International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014
DOI : 10.5121/ijaia.2014.5404 51
OUT PERFORMANCE OF CUCKOO SEARCH
ALGORITHM AMONG NATURE INSPIRED
ALGORITHMS IN PLANAR ANTENNA
ARRAYS
A.Sai Charan1
, N.K.Manasa2
, Prof. N.V.S.N. Sarma3
1, 2, 3
Department Of Electronics and Communication Engineering,
National Institute of Technology, Warangal
ABSTRACT
In this modern era a great deal of metamorphism is observed around us which eventuate due to some
minute modifications and innovations in the area of Science and Technology. This paper deals with the
application of a meta heuristic optimization algorithm namely the Cuckoo Search Algorithm in the design
of an optimized planar antenna array which ensures high gain ,directivity, suppression of side lobes,
increased efficiency and improves other antenna parameters as well[1], [2] and [3].
KEYWORDS
Meta-Heuristic, Side Lobe Suppression, Gain, Directivity, Side Lobe Level (SLL).
1. INTRODUCTION
Antenna optimization techniques have made a breakthrough in the Communication domain. They
have contributed vividly to modern wireless communications in the form of smart antennas which
are antenna arrays that adjust their own beam pattern to accentuate signals of interest and
concurrently reducing the radio frequency interference. In the field of antennas, Cuckoo Search
Algorithm (CSA) was first applied for side lobe suppression in linear antenna array by distance
modulation.
Large arrays are complex to build, have increased fabrication and set up cost and are heavier at
the same time. Therefore reducing antenna element weight from the array is desirable without
degrading the performance of the array. But here we are not reducing the mass of the antenna
array elements, only the weight of the antenna elements(current)are adjusted in order to achieve
minimum side lobe level.
We opt a technique based on density tapering to lower side- lobes in the array by monotonically
decreasing the magnitude of weights away from the centre of the array.
2. REVIEW OF VARIOUS TECHNIQUES
Owing to high adaptability and ability to optimize multi-dimensional problems, several
evolutionary algorithms have been proposed such as Particle Swarm Optimization (PSO),
International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014
52
Invasive Weed Optimization (IWO), Genetic Algorithm (GA), etc. These algorithms are
associated with following drawbacks which make them unreliable.
i) The PSO could not work out the problem of scattering and optimization.
ii) The IWO require the genes of minimum one parent species to be forwarded to next generation
and
iii) The GA has a poor fitness function which generates bad chromosome blocks in spite of the
fact that only good chromosome blocks cross over. Also no assurance is given whether the GA
will find a global optimum solution [4].
This paper has explored a choice of antenna array synthesis, the (CSA) [5], to overcome the
above mentioned problems and to yield promising results.
3. PLANAR ARRAYS
Planar array is a two dimensional configuration of elements arranged to lie in a plane. The planar
array may be thought of as an array of linear arrays. The elements are arranged in a matrix form
having a phase shifter. The planar arrangement of all antenna elements forms the complete
phased array antenna. There are wide spread applications of planar antenna arrays which involve
the suppression of side lobes. The signals radiated by individual antennas determine the effective
radiation pattern of the array. They are used to point a fixed radiation pattern or to scan a region
rapidly in the azimuthal plane. Several methods have been developed for the design of planar
antenna array but all those methods pertain to other nature inspired optimization algorithms.
Planar antenna array optimization has been implemented earlier using Fuzzy GA [6]. Direction
angle (reference angle) is considered with the plane of planar antenna array. This paper deals with
the design of a planar antenna array by using CSA.
4. CUCKOO SEARCH ALGORITHM (CSA)
CSA is one of the modern nature inspired meta-heuristic algorithms. The Greek terms “meta” and
“heuristic” refer to “change” and “discovery oriented by trial and error” respectively. Various
techniques are used to minimise the constraints associated with the problem in order to obtain a
global optimum solution.
Cuckoos are attractive birds. The attractiveness is owing to the beautiful sounds produced by
them and also due to their reproduction approach which proves to be combative in nature. These
birds are referred to as brood parasites as they lay their eggs in communal nests. They remove the
eggs in the host bird nest in order to increase the hatching probability of their own eggs.
There are three types of brood parasites - the intraspecific brood parasite, cooperation breed and
nest take over type. The host bird involves in direct combat with the encroaching cuckoo bird. If
the host bird discovers the presence of an alien egg, it either throws away the egg or deserts the
nest. Some birds are so specialized that they have the characteristic of mimicking the colour and
the pattern of the egg which reduces the chances of the egg being left out thereby increasing their
productivity [7].
The timely sense of egg laying of cuckoo is quite interesting. Parasitic cuckoo birds are in search
of host bird nests which have just laid their own eggs. In general the cuckoo birds lay their eggs
earlier than the host bird’s eggs in order to create space for their own eggs and also to ensure that
a large part of the host bird feed is received by their chicks.
International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014
53
5. PRINCIPLE BEHIND CUCKOO SEARCH ALGORITHM
Each cuckoo bird lays a single egg at a time which is discarded into a randomly chosen nest. The
optimum nest with great quality eggs is carried over to next generations. The number of host
nests is static and a host can find an alien egg with a probability (Pa) [0, 1], whose presence leads
to either throwing away of the egg or abandoning the nest by the host bird [8].
One has to note that each egg in a nest represents a solution and a cuckoo egg represents a new
solution where the objective is to replace the weaker fitness solution by a new solution.
The flowchart for CSA is as shown which involves the following steps:
Step (1) - Introduce a random population of n host nests, Xi .
Step (2) - Obtain a cuckoo randomly by Levy flight behaviour, i.
Step (3) - Calculate its fitness function, Fi .
Step (4) - Select a nest randomly among the host nests say j and calculate its fitness, Fj .
Step (5) - If Fi < Fj , then replace j by new solution else let j be the solution.
Step (6) - Leave a fraction of Pa of the worst nest by building new ones at new locations using
Levy flights.
Step (7) - Keep the current optimum nest, Go to Step (2) if T (Current Iteration) < MI (Maximum
Iteration).
Step (8) - Find the optimum solution.
Important Stages involved in CSA are:
i) Initialization: Introduce a random population of n host nest (Xi = 1, 2, 3...n).
ii) Levy Flight Behaviour: Obtain a cuckoo by Levy flight behaviour equation which is defined as
follows:
Xi (t + 1) = Xi (t) + α ⊕ Levy (λ), α > 0 (1)
Levy (λ) = t (−λ), 1 < λ < 3 (2)
iii) Fitness Calculation: Calculate the fitness using the fit- ness function in order to obtain an
optimum solution. Select a random nest, let us say j. Then the fitness of the cuckoo egg (new
solution) is compared with the fitness of the host eggs (solutions) present in the nest. If the value
of the fitness function of the cuckoo egg is less than or equal to the fitness function value of the
randomly chosen nest then the randomly chosen nest (j) is replaced by the new solution.
Fitness Function = Current Best Solution – Previous Best Solution (3)
Since the Fitness function = Current best solution - Previous best solution, the value of the fitness
function approaching the value zero means that the deviation between solutions decreases due to
increase in the number of iterations.
The conclusion is that if the cuckoo egg is similar to a normal egg it is hard for the host bird to
differentiate between the eggs. The fitness is difference in solutions [10] and the new solution is
replaced by the randomly chosen nest. Otherwise when the fitness of the cuckoo egg is greater
than the randomly chosen nest, the host bird recognizes the alien egg, as a result of which it may
throw the egg or forsake the nest.
International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014
54
The various stages involved in the working of this algorithm are explained in the flow chart
International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014
55
Figure 1. Fitness function values for planar antenna array of 18x18 elements
From the fitness function graph it can be observed that as the number of iterations increases, the
value of the fitness function graph approaches to zero.
iv) Termination: In the current iteration the solution is compared and the best solution is only
passed further which is done by the fitness function. If the number of iterations is less than the
maximum then it keeps the best nest.
After the execution of the initialisation process, the levy flight and the fitness calculation
processes, all cuckoo birds are prepared for their next actions. The CSA will terminate after
maximum iterations [MI], have been reached.
Figure 2. Planar Antenna Array Set–Up
International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014
56
6. TECHNICAL DETAILS
6.1. Synthesis of Planar Antenna Array
Consider a planar antenna array which consists of M-by-N rectangular antennas which are spaced
equally [10]. They have been arranged in a regular rectangular array in the x-y plane. The inter-
element spatial arrangement is
d = dx = dy = λ/2 = R0
Where λ is the wavelength
The outputs are summed up in order to provide a distinct output.
Where
6.2 Number of Cuckoo Birds
This parameter decides number of Cuckoo birds being initialized in the field space.
6.3. Step Size
In case of CSA, step size refers to the distance covered by a cuckoo bird for a fixed number of
iterations. It is preferred to have an intermediate step size in order to obtain an effective solution.
If the step size is too large or too small it leads to deviation from the required optimum solutions
[7].
7. FIGURES AND TABLES
Table 1: SLL values for various sizes of Planar Antenna Array
International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014
57
Figure 3. Polar pattern of a single antenna array element
Figure 4. Radiation Pattern for a planar antenna array of 121 elements and φ= 0 degrees
International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014
58
Figure 5. Polar pattern for the radiation of an 11X11 planar antenna array
Figure 6. Radiation Pattern for a planar antenna array of 256 elements and φ= 0 degrees
International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014
59
Figure 7. Radiation Pattern for a planar antenna array of 400 elements and φ = 90 degrees
8. COMPARING CSA WITH GA AND PSO
Consider the results for GA from the reference papers [2] and [13]. From paper [2], for N=16
elements -16.07 dB of SLL was achieved. From paper [13], for N=16 elements -28.47 dB of SLL
was achieved.
Figure 8. Comparison of CSA with GA using reference papers [2] and [13]
Consider the results for PSO from the reference paper [14]. From paper [14], for N=20 elements -
16.2037 dB of SLL was achieved.
From paper [2], for N=20 elements -21.05 dB of SLL was achieved. From paper [13], for N=20
elements -28.59 dB of SLL was achieved.
International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014
60
Figure 9. Comparison of CSA with GA and PSO using reference papers [2], [13] and [14]
9. OBSERVATIONS
When φ = 0 degrees, Maximum iterations = 500, a narrow beam is obtained as the best optimum
solution for a large value of the number of iterations (optimum value) [11]. When maximum
iteration is 150, main lobe appears to be spread over a wide range of direction angle (θ). For
increase in the number of antenna elements in a planar antenna array a narrow beam is achieved
correspondingly. The same field pattern is obtained for φ=π/2 degrees, maximum iterations of
500.
CSA in Planar Antenna Arrays is compared with GA and PSO that were implemented in Linear
Antenna Arrays which can be used as sufficient data to come to a conclusion about the out
performance of CSA compared to other Nature Inspired Algorithms.
The directivity of an isotropic antenna is unity as power is radiated equally in all directions [Fig
3]. In case of other sources such as omnidirectional antennas, sectoral antennas, directivity is
greater than unity. Directivity can be considered as the figure of merit of directionality as it is an
indication of the directional properties of the antenna with respect to an isotropic source. This
shows that for any alignment of planar antenna array the same field pattern will be obtained
which promotes beam steering in RADAR applications.
10. CONCLUSIONS
CSA is very easily applicable among all the nature inspired meta-heuristic algorithms since it
provides the optimum solution. The implementation of CSA led to a tremendous increase in
directivity [Fig 4] which promotes long distance communication. Gain of Antenna Array is also
increased by using CSA.
ACKNOWLEDGEMENTS
The authors are very thankful to Prof. N.V.S.N. Sarma for guiding us throughout this project
International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014
61
REFERENCES
[1] Ehsan Valian, Shahram Mohanna, Saeed Tavakoli, Improved Cuckoo Search Algorithm for Feed
Forward Neural Network Training, International Journal for Artificial Intelligence and Applications
(IJAIA) ,Vol.2, No.3, Pp. 36-43, July 2011.
[2] Pallavi Joshi, Nitin Jain, Optimization of Linear Antenna Array Using Genetic Algorithm for
Reduction in Side Lobe Level and to Improve Directivity, International Journal of Latest Trends in
Engineering and Technology(IJLTET), Vol.2, Issue 3, Pp. 185-191, May 2013.
[3] Khairul Najmy ABDUL RANI, Mohd.Fareq ABD MALEK, Neoh SIEW-CHIN, Nature Inspired
Cuckoo Search Algorithm for Side Lobe Suppression in a Symmetric Linear Antenna Array, RADIO
ENGINEER-ING, Vol.21, No.3, Pp. 865-974, September 2012.
[4] Ch.Ramesh, P.Mallikarjuna Rao, Antenna Array Synthesis for Suppressed Side Lobe Level Using
Evolutionary Algorithms, International Journal of Engineering and Innovative Technology(IJEIT),
Volume 2, Issue 3, Pp. 235-239, September 2012.
[5] Ehsan Valian, Shahram Mohanna, Saeed Tavakoli, Improved Cuckoo Search Algorithm for Global
Optimization, International Journal of Communications and Information Technology, IJCIT-2011-
Vol.1-No.1, Pp. 31-44, Dec 2011.
[6] Boufeldja Kadri, Miloud Boussahla, Fethi Tarik Bendimerad, Phase-Only Planar Antenna Array
Synthesis with Fuzzy Genetic Algorithms, IJCSI, International Journal of Computer Science Issues,
Vol.7, Issue 1, No.2, Pp. 72-77, January 2010.
[7] Xin-She-Yang, Nature Inspired Meta-heuristic Algorithm, 2nd Edition, Luniver Press, 2010.
[8] Monica Sood, Gurline Kaur, Speaker Recognition Based on Cuckoo Search Algorithm, International
Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-2,
Issue-5, Pp. 311-313, and April 2013.
[9] Moe Moe Zaw, Ei Ei Mon, Web Document Clustering Using Cuckoo Search Clustering Algorithm
based on Levy Flight, International Journal of Innovation and Applied Studies, ISSN 2028-9324,
Vol.4, No.1, Pp182-188, Sep 2013.
[10] Constantine A.Balanis, Antenna Theory Analysis and Design, 2nd Edition, Pp. 310.
[11] Robert S. Elliott, Antenna Theory and Design, Revised Edition, IEEE press.
[12] Randy I. Haupt, Wiley, Antenna Array - A Computational Approach, IEEE press.
[13] Shraddha Shrivastava, Kanchan Cecil, Performance Analysis of Linear Antenna Array
Using Genetic Algorithm, International Journal of Engineering and Innovative Technology (IJEIT),
Volume 2, Issue 5, Pp. 84-88, November 2012.
[14] M. Khodier and M. Al-Aqeel, Linear and Circular Array Optimization: A Study of Particle Swarm
Intelligence, Progress In Electromagnetics Research B, Vol. 15, 347–373, 2009

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Out performance of cuckoo search

  • 1. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014 DOI : 10.5121/ijaia.2014.5404 51 OUT PERFORMANCE OF CUCKOO SEARCH ALGORITHM AMONG NATURE INSPIRED ALGORITHMS IN PLANAR ANTENNA ARRAYS A.Sai Charan1 , N.K.Manasa2 , Prof. N.V.S.N. Sarma3 1, 2, 3 Department Of Electronics and Communication Engineering, National Institute of Technology, Warangal ABSTRACT In this modern era a great deal of metamorphism is observed around us which eventuate due to some minute modifications and innovations in the area of Science and Technology. This paper deals with the application of a meta heuristic optimization algorithm namely the Cuckoo Search Algorithm in the design of an optimized planar antenna array which ensures high gain ,directivity, suppression of side lobes, increased efficiency and improves other antenna parameters as well[1], [2] and [3]. KEYWORDS Meta-Heuristic, Side Lobe Suppression, Gain, Directivity, Side Lobe Level (SLL). 1. INTRODUCTION Antenna optimization techniques have made a breakthrough in the Communication domain. They have contributed vividly to modern wireless communications in the form of smart antennas which are antenna arrays that adjust their own beam pattern to accentuate signals of interest and concurrently reducing the radio frequency interference. In the field of antennas, Cuckoo Search Algorithm (CSA) was first applied for side lobe suppression in linear antenna array by distance modulation. Large arrays are complex to build, have increased fabrication and set up cost and are heavier at the same time. Therefore reducing antenna element weight from the array is desirable without degrading the performance of the array. But here we are not reducing the mass of the antenna array elements, only the weight of the antenna elements(current)are adjusted in order to achieve minimum side lobe level. We opt a technique based on density tapering to lower side- lobes in the array by monotonically decreasing the magnitude of weights away from the centre of the array. 2. REVIEW OF VARIOUS TECHNIQUES Owing to high adaptability and ability to optimize multi-dimensional problems, several evolutionary algorithms have been proposed such as Particle Swarm Optimization (PSO),
  • 2. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014 52 Invasive Weed Optimization (IWO), Genetic Algorithm (GA), etc. These algorithms are associated with following drawbacks which make them unreliable. i) The PSO could not work out the problem of scattering and optimization. ii) The IWO require the genes of minimum one parent species to be forwarded to next generation and iii) The GA has a poor fitness function which generates bad chromosome blocks in spite of the fact that only good chromosome blocks cross over. Also no assurance is given whether the GA will find a global optimum solution [4]. This paper has explored a choice of antenna array synthesis, the (CSA) [5], to overcome the above mentioned problems and to yield promising results. 3. PLANAR ARRAYS Planar array is a two dimensional configuration of elements arranged to lie in a plane. The planar array may be thought of as an array of linear arrays. The elements are arranged in a matrix form having a phase shifter. The planar arrangement of all antenna elements forms the complete phased array antenna. There are wide spread applications of planar antenna arrays which involve the suppression of side lobes. The signals radiated by individual antennas determine the effective radiation pattern of the array. They are used to point a fixed radiation pattern or to scan a region rapidly in the azimuthal plane. Several methods have been developed for the design of planar antenna array but all those methods pertain to other nature inspired optimization algorithms. Planar antenna array optimization has been implemented earlier using Fuzzy GA [6]. Direction angle (reference angle) is considered with the plane of planar antenna array. This paper deals with the design of a planar antenna array by using CSA. 4. CUCKOO SEARCH ALGORITHM (CSA) CSA is one of the modern nature inspired meta-heuristic algorithms. The Greek terms “meta” and “heuristic” refer to “change” and “discovery oriented by trial and error” respectively. Various techniques are used to minimise the constraints associated with the problem in order to obtain a global optimum solution. Cuckoos are attractive birds. The attractiveness is owing to the beautiful sounds produced by them and also due to their reproduction approach which proves to be combative in nature. These birds are referred to as brood parasites as they lay their eggs in communal nests. They remove the eggs in the host bird nest in order to increase the hatching probability of their own eggs. There are three types of brood parasites - the intraspecific brood parasite, cooperation breed and nest take over type. The host bird involves in direct combat with the encroaching cuckoo bird. If the host bird discovers the presence of an alien egg, it either throws away the egg or deserts the nest. Some birds are so specialized that they have the characteristic of mimicking the colour and the pattern of the egg which reduces the chances of the egg being left out thereby increasing their productivity [7]. The timely sense of egg laying of cuckoo is quite interesting. Parasitic cuckoo birds are in search of host bird nests which have just laid their own eggs. In general the cuckoo birds lay their eggs earlier than the host bird’s eggs in order to create space for their own eggs and also to ensure that a large part of the host bird feed is received by their chicks.
  • 3. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014 53 5. PRINCIPLE BEHIND CUCKOO SEARCH ALGORITHM Each cuckoo bird lays a single egg at a time which is discarded into a randomly chosen nest. The optimum nest with great quality eggs is carried over to next generations. The number of host nests is static and a host can find an alien egg with a probability (Pa) [0, 1], whose presence leads to either throwing away of the egg or abandoning the nest by the host bird [8]. One has to note that each egg in a nest represents a solution and a cuckoo egg represents a new solution where the objective is to replace the weaker fitness solution by a new solution. The flowchart for CSA is as shown which involves the following steps: Step (1) - Introduce a random population of n host nests, Xi . Step (2) - Obtain a cuckoo randomly by Levy flight behaviour, i. Step (3) - Calculate its fitness function, Fi . Step (4) - Select a nest randomly among the host nests say j and calculate its fitness, Fj . Step (5) - If Fi < Fj , then replace j by new solution else let j be the solution. Step (6) - Leave a fraction of Pa of the worst nest by building new ones at new locations using Levy flights. Step (7) - Keep the current optimum nest, Go to Step (2) if T (Current Iteration) < MI (Maximum Iteration). Step (8) - Find the optimum solution. Important Stages involved in CSA are: i) Initialization: Introduce a random population of n host nest (Xi = 1, 2, 3...n). ii) Levy Flight Behaviour: Obtain a cuckoo by Levy flight behaviour equation which is defined as follows: Xi (t + 1) = Xi (t) + α ⊕ Levy (λ), α > 0 (1) Levy (λ) = t (−λ), 1 < λ < 3 (2) iii) Fitness Calculation: Calculate the fitness using the fit- ness function in order to obtain an optimum solution. Select a random nest, let us say j. Then the fitness of the cuckoo egg (new solution) is compared with the fitness of the host eggs (solutions) present in the nest. If the value of the fitness function of the cuckoo egg is less than or equal to the fitness function value of the randomly chosen nest then the randomly chosen nest (j) is replaced by the new solution. Fitness Function = Current Best Solution – Previous Best Solution (3) Since the Fitness function = Current best solution - Previous best solution, the value of the fitness function approaching the value zero means that the deviation between solutions decreases due to increase in the number of iterations. The conclusion is that if the cuckoo egg is similar to a normal egg it is hard for the host bird to differentiate between the eggs. The fitness is difference in solutions [10] and the new solution is replaced by the randomly chosen nest. Otherwise when the fitness of the cuckoo egg is greater than the randomly chosen nest, the host bird recognizes the alien egg, as a result of which it may throw the egg or forsake the nest.
  • 4. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014 54 The various stages involved in the working of this algorithm are explained in the flow chart
  • 5. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014 55 Figure 1. Fitness function values for planar antenna array of 18x18 elements From the fitness function graph it can be observed that as the number of iterations increases, the value of the fitness function graph approaches to zero. iv) Termination: In the current iteration the solution is compared and the best solution is only passed further which is done by the fitness function. If the number of iterations is less than the maximum then it keeps the best nest. After the execution of the initialisation process, the levy flight and the fitness calculation processes, all cuckoo birds are prepared for their next actions. The CSA will terminate after maximum iterations [MI], have been reached. Figure 2. Planar Antenna Array Set–Up
  • 6. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014 56 6. TECHNICAL DETAILS 6.1. Synthesis of Planar Antenna Array Consider a planar antenna array which consists of M-by-N rectangular antennas which are spaced equally [10]. They have been arranged in a regular rectangular array in the x-y plane. The inter- element spatial arrangement is d = dx = dy = λ/2 = R0 Where λ is the wavelength The outputs are summed up in order to provide a distinct output. Where 6.2 Number of Cuckoo Birds This parameter decides number of Cuckoo birds being initialized in the field space. 6.3. Step Size In case of CSA, step size refers to the distance covered by a cuckoo bird for a fixed number of iterations. It is preferred to have an intermediate step size in order to obtain an effective solution. If the step size is too large or too small it leads to deviation from the required optimum solutions [7]. 7. FIGURES AND TABLES Table 1: SLL values for various sizes of Planar Antenna Array
  • 7. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014 57 Figure 3. Polar pattern of a single antenna array element Figure 4. Radiation Pattern for a planar antenna array of 121 elements and φ= 0 degrees
  • 8. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014 58 Figure 5. Polar pattern for the radiation of an 11X11 planar antenna array Figure 6. Radiation Pattern for a planar antenna array of 256 elements and φ= 0 degrees
  • 9. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014 59 Figure 7. Radiation Pattern for a planar antenna array of 400 elements and φ = 90 degrees 8. COMPARING CSA WITH GA AND PSO Consider the results for GA from the reference papers [2] and [13]. From paper [2], for N=16 elements -16.07 dB of SLL was achieved. From paper [13], for N=16 elements -28.47 dB of SLL was achieved. Figure 8. Comparison of CSA with GA using reference papers [2] and [13] Consider the results for PSO from the reference paper [14]. From paper [14], for N=20 elements - 16.2037 dB of SLL was achieved. From paper [2], for N=20 elements -21.05 dB of SLL was achieved. From paper [13], for N=20 elements -28.59 dB of SLL was achieved.
  • 10. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014 60 Figure 9. Comparison of CSA with GA and PSO using reference papers [2], [13] and [14] 9. OBSERVATIONS When φ = 0 degrees, Maximum iterations = 500, a narrow beam is obtained as the best optimum solution for a large value of the number of iterations (optimum value) [11]. When maximum iteration is 150, main lobe appears to be spread over a wide range of direction angle (θ). For increase in the number of antenna elements in a planar antenna array a narrow beam is achieved correspondingly. The same field pattern is obtained for φ=π/2 degrees, maximum iterations of 500. CSA in Planar Antenna Arrays is compared with GA and PSO that were implemented in Linear Antenna Arrays which can be used as sufficient data to come to a conclusion about the out performance of CSA compared to other Nature Inspired Algorithms. The directivity of an isotropic antenna is unity as power is radiated equally in all directions [Fig 3]. In case of other sources such as omnidirectional antennas, sectoral antennas, directivity is greater than unity. Directivity can be considered as the figure of merit of directionality as it is an indication of the directional properties of the antenna with respect to an isotropic source. This shows that for any alignment of planar antenna array the same field pattern will be obtained which promotes beam steering in RADAR applications. 10. CONCLUSIONS CSA is very easily applicable among all the nature inspired meta-heuristic algorithms since it provides the optimum solution. The implementation of CSA led to a tremendous increase in directivity [Fig 4] which promotes long distance communication. Gain of Antenna Array is also increased by using CSA. ACKNOWLEDGEMENTS The authors are very thankful to Prof. N.V.S.N. Sarma for guiding us throughout this project
  • 11. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 4, July 2014 61 REFERENCES [1] Ehsan Valian, Shahram Mohanna, Saeed Tavakoli, Improved Cuckoo Search Algorithm for Feed Forward Neural Network Training, International Journal for Artificial Intelligence and Applications (IJAIA) ,Vol.2, No.3, Pp. 36-43, July 2011. [2] Pallavi Joshi, Nitin Jain, Optimization of Linear Antenna Array Using Genetic Algorithm for Reduction in Side Lobe Level and to Improve Directivity, International Journal of Latest Trends in Engineering and Technology(IJLTET), Vol.2, Issue 3, Pp. 185-191, May 2013. [3] Khairul Najmy ABDUL RANI, Mohd.Fareq ABD MALEK, Neoh SIEW-CHIN, Nature Inspired Cuckoo Search Algorithm for Side Lobe Suppression in a Symmetric Linear Antenna Array, RADIO ENGINEER-ING, Vol.21, No.3, Pp. 865-974, September 2012. [4] Ch.Ramesh, P.Mallikarjuna Rao, Antenna Array Synthesis for Suppressed Side Lobe Level Using Evolutionary Algorithms, International Journal of Engineering and Innovative Technology(IJEIT), Volume 2, Issue 3, Pp. 235-239, September 2012. [5] Ehsan Valian, Shahram Mohanna, Saeed Tavakoli, Improved Cuckoo Search Algorithm for Global Optimization, International Journal of Communications and Information Technology, IJCIT-2011- Vol.1-No.1, Pp. 31-44, Dec 2011. [6] Boufeldja Kadri, Miloud Boussahla, Fethi Tarik Bendimerad, Phase-Only Planar Antenna Array Synthesis with Fuzzy Genetic Algorithms, IJCSI, International Journal of Computer Science Issues, Vol.7, Issue 1, No.2, Pp. 72-77, January 2010. [7] Xin-She-Yang, Nature Inspired Meta-heuristic Algorithm, 2nd Edition, Luniver Press, 2010. [8] Monica Sood, Gurline Kaur, Speaker Recognition Based on Cuckoo Search Algorithm, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-2, Issue-5, Pp. 311-313, and April 2013. [9] Moe Moe Zaw, Ei Ei Mon, Web Document Clustering Using Cuckoo Search Clustering Algorithm based on Levy Flight, International Journal of Innovation and Applied Studies, ISSN 2028-9324, Vol.4, No.1, Pp182-188, Sep 2013. [10] Constantine A.Balanis, Antenna Theory Analysis and Design, 2nd Edition, Pp. 310. [11] Robert S. Elliott, Antenna Theory and Design, Revised Edition, IEEE press. [12] Randy I. Haupt, Wiley, Antenna Array - A Computational Approach, IEEE press. [13] Shraddha Shrivastava, Kanchan Cecil, Performance Analysis of Linear Antenna Array Using Genetic Algorithm, International Journal of Engineering and Innovative Technology (IJEIT), Volume 2, Issue 5, Pp. 84-88, November 2012. [14] M. Khodier and M. Al-Aqeel, Linear and Circular Array Optimization: A Study of Particle Swarm Intelligence, Progress In Electromagnetics Research B, Vol. 15, 347–373, 2009