USING CUCKOO ALGORITHM FOR ESTIMATING TWO GLSD PARAMETERS AND COMPARING IT WITH OTHER ALGORITHMS

International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 5, October 2017
DOI:10.5121/ijcsit.2017.9507 87
USING CUCKOO ALGORITHM FOR ESTIMATING
TWO GLSD PARAMETERS AND COMPARING IT
WITH OTHER ALGORITHMS
Jane Jaleel Stephan, Haitham Sabah Hasan and Alaa Hamza Omran
University of Information Technology & Communications, Iraq
ABSTRACT
This study introduces and compares different methods for estimating the two parameters of generalized
logarithmic series distribution. These methods are the cuckoo search optimization, maximum likelihood
estimation, and method of moments algorithms. All the required derivations and basic steps of each
algorithm are explained. The applications for these algorithms are implemented through simulations using
different sample sizes (n = 15, 25, 50, 100). Results are compared using the statistical measure mean
square error.
KEYWORDS
Cuckoo search optimization (CSO) algorithm, maximum likelihood estimation (MLE) algorithm, method of
moments (MOM) algorithm, mean square error (MSE).
1. INTRODUCTION
The process of modifying a system to present several new features that corporate in enhancing
the system and work more efficiently is known as optimization process. Also the Optimization
process can be defined as the process of finding alternative solution to increase the performance
of the system under specific constraints such as increasing the desired parameters and decrease
the undesired parameters in the system which has a problem to solve it [1]. The increasing means
trying to get additional good results without additional cost such as the optimization which
occurs on computer or any android phone will results in increasing the speed of processing which
makes them run faster with less memory requirements. There are many algorithms in solving
optimization problems such as cukoo search algorithm which introduced for the first time by
Yang and Deb [2]. Many researchers work on testing this algorithm on some benchmark
functions and compare the results with other algorithms like PSO, GA; the obtained results show
that the cukoo algorithm is better than the others. One of the popular met heuristic,
combinatorial search optimization techniques is ACO (Ant Colony Optimization) which is
developed from natural ant behavior ACO was used along with Rough Sets and Fuzzy Rough
Sets in feature selection in [3], [4], [5] also it is used for optimizing of firewall rules in [6].
Today the Cuckoo search algorithm became as the one of the most optimization algorithm which
used in every domain like scheduling planning, forecasting, image processing, feature selection
and engineering optimization [7]. This paper presents a Comparing of the Cuckoo Algorithm
with Other Algorithms for Estimating Two GLSD Parameters. Some important functions are
defined as follows:
The discrete random variable (x) exhibits the generalized logarithmic series distribution (GLSD)
with two parameters (α and β), where (α) is a scale parameter and (β) is a shape parameter. Let

88
,
Where Ө is a function from α. The positive matrix factorization (p.m.f.) of GLSD is defined by
Eq. (1) as follows:
, (1)
Where , , , .
The distribution in Eq. (1) depends on the zero-truncated generalized negative binomial defined
by Eq. (2):
, (2)
Where .
When limit (k→0) is considered for Eq. (2), we obtain the studied distribution in Eq. (1).
The mean of GLSD is defined in Eq. (1), and the variance obtained from the general formula of
the (kth
) moments about the mean is as follows:
, ,
, (3)
. (4)
2. ESTIMATING PARAMETERS
We apply different methods for the p.m.f. parameters in Eq. (1).
2.1 Maximum Likelihood Estimation (MLE)
The Maximum Likelihood Estimation (MLE) [8], [9] that corresponds to Eq. (1) is given by:
,
,
And .
This equation derives:
. (6)
From , we obtain .
(5)

89
2.2 Method of Moments (MOM) Estimator for GLSD Parameters
Method of Moments (MOM) Estimator for GLSD Parameters [10], these estimators
( ) are obtained by solving the following:
,
.
When r = 1,
, (7)
.
When r = 2,
, (8)
.
Given that:
and
;
then,
.
We have
, (9)
, (10)
and
,
which is simplified as follows:
.
We also obtain the following:
. (11)
Then,
. (12)

90
We derive the first three non-central moments obtained from [ .
Then,
,
,
. (13)
Given that:
,
We obtain the following based on the preceding relation:
,
. (14)
Eq. (14) can be simplified into
. (15)
Given that
,
Eq. (15) can be written as follows:
, (16)
which is an implicit function that can be solved numerically to determine ( ) based on
observation. We then obtain ( ) by using ( ) and solving Eq. (11).
2.3 Cuckoo Search Optimization (CSO) Algorithm
This algorithm is based on the breeding behaviour of the cuckoo bird. It has three basic
optimization rules [11], [12].
1. Each bird lays one egg at a time, and each egg is placed randomly in a selected nest.
2. The best nest with the highest fitness value will be carried over to the next generation.
3. The number of available host nests is fixed. ( ) represents the probability of the host
bird discovering egg/s in its nest. The host bird can either throw away the egg/s or abandon the
nest to build a new one [13][14].
The latter scenario can be regarded as the new best solution.
Let:

91
Be the nest where the cuckoo bird initially lives, and
Be the new nest with the highest fitness value.
When random walk is used, the performance of the cuckoo (i) that applies levy flight is
expressed as [5, 7]:
.
Levy flight was first introduced by the French mathematician Paul Pierre.
The probability ( ) indicates that the egg/s in the nest may be from another bird, and
thus, the cuckoo bird may leave this host nest and build another one. The n hosts may be changed
to new hosts with random positions (probability of change). Thus, the objective function
belongs to the maximization type and the objective must be fitted into this type. The most
important algorithm that can be applied is one used to solve nonlinear equation problems or one
used in neural networks because these objects allow the algorithm to be transformed from state to
state to reach the optimal solution. Given that GLSD has two parameters (θ and β), then the
algorithm implements the following steps.
Each bird lays one egg at a time in a randomly selected nest. The number of selected nests is
equal to the number of parameters to be estimated. The number of nests is determined from the
following equation:
Number of nests = LB + (UB − LB) × random number (0, 1).
Let be the nest where the cuckoo bird initially lives.
is the new nest with the highest fitness value.
+
Each nest contains the parameters to be estimated, and the number of nests is also determined
based on these parameters.
Step (1):
Number of nests = LB + (UB − LB) × random number (0, 1)
Step (2):
The objective function for each nest is calculated as follows:
.

92
Step (3):
The best values of the parameters determine the best nest with respect to the eggs.
Step (4):
The repetition begins. Let
be the nest in which the cuckoo bird initially lives, and
+ be the new nest with the highest fitness value.
Step (5):
A new nest is generated for the cuckoo from k, as follows:
,
,
,
,
,
.
Step (6):
The objective function for each new nest is computed.
Step (7):
The solution is continued until the stopping rule ends with the total frequency. The best solution
determined is then printed.
The CSO algorithm, which represents a meta-heuristic algorithm, is adopted to estimate (θ ̂, β ̂).
Then, (θ ̂ ) provides the estimate of (α). More details on this algorithm are explained in detail in
[15].
3. SIMULATION
The three estimators of (α and β), i.e., the CSO, MLE, and MOM algorithms, are compared
through MATLAB: A11 program. Different sample sizes (n = 15, 25, 50, 100) are considered,
and the results are compared using the statistical measure mean square error (MSE) and run of
each experiment (R = 1000).

93
TABLE 1: Comparison of the Different Estimators When β = 1.5 and α = 0.3
TABLE 2: Comparison of the Different Estimators When β = 2 and α = 0.2

94
TABLE 4: Comparison of the Different Estimators When β = 3 and α = 0.33

95
3. CONCLUSION
After estimating (α and β) using the three different methods (i.e., MOM, CSO, and MLE) with
different sample sizes (n = 15, 25, 50, 100), we determined that the best estimator for small
sample sizes (n = 15, 25) based on MSE was the CSO estimator, as shown in Tables 1 to 5. By
contrast, MLE was the best estimator for large sample sizes (n = 50, 100). However, we
conclude that the CSO estimator is the best type for small sample sizes (n = 15, 25) because the
CSO algorithm depends on the number of eggs in the host nest, which is limited.
REFERENCES
[1] Azizah Binti Mohamad, Azlan Mohd Zain & Nor Erne Nazira Bazin, (2014), “Cuckoo Search Algorithm for
Optimization Problems—A Literature Review and its Applications”, Applied Artificial Intelligence An
International Journal Volume 28, Issue 5.
[2] Xin She Yang and Sush Deb, "Nature & Biologically Inspired Computing," in IEEE, University of Cambridge,
Trumpinton Street, CB2 1PZ, UK, 2010.
[3] Ravi Kiran Varma P, Valli Kumari V, and Srinivas Kumar S, "A novel intelligent attribute reduction technique
based on Ant Colony Optimization," International Journal of Intelligent Systems Technologies and Applicaitons,
vol. 1, no. 1, pp. 23-45, 2015.
[4] Ravi Kiran Varma P, Valli Kumari V, and Srinivas Kumar S, "Feature selection using relative fuzzy entropy and
ant colony optimization applied to real-time intrusion detection system," Procedia Computer Science, vol. 85, no.
2016, pp. 503-510, 2016.

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[5] Ravi Kiran Varma P, Valli Kumari V, and Srinivas Kumar S, "Application of Rough Sets and Ant Colony
Optimization in feature selection for Network Intrusion Detection," International Journal of Applied Engineering
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[6] Ravi Kiran Varma P, Valli Kumari V, and Srinivas Kumar S, "Ant Colony Optimization Based Anomaly
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[12] Hongqing Zheng and Yongquan Zhou,(2013), A Cooperative Coevolutionary Cuckoo Search Algorithm for
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