the classical MPAs do not
perform well enough to be used in the real world applications. In this sense, various performance
improvement techniques such as stacked patches, air gaps, compact meandering geometries,
fractal-shapes and shorting pins are applied to design improved MPAs. Use of bio-inspired
algorithms along with these techniques is trending due to their capability of manipulating antenna
parameters to achieve optimized performance. Literature presents use of bio-inspired algorithms
such as Genetic algorithms (GA), Particle swarm optimization (PSO), Differential evolution (DE),
Invasive weed optimization (IWO), Wind driven optimization (WDO) and Ant colony optimization
(ACO) on MPAs for performance enhancement. This paper begins with an introduction to MPAs
followed by an analysis of the performance improvement techniques. Evolution of bio-inspired
algorithms and their applications in the field of MPAs are also presented. Based on the
compilation of studies, importance of applying multi-objective bio-inspired algorithms for
simultaneous optimization of multiple antenna parameters is emphasized. Further, research voids
in the field are revealed and direction is shown to design compact multifunctional MPAs.
Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved.
Keywords: Genetic Algorithms, Particle Swarm Optimization, Differential Evolution, Wind
Driven Optimization, Invasive Weed Optimization, Ant Colony Optimization,
Microstrip Patch Antennas
I. Introduction
Classical microstrip patch antennas (MPAs) consist of
a radiating patch etched on one side of a dielectric
substrate, which has a ground plane on the other side [1].
MPAs are widely used nowadays due to its low
volume, low cost, simple planar configuration and light
weight. However, the readily available classical MPAs
have some inherent limitations such as narrow
bandwidth, low gain and low efficiency. Throughout the
years, a significant amount of research has been done to
create new designs and to modify the original antennas to
achieve high performance in a single MPA element.
Various techniques have been proposed in the literature
to improve the properties such as resonant behavior,
directional properties and polarization pattern of MPAs.
Use of stacked patches, air gaps, compact meandering
geometries, fractal-shapes and shorting walls, strips or
pins are popular among them. They have proven to be
successful in developing MPAs with broadband,
multiband, miniature and high-directivity properties.
Rapidly growing wireless communication technology
urges MPAs that are suitable for numerous applications
ranging from small hand-held devices to wireless local
area networks. The performance of MPAs can be
enhanced by proper selection of materials, geometries
and dimensions.
Performance improvement techniques need to be
applied on MPAs to design such antenna parameters. The
state of the art method to design such MPAs is to model
several trial designs and to select the best out of a pool of
trial solutions. However, this selection criterion has
limitations as only a partial solution space is being
searched. As a result of it, the final antenna design may
not be the best out of the overall solution space.
In contrast, by applying the above mentioned
performance enhancement techniques along with bio-
inspired algorithms, the best performing MPA in the
solution space can be designed. Further, the use of bio-
inspired algorithms implemented as software is a fast and
effective way to draw MPAs automatically and perform
the simulations repeatedly.
It is more efficient and easier to implement than
feeding antenna parameters manually for each and every
design. Realizing these advantages, bio-inspired
algorithms are becoming popular in the field of MPAs.
Bio-inspired algorithms can be categorized as
evolutionary algorithms, swarm-based algorithms and
ecology-inspired algorithms according to the biological
phenomenon based on which they have been developed
(Fig. 1) [2]. Evolutionary algorithms are the
computational equivalents of natural selection.
They are heuristic and make complex problems more
tractable.
Omar A. Saraereh, Amer A. Al Saraira, Qais H. Alsafasfeh, Aodeh Arfoa
Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved Int. Journal on Communications Antenna and Propagation, Vol. 6, N. 6
337
Evolutionary Algorithms
[1] Genetic Algorithms
[2] Differential Evolution
Swarm-Based Algorithms
[3] Particle Swarm
[4] Ant Colony
[5] Bacterial foraging
[6] Artificial bee colony
[7] Cuckoo search
[8] Firefly
Ecology-Inspired Algorithms
[9] Wind Driven
[10] Invasive Weed
[11] Biogeography-based
BIO-INSPIRED ALGORITHMS
Fig. 1. Classification of bio-inspired algorithms applied on MPAs
Swarm-based algorithms were inspired by the social
behavior of colonized animals such as ants, bees, birds
and fish. In contrast, ecology-inspired algorithms were
introduced by studying natural ecosystems.
A vast literature exists on bio-inspired algorithms such
as Genetic Algorithms (GA), Particle Swarm
Optimization (PSO), Ant Colony Optimization (ACO),
Differential Evolution (DE), Wind Driven Optimization
(WDO) and Invasive Weed Optimization (IWO)
employed on the parameters of MPAs. This paper
reviews how such bio-inspired algorithms have been
applied on MPAs to tune antenna parameters.
In case of a rectangular shaped conventional patch, the
current flows along a straight line as shown in Fig. 2(a).
The direct flow of current can be disturbed by
inserting slits or slots generating a longer current path
[3]. As a result, the effective electrical length becomes
longer as illustrated in Fig. 2(b) making the MPA
resonate at a lower frequency.
Moreover, multiple resonating current paths can be
obtained leading to multiband operation by introducing
different shapes in the radiating element. Fig. 2(c) shows
a rectangular patch with a U-slot [4]. It performs dual
band operation, where lower and upper resonant
frequencies depend on dimensions of patch and U-slot
respectively. If the patch geometry is such that resonant
frequencies are close to each other, broadband
characteristics can be achieved. Due to these significant
scenarios, bio-inspired algorithms have been applied on
MPAs most commonly to etch different shapes on the
radiating element. Dimensions and locations of radiating
patch elements and non-conducting slots with various
shapes have been synthesized with the help of bio-
inspired algorithms. When the patch geometry becomes
non-conventional, finding the most suitable feed position
becomes a challenge. In this sense, bio-inspired
algorithms can be applied on MPAs to place the feed by
matching the feeding line to the input impedance.
This mechanism gives the control of the null voltage
point to the designer.
(a)
(b)
PatchSlot
Substrate
Probe feed
(c)
Figs. 2. Etching slots on the patch. (a) Straight current flow on a
classical rectangular patch [1]. (b) A slot on the patch placed
transversely to the electrical current lines creates an elongated current
path. [3]. (c) A rectangular patch with a U-slot for dual band
operation [4]
For an example, Figs. 3 show two antenna
configurations of an optimization problem which tuned
both the patch geometry and feed position
simultaneously. Figs. 3(a) shows the result of a
bandwidth optimization problem. When both bandwidth
and broadside gain have been optimized, the patch
geometry has changed resulting in a different feed
position as shown in Fig. 3(b). Similarly, shorting pins,
shorting walls or shorting strips can also be placed
connecting the patch to the ground plane (Figs. 4).
Insertion of a shorting pin adds inductance to the input
impedance at the feed point. But at higher order modes
electrical distance from the short circuit to the feed can
be such that it adds capacitance to the input impedance.
Omar A. Saraereh, Amer A. Al Saraira, Qais H. Alsafasfeh, Aodeh Arfoa
Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved Int. Journal on Communications Antenna and Propagation, Vol. 6, N. 6
338
(a)
(b)
Figs. 3. Antenna configurations with different feed positions [5].
(a) Single objective optimization of bandwidth. (b) Multi-objective
optimization of bandwidth and broadside gain
Thus, by suitably placing the shorting and feeding
positions, multiband operation at desired frequencies can
be achieved. When classical MPAs are modified to
achieve a single objective, its performance may decay by
means of other characteristics. For example, when the
patch geometry is modified to achieve bandwidth
enhancement, the radiation pattern may be distorted.
However, some real world applications require a wide
single band while some others need to be operated in
different frequency bands giving multiple services.
Further, MPAs need to be compact in order to be
integrated with other electronic components of small
hand held devices. Some MPAs need synthesizing a far-
field radiation pattern with side-lobe goals.
Concisely, synthesis of an MPA with multiple
objectives such as bandwidth enhancement, directivity
improvement and size reduction is interesting but
challenging as the objectives are conflicting.
In this sense, MPAs often require simultaneous
optimization of multiple objectives and bio-inspired
algorithms are becoming popular as suitable candidates.
Six types of bio-inspired algorithms that were
popularly applied on MPAs have been identified after a
thorough literature review. Sections II to VII give a
conceptual overview of aforementioned algorithms and
summarize their integration with classical performance
improvement techniques.
Coaxial
cable
Ground
planeShorting
wall
Patch
W
L
(a)
Shorting
strip
Coaxial
cable
Ground
plane
Patch
W
L
(b)
Coaxial
cable
Ground
plane
Shorting
pin
PatchL
W
(c)
Figs. 4. Side view of shorted MPAs [5].
(a) Shorting wall (b) Shorting strip (c) Shorting pin
Section VIII presents some more bio-inspired
algorithms, which have been applied rarely in the field of
MPAs. Comparison of algorithms and their recent
advances are presented in Section IX. Finally, Section X
concludes the findings.
II. Genetic Algorithms
Among different evolutionary algorithms, GA has
shown to be useful in various electromagnetic
applications [6]-[9] including design of MPAs. GA is
developed based on Darwin’s principle of evolution.
Charles Darwin formulated the principle of natural
selection, without having any knowledge about the
genetic mechanism.
Omar A. Saraereh, Amer A. Al Saraira, Qais H. Alsafasfeh, Aodeh Arfoa
Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved Int. Journal on Communications Antenna and Propagation, Vol. 6, N. 6
339
GA became popular through John Holland's work in
the early 1970s. The mathematical model presented by
him, supports nonlinearity of complex interactions.
He demonstrated the model's universality by applying
GA in numerous applications. With the developments of
Holland’s theory, GA emerged as a powerful mechanism
for solving optimization problems. Therefore, GA was
applied successfully to synthesize improved MPAs as an
alternative method to classical techniques.
Nearly 20 years ago, GA was applied to obtain
required radiation characteristics by optimizing a set of
metallic strips [6]. Few years later, GA based geometric
modeling of MPAs was presented [7]. Since then, GA
has been significantly used to introduce broadband [10]-
[23], multiband [19]-[37] and miniature [36]-[41] MPAs
for various applications. Further, MPAs with high-
directivity [42], [43] broadside radiation [44] and
increased gain [45] have been designed by using GA.
In most of the GA based studies, only patch geometry
has been considered as the designing parameter.
Literature reveals that most commonly the patch area of
MPAs is fragmented into rectangular or square cells and
conducting or non-conducting properties of each cell are
defined (Figs. 5). When GA is applied, conductivity of
each cell is represented as a gene.
A vector of genes is called a chromosome. In the
traditional method, the contact area between diagonally
adjoining cells is insignificant [38], [41]. It may create a
connection problem, when manufacturing the MPA by
using a chemical etching process.
To avoid this drawback, some techniques such as
generation of amorphous antenna shapes using ellipses
[18] and use of overlapping cells along the vertical axis
[32] are proposed (Figs. 6). When more antenna
parameters such as feeding and shorting positions,
substrate thickness and permittivity are designed, they
are defined by including more genes into the
chromosome. Another simple GA based designing
method is to tune both patch geometry and the feeding
position [40]-[43]. Figs. 7 demonstrate a multi-frequency
wideband MPA covering GSM1800, GSM1900, UMTS,
LTE2300, and Bluetooth bands [40]. Geometry of the
grid of cells with extra flexibility of non-uniform
overlapping and the position of the coaxial feed have
been optimized simultaneously with multiple objective
functions of wide bandwidth and broadside gain.
The optimized antenna resonates at three frequencies
due to three different current paths. As the resonating
frequencies are closer to each other, the PIFA exhibits
broadband performance. The optimization target has
been achieved after about 45 iterations. There are some
more research which indicate great promise in handling
multiple antenna parameters simultaneously. Ref [46]
presents optimization of the size and the feeding point of
an MPA by applying GA. In [31], positioning the slots
on the patch and shorting strips connecting the patch with
the ground has been performed. In [41], three antenna
parameters: patch geometry, feeding position and
shorting position have been tuned by employing GA.
(a)
(b)
Figs. 5. Antenna configuration of a GA based PIFA [29]. (a) Grid of
cells before applying an optimization algorithm. (b) Conducting or non-
conducting properties assigned after GA optimization
(a)
(b)
Figs. 6. Techniques to avoid insignificant contact between diagonally
adjoining cells. (a) Amorphous antenna shapes using ellipses [19].
(b) Overlapping cells [32]
Omar A. Saraereh, Amer A. Al Saraira, Qais H. Alsafasfeh, Aodeh Arfoa
Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved Int. Journal on Communications Antenna and Propagation, Vol. 6, N. 6
340
Ref [25] explores reduction of substrate thickness by
inserting shorting pins into proper positions, while
obtaining dual-band operation.
The MPA presented in [29] is a very compact
multiband antenna where the planar geometry and
feeding and shorting pin positions are obtained by
applying GA. Simultaneous optimization of the fractal
geometry and load parameters are presented in [37].
In [18], bandwidth has been improved by tuning four
antenna parameters: the patch geometry, feed position,
substrate thickness and material simultaneously.
Different designs that satisfy a predefined objective
have been presented by considering different
combinations of aforesaid parameters in the GA
optimization procedure. Likewise, GA is proven to be
suitable to handle complex MPA designing problems for
about last 15 years. Further, GA is remarkably useful
when the solution space is extremely large.
III. Particle Swarm Optimization
PSO is another bio-inspired algorithm which has been
used in the field of MPAs [47]-[49]. PSO is simpler and
more robust compared to other bio-inspired algorithms
like GA [49]. Even though PSO is relatively new to the
antennas and propagation community, it possesses
similar or higher capabilities as GA.
PSO is developed based on the movement and
intelligence of swarms; e.g. swarm of bees searching for
flowers, flock of birds searching for food. One example is a flock of birds in a field with the objective of finding
the location with the highest food density in a wood.
The birds start in random locations and fly with
random velocities searching foods. Occasionally, one
bird may find a location with more foods than had been
come across by any bird in the flock. Then the entire
flock will be attracted to that place in addition to their
own personal findings. Eventually, the birds will gather
to the place with the highest food concentration. PSO
was invented in 1995 by J. Kennedy and R. Eberhart in
attempting to model this behavior.
PSO was started to be used in the field of MPAs about
a decade ago. Therefore, in contrast to GA based MPAs,
lesser number of PSO based MPAs are found in the
literature. Potential of using PSO to synthesize ultra
wideband [50]-[53], broadband [54]-[55] and multiband
[55]-[57] MPAs have been explored. In most of the
work, manipulation of geometrical parameters is
apparent. Selecting the most appropriate antenna
dimensions was the objective of most of the problems,
instead of using a grid of cells [50]-[59]. Therefore,
radiating patch elements with simple geometries such as
an E-shape have been maintained by working on the
dimensions of the patch arms with the help of PSO [54].
For an example Figs. 8 demonstrate an E-shape patch
with the feed mounted on the central arm. It generates
two different current paths making the MPA resonates at
two frequencies. The resonating frequencies depend on
the dimensions of patch arms.
(a)
(b)
(c)
Figs. 7. GA optimization of an MPA [40]. (a) Antenna configuration.
(b) Convergence results. (c) Current patterns at three resonating
frequencies
By exploiting the frequency ratio, either dual-band or
broadband performance can be obtained.
In both simulations, the fitness values have converged
after about 500 iterations. In addition to that, slots were
cut on the patch by applying PSO to tune slot dimensions
[57]. In [60], only the feed position has been designed by
using PSO, while keeping the patch shape rectangular.
Omar A. Saraereh, Amer A. Al Saraira, Qais H. Alsafasfeh, Aodeh Arfoa
Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved Int. Journal on Communications Antenna and Propagation, Vol. 6, N. 6
341
Ref [61] proposes tuning of both dielectric constant
and thickness of the substrate, again on a rectangular
patch. Likewise, applying PSO in the field of MPAs
becomes popular increasingly among the antenna
research community.
IV. Differential Evolution
DE algorithm was first proposed by R. Storn and K.
Price in 1995 [62]. It is a population-based stochastic
bio-inspired algorithm for the optimization of variables
in multi-dimensional spaces. Similar to GA, DE is
modeled based on natural selection and genetic pressure.
Also a competitive mechanism is applied on
populations of individuals in the optimization procedure.
However, it uses a differential operator for
regenerating broods in the next generation. DE is a
global optimizer and facilitates escaping from local
minima due to hill-climbing features. DE can be easily
integrated with gradient-based optimization tools. In DE,
physical constraints or presumptive knowledge about the
problem can be introduced in an uncomplicated manner.
DE has also been applied in the field of electromagnetics,
but with few publications on DE based MPAs [63].
DE has been applied to synthesize broadband [64]-
[66], high-gain [67] and miniature [68] MPAs. The most
common approach is to design antenna geometric
parameters [65]-[67].
Dimensions of patch elements and slots have been
optimized by creating simple shapes without using a grid
of cells. In [69], feed position as well as the antenna
dimensions has been determined by using DE to make
the MPA operates over the given bandwidth. In [68],
optimization of positions of feeding probe and shorting
pin with the objective of reducing the size is presented.
However, as a result of not controlling the radiation
characteristics in the optimization process, the radiation
pattern of the compacted MPA has become distorted as
indicated in Figs. 9. In this sense, Applying DE on MPAs
is an interesting topic that needs to be explored more by
the antenna research community.
V. Invasive Weed Optimization
Some researchers have had success in implementing
IWO on MPAs. It is another clever technique that has
been applied in the field of electromagnetics and has
started to be applied on MPAs few years ago [74].
It is used to achieve dual-band operation [74],
bandwidth enhancement [75]-[77] and symmetrical
radiation pattern [77]. Use of IWO algorithm has been
limited to derive the most suitable dimensions of MPAs
with simple shapes [74], [76].
For an example, Figs. 10 show an MPA designed to be
resonated at 5.8 GHz, by optimizing an E-shape patch
and the feeding location. Despite the fact that IWO was
introduced recently, its capability to design improved
MPAs has been proven and more research opportunities
exist for its use in multi-objective optimization.
IWO algorithm was introduced by A.R. Mehrabian
and C. Lucas in 2006 by modeling the colonizing
behavior of weeds. It considers spreading seeds on a
field. All seeds grow to flowering plants and produce
seeds proportional to their suitability. The seeds of the
next generation are being spread arbitrarily over the
region of exploration and they grow to new plants. The
weeds grow their population over a geographically
specified area. The plants are removed by considering
their adaptability.
(a)
(b)
(c)
Figs. 8. Apply PSO on an E-shaped MPA to design antenna
dimensions. [55] (a) Antenna configuration showing the dimensions to
be optimized. (b) Dual-band MPA with optimized dimensions.
(c) Convergence results of the dual-band MPA
Omar A. Saraereh, Amer A. Al Saraira, Qais H. Alsafasfeh, Aodeh Arfoa
Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved Int. Journal on Communications Antenna and Propagation, Vol. 6, N. 6
342
(a)
(b)
Figs. 9. Apply DE to design an MPA with reduced size. [68]
(a) Shorted MPA. (b) Distorted radiation pattern
When this process is repeated, less suitable candidates
are removed while more suitable candidates dominate.
VI. Wind Driven Optimization
The WDO is a novel bio-inspired global optimization
algorithm which was developed based on motion of wind
in the atmosphere. This technique has been invented by
Z. Bayraktar with his initial idea of modeling wind
moving from high to low pressure points. It models a set
of microscopic air parcels travels over a search space
following the Newton's second law of motion.
This is similar to the flow of air within the earth's
atmosphere. It is mapped to the optimization where we
want to move from low performing combinations to high
performing combinations within a search space.
Compared to other particle based algorithms, WDO is
robust and provides extra degrees of freedom to fine-tune
the optimization. WDO algorithm has also been applied
in the field of electromagnetics [71]-[72]. However,
applying DE on MPAs has been started recently. It has
been used to design MPAs with dual resonance [72] and
high broadside gain [73]. In the related literature, only
the geometrical parameters of MPAs have been tuned by
applying WDO [72], [73]. However, this may ignore a
better design, which can be obtained by simultaneously
tuning several parameters such as feeding position,
shorting position, substrate thickness and material. This
algorithm is also novel to the field of antennas and
designing MPAs by working on multiple parameters are
yet to be explored on.
(a)
(b)
Figs. 10. Design of the patch shape by using IWO [76]
(a) Antenna configuration. (b) Convergence of patch length L, the
patch width W
VII. Ant Colony Optimization
Some bio-inspired algorithms model how certain
animals optimize their path to find food. For an example,
in an ant colony, ants optimize their route from the nest
to the food. ACO is a swarm-based algorithm that has
been developed based on this phenomenon. Initially, the
ants walk randomly.When an ant finds food, it goes back
to the nest leaving pheromones on the path. When other
ants find the pheromones, they preferably follow the
path. Since the ants leave pheromones every time they
bring food, shorter paths become stronger. Once the food
source is diminished, the route becomes less popular
gradually. M. Dorigo & G. Di Caro introduce the ACO
algorithm in 1999. Applications of ACO algorithm
started trending in various fields of including
electromagnetics more than a decade ago [78]. However,
it has not become popular among the antenna research
community to be applied on MPAs. ACO algorithm has
been used to synthesize multiband MPAs by designing
the geometry of the radiating patch [79] or that of both
the patch and the ground plane [80].
Omar A. Saraereh, Amer A. Al Saraira, Qais H. Alsafasfeh, Aodeh Arfoa
Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved Int. Journal on Communications Antenna and Propagation, Vol. 6, N. 6
343
VIII. Other Bio-Inspired Algorithms
Some rather newly developed bio-inspired algorithms
such as bacterial foraging [81], biogeography-based
optimization [82], artificial bee colony [83], cuckoo
search [84] and firefly algorithm [85] have also been
applied on MPAs to obtain significant performance
improvements.
Bacterial foraging optimization algorithm which was
inspired by the foraging phenomenon of a bacterial
colony was developed by K. M. Passino in 2002.
Biogeography-based optimization algorithm was
developed by Dan Simon in 2008 based on the idea of
immigration and emigration of species between habitats.
Artificial bee colony algorithm models the intelligent
foraging behavior of the bees. It was introduced by D.
Karaboga and B. Basturk in 2007. Firefly algorithm was
proposed by X.S Yang in 2008, by simulating the
flashing behavior of fireflies.
Cuckoo search algorithm was also developed by X.S.
Yang jointly with S. Deb in 2009. This algorithm was
developed based on the fact that cuckoos lay eggs on
hosts nest and the eggs are hatched to chicks if they are
not detected and destroyed.
IX. Discussion
Devices with wireless communication facilities such
as mobile phones, laptops, tablets and WLAN systems
have become more compact and operational in different
frequency bands giving multiple services. As a result, the
antennas need to be low profile and integrable with other
electronic components along with multiband and high
gain features. MPAs provide excellent solutions in this
regard due to their advanced physical and mechanical
properties.
However, the need for high performance antennas
often combined with volume constraints in diverse
wireless devices has made the design of MPAs a
challenging task. Therefore, over several decades,
numerous techniques have been applied to overcome
well-known drawbacks of MPAs. In the recent past, bio-
inspired algorithms have been applied with an explosive
growth to tackle this problem systematically.
This review is helpful in understanding the
development trends of bio-inspired algorithms applied on
MPAs. Bio-inspired algorithms have been derived by
researchers as a result of long term studies of various
phenomena in the nature such as social behavior of
animals, evolution of natural systems and biological
processes.
As a result, presenting numerous design solutions and
strategies by applying bio-inspired algorithms instead of
cumbersome trial-and-error methods has become a
promising approach in the field of MPAs. Among the
series of bio-inspired algorithms reviewed in this paper,
most of the researchers tend to favor GA for about two
decades. Multi-objective GA optimization of a grid of
patch cells demonstrates the dynamics of GA in
synthesizing MPAs which fulfill the real industrial needs.
It is the most effectively applied bio-inspired
algorithm in combination with conventional performance
enhancement techniques. The other evolutionary
algorithm found to be applied on MPAs is DE algorithm.
Both of these population-based stochastic search
algorithms have been developed two decades ago based
on survival-of-the-best criteria. They differ slightly from
each other. However, DE algorithm has not attracted
antenna researchers like GA has done.
Even though PSO also has a long history, its
applications in the field of MPAs have started to appear
in the recent past. Still being young, a considerable
amount of publications demonstrates the suitability of
PSO on MPAs, mainly due to its algorithmic simplicity.
However, GA slightly outperforms PSO for complex
synthesis. In this context, use of patch conductors with
simple shapes in PSO has been proposed instead of a grid
of cells. Both PSO and ACO have been developed based
on the social behavior of animals searching for foods.
ACO has not been implemented on MPAs with full
exploration and not been developed to design amorphous
patches with simultaneous optimization of feeding and
shorting positions.
The other swarm-based algorithms; bacterial foraging,
artificial bee colony, cuckoo search and firefly
algorithms are also much novel to the field of MPAs and
have not been applied with a broad scope.
Among ecology-inspired algorithms, IWO has been
applied on MPAs for multi-objective optimization.
Applying IWO on a grid of cells to design the patch
along with different combinations of other antenna
parameters is pending. Implementation of newly emerged
algorithms such as WDO and biogeography-based
algorithm is also limited to geometrical optimization of
MPAs.
The review depicts that higher degrees of freedom,
which is the main advantage of algorithms, allows
developing new design solutions. Therefore, approaches
involving bio-inspired algorithms to solve
multidimensional optimization of multifunctional MPAs
are worth to be employed.
X. Conclusion
This review is a comprehensive reference for research
on improving performance of MPAs by applying bio-
inspired algorithms. Bio-inspired algorithms have been
successfully applied on MPAs to achieve the desired
goals by means of different factors such as resonant
behavior, gain, directivity, polarization and efficiency.
The motivation behind application of bio-inspired
algorithms on MPAs is its ability to address the
challenges and requirements presented by variety of
sophisticated wireless systems than that of classical
performance enhancement techniques.
This review presents an interesting comparative study
among various algorithms and a conclusion based on a
thorough review.
Omar A. Saraereh, Amer A. Al Saraira, Qais H. Alsafasfeh, Aodeh Arfoa
Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved Int. Journal on Communications Antenna and Propagation, Vol. 6, N. 6
344
Accordingly, GA is the most commonly applied bio-
inspired algorithm followed by PSO and DE
sequentially. GA based pioneering works have been
presented by optimizing various antenna parameters,
such as geometry of patch and/or ground plane, feeding
and shorting positions, substrate thickness and material,
individually or in combination.
However, full potential of PSO and DE paradigms has
not been used in the designing process and the solution
space has been explored partially most of the time. ACO,
WDO and IWO are still in their infancy and unable to
synthesize non- intuitive solutions.
There exist other bio-inspired algorithms which have
been proven to be suitable for MPA designing but again
with seldom usage. However, most of the algorithms
based MPAs outperform state of the art designs. In this
context, filling the voids in the field by creative antenna
designers will be an exciting suggestion for the future
advancement in the field.
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Authors’ information 1Department of Electrical Engineering, The Hashemite University,
Zarqa, Jordan.
E-mail: [email protected]
2Department of Biomedical Engineering, The Hashemite University,
Zarqa, Jordan.
E-mail: [email protected]
3The King Abdullah II School for Electrical Engineering, PSUT,
Amman, Jordan.
E-mail: [email protected]
4Electrical Engineering Department, Tafila Technical University,
Tafila. Jordan.
E-mail: [email protected]
Omar A. Saraereh initially qualified as a
Telecommunication Engineer 1999 from
Mu'TAH University, Jordan; he then obtained a
Master of Science Degree in Digital
Communication Systems from Loughborough
University in England. In 2005 he completed his
PhD in Electrical and Electronic
Engineering/Mobile Communications from
Loughborough University, England. During the period 2001-2005 he
also was a member of staff at Centre for Mobile Communication
Research in Loughborough University/England. Dr. Saraereh has Over
12 years of academic and practical experience in Electrical
Engineering, Mobile Communications, Various Antennas Design,
Fabrication & Measurements, Radiation Hazards and Health Effects,
and Wireless Communications. Dr. Saraereh has published many
papers in various international journals and conferences. He has also
worked as a high level consultant and a Turn Key Solution Originator
in countless business and charitable sectors as well as an international
public speaker and trainer on a variety of business and people
management topics. Currently Dr. Saraereh is an associate professor in
the Department of Electrical Engineering at The Hashemite
University/Jordan.
Amer A. Alsaraira initially qualified as a
Telecommunication Engineer 2001 from
Mu'TAH University, Jordan; he then obtained a
Master of Biomedical Engineering from Monash
University, Australia, 2003. In 2009 he
completed his Ph.D. degree in Biomedical
engineering from Monash University, Australia.
He is currently an Assistant Professor in the
Biomedical Engineering Department at the Hashemite University
(Jordan). His research focuses on bioinstrumentation, biotelemetry,
biomechanics, and modeling and simulation of biomedical systems. He
participated in many local, regional, and international conferences to
share ideas with other scientists in his field around the world.
Qais H. Alsafasfeh is Associate Professor of
Electrical Engineering/Energy and control, and
a certified energy manager. He completed his
Ph.D. in 2010 at Western Michigan University.
His Ph.D. research was supported by Western
Michigan University, and received a research
grant from Australian Endeavour and European
Commission for his post-Doctoral research. He
has six years of academic teaching experience of energy and energy-
related courses and published over 30 articles in International energy
conferences and first-class International energy journals. Dr. Alsafasfeh
also has two years of academic management experience as Head of the
Electrical Engineering Department, and four years as a Director of
Clean Energy Research Center. He received several local and
international awards and research grants. Dr. Alsafasfeh has excellent
working relations with local, regional, and international researchers,
policy makers, and entrepreneurs. He is an authorized trainer by the
Association of Energy Engineers (TUV). Dr. Alsafasfeh also has a
strong industrial experience. He managed and supervised numerous
energy and environmental audits and studies in Jordan and the region
also he has excellent practical experience in 132/33/11kV substation
Omar A. Saraereh, Amer A. Al Saraira, Qais H. Alsafasfeh, Aodeh Arfoa
Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved Int. Journal on Communications Antenna and Propagation, Vol. 6, N. 6
347
maintenance, erection and testing and also in switching operations of
electricity network for 400kV, 132kV, 33kV, 11kV and 0.4kV. He is a
member of IEEE, IASTED, IREO, and JRES.
Aodeh Arfoaa was born in 1963, Tafila Jordan.
Obtained a degree of bachelor in electrical
engineering in 1989 from Kiev Technical
University then a master of electrical station in
1990. Head division of electrical maintenance
department in Jordan Lafarge cement factory.
Dr. Arfoaa obtained a PhD in electrical
engineering, catastrophe theory to determine the
stability of the power system in 2003. Now he is a head division of
electrical engineering department in Tafila Technical University. The
field of research, electrical load forecasting, stability of the power
system.