233
Original scientific paper
 MIDEM Society
A Meta-Heuristic Approach for Wavelength 
Assignment in Long-Haul Optical System
R. Hemalatha, R. Mahalakshmi
Department of EEE, Kumaraguru College of Technology, Coimbatore, India
Abstract: Routing and Wavelength Assignment (RWA) problem is one of the optimization problems in optical networks. The aim is to 
minimize the blocking probability and the number of wavelengths used. The RWA problem can be solved by number of algorithms 
like Genetic Algorithm (GA), Ant Colony Optimization (ACO), etc. In the proposed research, Shuffled Frog Leaping Algorithm (SFLA) 
has been implemented in optical networks to solve the RWA problem. The optimization parameters considered are cost, number of 
wavelengths, hop count and blocking probability. The problem is analyzed for different wavelength assignment methods such as first 
fit, random, round robin, wavelength ordering and Four Wave Mixing (FWM) priority based wavelength assignment. Fitness function 
devised includes cost, number of wavelengths, hop count and setup time. The proposed SFLA algorithm has been compared with GA 
and is found to minimize the blocking probability, cost and computational complexity.
Keywords: GA; RWA; SFLA; WDM
Metahevrističen pristop določitve valovne dolžine 
optičnega sistema Long-Haul
Izvleček: Osnoven problem optičnih omrežjih je njihova pot in določitev valovne dolžine (RWA). Namen je minimizacija verjetnosti 
združevanja in števila uporabljenih valovnih dolžin. RWA problem se lahko reši s številnimi algoritmi, kot so generičen algoritem (GA), 
Ant Colony optimizacija (ACO) in drugi. V predstavljeni raziskavi je bil uporabljen Shuffled Frog Leaping Algoritem (SFLA). Parametri 
optimizacije so bili strošek, število valovnih dolžin ter število poskokov in verjetnost združevanja. Problem je bil analiziran za različne 
določitve valovnih dolžin, kot so prvi približek, naključnost, sistem vsak s vsakim, urejanje valovnih dolžin ter Four Wave Mixing (FWM) 
prednostna določitev valovne dolžine. Predlagan SFLA algoritem je bil primerjan z GA algoritmom. Izkazalo se je, da SFLA zmanjšuje 
verjetnost združevanja, strošek in kompleksnost računanja.
Ključne besede: GA; RWA; SFLA; WDM
* Corresponding Author’s e-mail: hemalatha.r.ece@kct.ac.in
Journal of Microelectronics, 
Electronic Components and Materials
Vol. 47, No. 4(2017), 233 – 240
1 Introduction
In high capacity telecommunication networks, Optical 
networks play a major role. Routing, grooming and res-
toration are the wavelength based services provided 
by optical networks. Fiber optics is to transmit data in 
the form of light. Electrically powered switching equip-
ment such as a router or a switch aggregator is used in 
active optical system. It is used to regulate signal distri-
bution and to direct the signals to different users. The 
switch controls the incoming and outgoing signals. 
Optical splitters are used to isolate and collect optical 
signals. The aim of optical communication systems is 
to transfer large amount of information with simple 
equipments (Batagelj 2014).Optical fiber communica-
tion performs better in terms of transmission capacity 
and communication range (Vidmar 2001).
An optical Wavelength Division Multiplexing (WDM) 
network is a network with fiber optic transmission links 
designed to utilize the features of fibers and WDM. 
Wavelength-division multiplexing meets the high 
bandwidth demand. Several routing and wavelength 
assignment problems that exist in optical wavelength 
division multiplexing are traffic grooming , optimal 
routing and wavelength assignment, survivability, 
Quality of service(QoS) routing and physical layer im-
pairment aware (PLI aware) problems (Bhanjaa and 
Mahapatra 2013). A lightpath is an optical connection 
234
between two nodes. Depending on the wavelength of 
the lightpaths, data are optically routed at intermedi-
ate nodes (Le et al 2005 and Bisbal et al 2004). Con-
ventional methods to solve these complex problems 
consume more computational time (Wang et al 2014 
and Triay et al 2010). Multi-objective evolutionary algo-
rithms based on swarm intelligence are used to solve 
the RWA problem, which are in real- world optical net-
works (Kavian et al 2013 and Largo et al 2012). In the 
proposed method Shuffled Frog Leaping Algorithm is 
used to solve this problem. Similar algorithms available 
are either simple leading to poor performance or too 
complex to be used. The aim is to use computationally 
feasible algorithm for better network performance.  
In this research paper, routing and wavelength assign-
ment problem model is described with two optimiza-
tion algorithms, genetic algorithm and shuffled frog 
leaping algorithm. Genetic Algorithm is used to solve 
many problems in variety of fields and hence compari-
son of SFLA is done with this algorithm. The discussions 
include simulation results, analysis, conclusions of the 
study and possible future work. 
2 Routing and wavelength assignment 
problem
2.1 Problem Definition
In dynamic routing and wavelength assignment, the 
lightpath requests arrive dynamically. A lightpath in a 
network is the path that satisfies the wavelength con-
tinuity constraint (that is, same wavelength should 
be  used by  the  l ightpath on all the links i n  its 
path). For each lightpath request, source node, destina-
tion node and holding time are defined. Holding time 
is the time during which a lightpath and the associated 
resources remain occupied. The resources become free, 
when the holding time elapses and can support other 
lightpath requests. Fig.1 shows the model to solve the 
RWA problem (Bhanjaa et al 2013).
Figure1: Block diagram of optimization method
2.2 Network Model
A network with N nodes can be modeled as a graph 
G(V,E), where V is the set of nodes denoting the routers 
or switches and E is the set of edges denoting connec-
tivity between the nodes. The links between the nodes 
are assumed to be bidirectional. In dynamic routing 
and wavelength assignment problem, V is the set of 
nodes denoting routers or wireless routing networks 
and E is the set of fiber links denoting physical connec-
tivity between the nodes.
2.3 Routing Model
Routing and Wavelength Assignment (RWA) is one of 
the major problems in optical networking. The goal is 
to maximize the number of optical connection. A route 
and wavelength must be assigned for each connection 
request. Throughout the path, wavelength must be the 
same, unless the usage of wavelength converters is as-
sumed. Two connections requests can share the same 
optical link, if different wavelength is provided (Bhan-
jaa et al 2012).
Fitness function to be maximized is given by 
 
1
,
( ), ( 1) ( , )
1
x
x x x
x k x
i j x
gx j gx j i j E
j
W W Wf
H TC
−
+ ∈
=
= + +
∑∑  (1)
Wx is free wavelength factor and takes the value of one, 
if same wavelength is available in all the links of path 
x or otherwise, zero. Summation in the first term de-
fines the total link cost of the path and summation in 
the second term represents the total number of hops 
in the path. The variable Hxi,j is one, if link (i, j) is a part of 
path x and is zero otherwise. Variable Tx represents the 
set up time of path x. Variable Kx represents the length 
of the x-th chromosome or number of memeplexes. 
The route is optimal when the objective function maxi-
mizes with the following constraints being satisfied.
 
( , ) ( , )
1,  if  i=S, lp LPlp lpij ij
i j E j i E
I I
∈ ∈
− = ∈∑ ∑  (2) 
 
 
( , ) ( , )
1,  if  i=D, lp LPlp lpij ij
i j E j i E
I I
∈ ∈
− = − ∈∑ ∑  (3)
 
( , ) ( , )
0,  if  i S, i D, lp LPlp lpij ij
i j E j i E
I I
∈ ∈
− = ≠ ≠ ∈∑ ∑   (4)
 
( , )
1,  if  i D, lp LPlpij
i j
i j E
I
≠
∈
≤ ≠ ∈∑    (5)
 
( , )
0,  if  i=D, lp LPlpij
i j
i j E
I
≠
∈
= ∈∑   (6)
R. Hemalatha et al; Informacije Midem, Vol. 47, No. 4(2017), 233 – 240
235
 
0
( , )
,  for t Tlpij
i j E
I h
∈
≤ ≤∑    (7)
 
0
( , )
( 1),  for t Tlpij
i j E
h I N
∈
< ≤ − >∑    (8)
Equations (2) to (6) represent the flow conservation 
constraint. Equations (7) and (8) represent the hop 
count constraint.
2.4 Wavelength Assignment Model
First fit and Random fit are the wavelength assignment 
techniques that are used generally. First Fit method de-
cides the available wavelength with the lowest index 
while random fit method identifies the available wave-
lengths and chooses randomly amongst them. O(w) 
is the complexity of both algorithms, where w is the 
number of wavelengths. First Fit outperforms Random 
Fit.  Other wavelength assignment techniques used 
here are round robin technique, wavelength ordering 
technique and Four Wave Mixing aware wavelength as-
signment technique. In FWM aware wavelength assign-
ment technique, since the FWM crosstalk power will be 
more over the center of transmission window, priority 
is given to the wavelengths towards the edges of the 
transmission window. O(N3log2N) is the complexity of 
this method, where N is the number of nodes in the 
network. In the proposed fitness function, Wx the free 
wavelength factor is updated after the wavelength as-
signment phase. In the wavelength assignment model, 
if the link (i, j) is used by the lightpath lp, the variable 
Iij
lp assumes one else it assumes zero. Variable Iijw
lp is the 
lightpath wavelength indicator. It shows whether the 
lightpath lp uses wavelength ‘W’ on link (i, j). Variable 
Iijw
lp(x,y) is the lightpath wavelength link indicator and 
is one when the lightpath uses wavelength ‘W’ on link 
(i, j) between the nodes x and y. l(x,y) equals one if a 
physical link exists between the nodes x and y (Bhanjaa 
et al 2010).
The wavelength continuity constraints are 
 1
0
,   (i,j)
W
lp lp
ij ijw
w
I I
−
=
= ∀∑     (9) 
 ( , ) ,   (i,j),  (x,y),  wlp x y lpijw ijwI I≤ ∀ ∀ ∀ ,  (10)
 ( , )
,
1,   (x,y),  wlp x yijw
i j
I ≤ ∀ ∀∑                   (11)
 1 1
( , ) ( , ) ( , ) ( , )
0 0
,  y=j
W W
lp x y x y lp y x y x lp
ijw ijw ij
w x w x
I l I l I
− −
= =
− =∑∑ ∑∑ (12)
 1 1
( , ) ( , ) ( , ) ( , )
0 0
,  y=I
W W
lp x y x y lp y x y x lp
ijw ijw ij
w x w x
I l I l I
− −
= =
− = −∑∑ ∑∑    (13)
 1 1
( , ) ( , ) ( , ) ( , )
0 0
0,  y i, y j
W W
lp x y x y lp y x y x
ijw ijw
w x w x
I l I l
− −
= =
− = ≠ ≠∑∑ ∑∑   (14)
3 Optimization algorithms
3.1 Genetic Algorithm
The methodology of Genetic Algorithm is shown in 
Fig.2. This method works iteratively on an initial solu-
tion set which is referred to as population and con-
verges to arrive on best solution (Kavian et al 2009). 
Figure 2: Flow diagram of GA
3.2 Representation of chromosome and Initialization 
of population 
Route or path encoded from source to destination is 
represented by a chromosome. A sequence of nodes 
creates each chromosome and is generated randomly 
satisfying the topology of particular network. The chro-
mosomes are of variable length, each of which is the 
encoding of a path from the source node S to the des-
tination node D. Random selection of solutions create 
R. Hemalatha et al; Informacije Midem, Vol. 47, No. 4(2017), 233 – 240
236
initial population. The initial population has only one 
chromosome. 
3.3 Crossover and Mutation
Crossover does not depend on the position of nodes 
in routing paths. One pair is randomly taken and the 
crossing site of each chromosome is identified by the 
locus of each node. The crossing points of two chromo-
somes may be different from each other (Ahn and Ram-
akrishna 2002). During mutation, the mutation site of 
the parent chromosome is chosen randomly. From the 
mutation site to the destination, different path chosen 
is based on the topology database.
3.4 Calculation of fitness function
The fitness function is formulated as in equation (1) 
and is to evaluate the quality of the chromosomes.
3.5 Shuffled Frog Leaping Algorithm
Shuffled Frog Leaping Algorithm (SFLA) is a natural 
inspired metaheuristic algorithm. Novelty of this al-
gorithm is its fast convergence speed. It combines the 
advantages of the both the genetic-based memetic 
algorithm and the behavior-based Particle Swarm Op-
timization (PSO) algorithm. In the SFLA, possible solu-
tions are defined by a group of frogs which is referred 
to as population. These groups of frog are partitioned 
into several communities referred to as memeplexes. 
Each frog in the memeplexes perform local search. The 
behavior of individual frog is influenced by behaviors 
of other frogs within each memeplex and it develops 
through a process of memetic evolution. After a cer-
tain number of memetic evolutions, the memeplexes 
are forced to mix together and through shuffling pro-
cess, new memeplexes are formed. Until convergence 
criteria are satisfied, the local search and the shuffling 
processes continue. The flowchart of Shuffled frog 
leaping algorithm is illustrated in Fig.3 (Roshni et al 
2016). 
The various steps are as follows:
a) SFLA involves a population ‘P’ of possible solu-
tion, defined by a group of virtual frogs(n).
b) Frogs are sorted in descending order based on 
their fitness and partitioned into subsets called as 
memeplexes (m).
c) Frog i is expressed as Xi = (Xi1, Xi2, …..Xin) where X 
represents number of variables.
d) Frogs with worst and best fitness are identified as 
Xw and Xb within each memeplex.
e) Frog with global best fitness is identified as Xg.
f ) The frog with worst fitness is improved based on 
the following equation.
Di=rand() (Xb -Xw)                   (15)
Xneww=X oldw+ Di                              (16)
Rand() is a random number in the range of [0,1] (Muzaf-
far 2006). 
Figure 3: Flow diagram of SFLA
R. Hemalatha et al; Informacije Midem, Vol. 47, No. 4(2017), 233 – 240
237
Di is the step size of i-th leaping frog and Dmax is the 
maximum step size allowed. If the fitness value of new 
Xw is better than the current one, Xw will be accepted. 
Otherwise, the calculated step size of leaping frog Di 
and new fitness Xneww are recomputed with Xb replaced 
by Xg. Further if no improvement is achieved, a new Xw 
is generated randomly. The update operation is repeat-
ed for specific number of iterations. After a predefined 
number of memetic evolutionary steps within each 
memeplex, the solutions of evolved memeplexes are 
replaced into new population. This is called shuffling 
process. Global information exchange among the frogs 
is promoted by the shuffling process. The population is 
then sorted in order of decreasing performance values 
and updates the population based on best frog’s posi-
tion, repartition the frog group into memeplexes and 
progress the evolution within each memeplex until the 
convergence criteria are satisfied (Samuel and Rajan 
2014).
4 Simulation results
The optimization algorithms have been carried out in 
MATLAB R2012b. Simulations are carried out for a 14 
node network similar to NSFNET network topology 
with 21 bidirectional links. Fig.4 depicts the fitness of 
the genetic algorithm and shuffled frog leaping algo-
rithm against the execution time. The fitness function 
involves cost, number of hops and holding time. Better 
fitness is achieved for a smaller execution time.
Figure 4: Fitness function of GA and SFLA
Fig.5 and 6 shows the variation in the blocking proba-
bility assuming different values of adjacent wavelength 
rejection ratios for GA and SFLA respectively. In each 
case by executing the program several times and then 
by computing the average, mean blocking probability 
is estimated. In FWM aware priority based wavelength 
assignment, the mean blocking probability decreases 
for a reduction in each of the adjacent wavelength re-
jection ratio. To reduce the FWM crosstalk equal and 
unequal channel spacing is also used which is said to 
be spectrum separation technique. The comlexity is 
lower in this technique. But the mean blocking prob-
ability is lesser and fitness score is better in the dynam-
ic wavelength allocation based on FWM aware based 
priority assignment that makes it advantageous to be 
used.  
Figure 5: Mean blocking probability for a fixed net-
work load using GA
Figure 6: Mean blocking probability for a fixed net-
work load using SFLA
Fig.7 shows the rate of convergence of genetic algo-
rithm and shuffled frog leaping algorithm for first fit, 
random, round robin, wavelength ordering and FWM 
aware priority based wavelength assignment tech-
niques. By randomly selecting an individual and fix-
ing the best fitness value, the curves are plotted. The 
average fitness score decreases with increase in gen-
erations. Average fitness score for GA and SFLA using 
different wavelength assignment techniques are ap-
proximately the same. FWM priority based assignment 
has higher average fitness score.
R. Hemalatha et al; Informacije Midem, Vol. 47, No. 4(2017), 233 – 240
238
Figure 7: Average fitness score for GA and SFLA
The mean execution time of the five wavelength as-
signment techniques for different network load in Er-
lang using GA and SFLA are in Table 1. FWM aware pri-
ority based wavelength assignment technique has the 
least mean execution time for different network loads 
in both the algorithms. However, the mean execution 
time is minimum using SFLA compared to GA.
The experimental results in Table 2 show that wave-
length ordering and round robin exhibits less mean 
blocking probability with respect to channel rejection 
ratio and number of generations respectively. Average 
fitness score is higher and the mean execution time 
is minimum in FWM aware priority based wavelength 
assignment technique. Comparing Genetic Algorithm 
and Shuffled Frog Leap Algorithm, Shuffled Frog Leap 
Algorithm achieves least mean blocking probability 
and a mean execution time for different wavelength as-
signment techniques such as First Fit, Random, Round 
Robin, Wavelength Ordering and FWM aware priority 
based wavelength assignment though the average fit-
ness score is approximately same for both the algo-
rithms.
5 Conclusions
Routing and Wavelength Assignment (RWA) problem 
is one of the most complex optimization problems in 
optical networks. In the proposed work, Genetic Algo-
Table 1: Mean Execution Time of different wavelength assignment techniques using GA and SFLA
Wavelength As-
signment Tech-
niques
Mean Execution Time (w.r.to network 
load(Erlang)) using GA
Mean Execution Time (w.r.to network 
load(Erlang)) using SFLA
0 1.4 2.7 3.6 4.4 0 1.4 2.7 3.6 4.4
First Fit 0.1200 0.0432 0.0689 0.1038 0.1019 0.1191 0.0423 0.0654 0.1018 0.1003
Random 0.3000 0.2372 0.2328 0.3155 0.3762 0.2987 0.2372 0.2328 0.3155 0.3762
Round Robin 0.1200 0.1736 0.1613 0.2004 0.2251 0.1198 0.1703 0.1610 0.2001 0.2248
Wavelength Ordering 0.0500 0.0062 0.0123 0.0302 0.0415 0.0490 0.0059 0.00117 0.0295 0.0409
FWM priority based 
Assignment
0.0050 4.275e-11 8.509e-11 2.1167e-10 2.94e-10 0.038 4.257e-11 8.503e-11 2.1068e-10 2.85e-10
Table 2: Comparison of GA and SFLA for different wavelength assignment techniques
Wavelength 
Assignment 
Techniques
GA SFLA
Mean 
Blocking 
Probabil-
ity (w.r.to 
channel 
rejection 
ratio)
Average 
fitness 
score
Mean Block-
ing Probabil-
ity (w.r.to no. 
of genera-
tions)
Mean 
Execution 
Time
Mean 
Blocking 
Probability 
(w.r.to chan-
nel rejection 
ratio)
Average 
fitness 
score
Mean Block-
ing Probabil-
ity (w.r.to no. 
of genera-
tions)
Mean 
Execution 
Time
First Fit 0.8910 2.1184 0.5176 0.0829 0.8899 2.1184 0.4508 0.008025
Random 0.7875 0.6087 0.6035 0.2711 0.7802 0.6087 0.5805 0.2709
Round Robin 0.5225 1.023 0.2018 0.1619 0.1009 1.023 0.0010 0.1618
Wavelength 
Ordering
0.0959 0.08175 - 0.0266 0.0947 0.08175 - 0.0256
FWM based 
Assignment
- 12.481 - 0.00070 - 12.481 - 0.00067
R. Hemalatha et al; Informacije Midem, Vol. 47, No. 4(2017), 233 – 240
239
rithm and Shuffled Frog Leaping Algorithm are used 
to solve this problem. The fitness function minimizes 
the cost, number of hops and blocking probability. 
Five different wavelength assignment techniques such 
as first fit, random, round robin, wavelength ordering 
and FWM aware priority based wavelength assignment 
are used while evaluating the performance of GA and 
SFLA. 
In SFLA, better fitness value is achieved compared to 
GA. Among different wavelength assignment tech-
niques, FWM aware priority based wavelength as-
signment technique gives maximum average fitness 
score and least mean execution time. Comparing these 
optimization algorithms, SFLA is better than GA with 
minimum mean blocking probability, less mean execu-
tion time and better fitness value. SFLA approach has a 
lower time complexity compared to Genetic Algorithm 
and hence the proposed scheme may provide certain 
degree of flexibility in the network design.
6 References
1. A. Adhya and D. Datta, 2009. Design methodol-
ogy for WDM backbone networks using FWM-
aware heuristic algorithm. Optical Switching and 
Networking, 6: 10–19.
2. C. W. Ahn and R. S. Ramakrishna, 2002. A genetic 
algorithm for shortest path routing problem and 
the sizing of populations. IEEE Transactions on 
Evolutionary Computation, 6: 566–579.
3. A´ lvaro Rubio-Largo, Miguel A. Vega- Rodriguez, 
Juan A. Gomez-Pulido and Juan M. Sanchez-
Pe´rez, 2012. A Comparative Study on Multi ob-
jective Swarm Intelligence for the Routing and 
Wavelength Assignment Problem. IEEE Transac-
tions on systems, man, and cybernetics , 4: 1644–
1655.
4. David Bisbal, Ignacio de Miguel and Fernando 
Gonzalez, 2004. Dynamic Routing and Wave-
length Assignment in Optical Networks by Means 
of Genetic Algorithm. Photonic Network Commu-
nications, pp: 43-58.
5. G. Giftson Samuel and C. Christober Asir Rajan, 
2014. A Modified Shuffled Frog Leaping Algo-
rithm for Long-Term Generation Maintenance 
Scheduling. Springer, 258: 11-24.
6. Joan Triay and Cristina Cervello-Pastor, 2010. An 
Ant-Based Algorithm for Distributed Routing and 
Wavelength Assignment in Dynamic Optical Net-
works. IEEE journal on selected areas in communi-
cations , 28: 542-552.
7. V. T. Le, X. Jiang, S. H. Ngo and S. Horiguchi, 2005. 
Dynamic RWA Based on the Combination of Mo-
bile Agents Technique and Genetic Algorithms in 
WDM Networks with Sparse Wavelength Conver-
sion. IEICE Transactions on Information and Sys-
tems, 9: 2067-2078.
8. Muzaffar, Kevin and Fayzul Pasha, 2006. Shuffled 
frog-leaping algorithm: a memetic meta-heuristic 
for discrete optimization. Engineering Optimiza-
tion, 38, 2: 129–154.
9. R. Ramaswami and K.N. Sivarajan, 2000. Optical 
Networks: A Practical Perspective. Morgan Kauf-
mann Publishers, San Francisco.
10. D. Srinath and J. Janet, 2013. Secured Ant Colony 
Optimization Routing for Wireless Network. Asian 
Journal of Information Technology, 12: 83-90.
11. Urmila Bhanjaa, Sudipta Mahapatra and Rajar-
shi Roy, 2010. A novel solution to the dynamic 
routing and wavelength assignment problem in 
transparent optical networks. International Jour-
nal of Computer Networks and Communications, 
2: 119-130.
12. Urmila Bhanjaa, Sudipta Mahapatra and Rajarshi 
Roy, 2012. FWM aware evolutionary program-
ming algorithm for transparent optical networks. 
Photonic Network Communications, 3: 285-299.
13. Urmila Bhanjaa and Sudipta Mahapatra, 2013. A 
metaheuristic approach for optical network op-
timization problems. Elsevier-Applied Soft Com-
puting, 13: 981–997.
14. Urmila Bhanjaa, Sudipta Mahapatra and Rajarshi 
Roy, 2013. An evolutionary programming algo-
rithm for survivable routing and wavelength as-
signment in transparent optical networks. Elsevi-
er-Information Sciences, 222: 634–647.
15.  X. Wang, M. Brandt-Pearce, and S. Subramaniam, 
2014. Distributed Grooming, Routing, and Wave-
length Assignment for Dynamic Optical Networks 
Using Ant Colony Optimization. IEEE/OSA Journal 
of Optical Communications and Networks, 6: 578-
589.
16. Y. S. Kavian ,W. Ren , H. F. Rashvand, M. S. Leeson , 
M. Naderi and E. L. Hines, 2009. Genetic Algorithm 
for Designing DWDM Optical Networks under De-
mand Uncertainty.  Proceedings of ICTON Medi-
terranean Winter  Conference, December 10-12, 
2009, Angers, pp: 1-4.
17. Yousef S. Kavian, Arash Rashedi, Ali Mahani and 
Zabih Ghassemlooy, 2013. Routing and wave-
length assignment in optical networks using Ar-
tificial Bee Colony algorithm. Elsevier-Optik, 124: 
1243-1249.
18. Roshni. V. V, R. Hemalatha and R. Mahalakshmi, 
2016. Optimization of Routing and Wavelength 
assignment in passive optical networks. Pak. J. 
of Biotechnology, Special issue on innovations in 
information embedded and communication sys-
R. Hemalatha et al; Informacije Midem, Vol. 47, No. 4(2017), 233 – 240
240
tems, Vol. 13: 247-251.
19. B. Batagelj, V. Janyani and S. Tomazic, 2014. Re-
search Challenges in optical communications to-
wards 2020 and beyond. Informacije MIDEM, Vol. 
44:177-184.
20. M. Vidmar, 2001. Optical-fiber communications: 
components and systems. Informacije MIDEM, 
Vol. 31:246-251.
Arrived: 04. 07. 2017
Accepted: 03. 10. 2017
R. Hemalatha et al; Informacije Midem, Vol. 47, No. 4(2017), 233 – 240