Bio-Inspired Algorithms Applied on Microstrip Patch ... · algorithms and their applications in the...

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.



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.



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]



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.



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



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].



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.



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.


3The King Abdullah II School for Electrical Engineering, PSUT,

Amman, Jordan.


4Electrical Engineering Department, Tafila Technical University,

Tafila. Jordan.


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

mailto:[email protected]






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.

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