close

Вход

Забыли?

вход по аккаунту

?

PARCOMPTECH.2017.8068332

код для вставкиСкачать
An Improved Firefly Algorithm for Permutation Routing in Baseline Multistage
Interconnection network
Abhay B. Rathod
Sanjay M. Gulhane
Department of Electronics and Telecomm. Engg
Jawaharlal Darda Institute of Engg. and Tech.
Yavatmal, India.
Department of Electronics and Telecomm. Engg
Jawaharlal Darda Institute of Engg. and Tech.
Yavatmal, India.
Abstract— This paper present the parallelization of firefly
algorithm for permutation routing in baseline multistage
interconnection network. The proposed parallel firefly algorithm
employs a self routing approach to route the permutation in
baseline network. The objective of this paper is as follows (i) to
improve serial firefly algorithm to parallel one for better
utilization of multi core/ many core processor. (ii) to use speedup,
quality of solution and efficiency as performance metric in
improved firefly algorithm for optimal permutation routing in
baseline network. (iii) the analysis of the simulation results on
firefly algorithm is compared with other algorithms. Simulation
results demonstrate that the proposed parallel firefly algorithm
for permutation routing in baseline network
outperforms
known sequential and parallel algorithm in terms of speedup,
efficiency and quality solution.
Index Terms— Firefly algorithm, permutation, multistage
interconnection network, self-routing, Latin square, baseline
network, multi core processor, optimization, parallel processing
mutation, simulation.
I. INTRODUCTION
To gain the advantage of multi/ many core processors and
to explore thread level parallelism of an application one of the
best solution is to shift from sequential algorithm to parallel
one. Parallel implementation of sequential algorithm is
becoming more popular in order to improve the efficiency of
sequential algorithm. In this paper a parallelization of firefly
algorithm is introduced for permutation routing in baseline
multistage interconnection network.
Firefly algorithm is one of the latest bio-inspired algorithms
developed by Yang [21]. It is highly suitable for parallel
implementation as different firefly work independently [5].
Firefly algorithms are simple, distributed and do not have
central control mechanism which allows the system to become
more scalable. This motivated us to check the permutation
realization capabilities of baseline networks using fire fly
algorithm.
Firefly algorithm is an optimization method and it is found
to be efficient in solving the permutation combinatorial
optimization problems [4]. The main aim of the optimization
method is to find the most suitable value of a function. The
optimization methods can be divided into two major groups:
deterministic and stochastic methods. The majority of
deterministic methods always produce the same set of solutions
(optimal solution) if the algorithm start under the same initial
conditions [21]. In turn, the stochastic methods under same
initial condition may not generate the same optimal solutions.
Firefly algorithm belongs to the second group, that is,
stochastic algorithms [1]. To the best of our knowledge, this is
the first time that an improved firefly algorithm is applied for
permutation routing in baseline multistage interconnection
network.
Bigger is the routing problem in multistage interconnection
network (MIN), permutations is one of the fundamental
solution. Permutation is an important communication pattern in
parallel and distributed computing systems [6]. Permutation
represents a one-to-one mapping among the processors for
communication in a processor group.
To keep the communication step overhead minimum,
parallel algorithms are often designed with permutation type
data transfers [15]. We can realize a permutation operation in
MIN as it has N inputs and N outputs. There has been much
research work in the literature on the permutation capability of
various multistage networks [9]. However none of the paper
published on permutation realization capability of MIN using
firefly algorithms running on multi-core processor.
A class of unique-path, self-routable multistage
interconnection networks, such as baseline, omega, and
Banyan networks, have been proposed and widely used in
parallel processing systems [6]. Due to their easy routing
capability and uniform communication latency between any
network inputs and outputs, these types of MIN has been used
in several parallel computers, such as NEC Cenju-3 and IBM
SP [11].
In this paper, we are realizing permutation routing in
baseline network because this class of multistage network have
unique-path, low hardware cost O(n log n), low network
latency, self-routing capability [8] and the routing for
permutations is efficient (in O(log n) time complexity) [9].
The paper is organized as follows: Section 1 gives
introduction about the paper topic. Section 2 describes the
structure of baseline network and the concepts of realizing
permutations in such networks. In Section 3, self routing in
baseline network and application of Latin square for
permutation routing is presented. The section further provides
the detailed algorithm for implementing parallel firefly
algorithm in network under consideration. Simulation results
will be presented in Section 4. Finally, Section 5 concludes the
paper.
II.
PRELIMINARIES
A. Baseline Network Structure
A switch is the basic elements in a network. A baseline
network consists of several stages of switching. In switching
network the outputs of one stage are connected to the inputs of
the next stage. A switch with N inputs, and N outputs can
realize N!.
For a class of unique-path, self-routable multistage
network, such as a baseline network, which consists of log n
stages of 2 X 2 switches, routing for permutations can be
performed in the most efficient way [6]. Parker in [6] showed
that the set of admissible permutations of a baseline network is
equal to that of its reverse network. This shows that any
permutation admissible to a baseline network is also admissible
to a reverse baseline network, and vice versa. A network
belonging to this class satisfies the following basic properties
[7]:
1. The switching elements are 2 × 2 crossbar switches.
2. There is a unique path between each input and each
output.
3. There are n stages of switches, each stage contains L
switches or L levels, and each level contains n switches,
where n = log2 N, L = N/2, and N = number of inputs
or outputs.
Because of the above three properties (short connection
diameter, unique connection path, uniform modularity, etc.),
baseline network is very attractive for constructing switching
networks.
As disused above baseline network posses a common
feature that there is as unique path between each pair of
network inputs and network outputs. In some literature, it is
also called banyan property. This property enables the baseline
network to have self routing capability which makes a very fast
routing possible [8]. We will elaborate self routing algorithm in
the next subsection.
B. Self-Routing in Baseline Network
In this paper the proposed firefly algorithm employs self
routing approach to route the permutation in baseline network.
In self routing concept each switching elements operates
independently. The characteristic of firefly algorithm is also
self–organization and decentralized decision making [1]. These
characteristic is highly suitable for self-routing (i.e. setting
switches while routing) approaches to route permutations in a
baseline network (or a reverse baseline network).
A switching network is a self-routing network if any
connection can be established only by the addresses of its
source and destination regardless of other connections. A MIN
that can determine the path of a message from source to
destination on the fly, using simple computational steps within
the switches, is known as a self-routing MIN [12]. To establish
connection self routing is an attractive feature of MIN in that
no complicated control mechanism is needed for establishing
connection [7].
Fig. 1. Demonstration of 8 X 8 baseline network with self routing between
S=000 and D=101.
Figure 1 shows the connection between Source address =
000 and Destination address = 101. A binary routing tag of D
can be used to achieve self-routing, with the ith bit of the tag
used to select the upper output (0) or the lower output (1).
In next section we will discuss Permutation as data routing
function and its realization in baseline network. We will also
discuss the application of Latin square for conflict free data
routing in baseline network and at the end of Section 3 we will
describe the implementation of parallel firefly algorithm.
III.
PERMUTATION AS DATA ROUTING FUNCTION
A. Permutation realized in Baseline Network
Permutation is a one-to-one mapping between the network
inputs and outputs. It refers to the connection of a set of
sources to a set of destinations such that each source is
connected to a single destination. A permutation [(s0, d0), (s1,
d1), ......, (s7,d7)] means that source s0 is connected to d0, s1 to
d1, and so on. In permutation notation, there are two rows of
values. The values in the upper row are mapped onto the
elements in the lower row. For example, given a set of values
{0, 1} for which 0 maps to 1 and 1 maps to 0, the permutation,
α, of the set is written as in Eq. (1)
0 1

  
(1)
1 0
Identity permutation β can be represented as in Eq. (2)
 0 1

  
(2)
 0 1
The switch setting for α is called as cross and β is called as
parallel which are illustrated in figure 2.
Fig. 2. Switch Settings in a 2 X 2 switch
As mentioned before, permutation notation is one of the
common methods used to describe the mappings in a switch,
stage, and network. To realize a permutation operation in a
parallel or distributed computing system is to use a multistage
interconnection network (MIN) [8] such as baseline network.
A baseline network consists of multiple stages of 2×2
switches with adjacent stages connected by a mapping
function. Consider an 8×8 baseline network with 2×2 switches
and three stages. We refer permutation σi as a stage
permutation, permutation πi as an inter-stage permutation, and
the permutation realized by the entire multistage network as
an admissible permutation of the network. Clearly, an
admissible permutation can be expressed by a composition of
stage permutations and inter-stage permutations. For
examples, an admissible permutation of an n × n network can
be expressed as Eq. (3).
σm-1 πm-2 σm-2…… π0 σ0
(3)
TABLE I. THE ALGORITHM FOR SELF-ROUTING IN BASELINE NETWORK
where σi(0≤i≤m-1) denote the permutation represented by stage
i, and πi(0≤i≤m-2) denote the permutation represented by the
set of interstage links between stage i and stage i+1.
Fig. 3.An example of Permutation Routing in an 8X 8 baseline MIN
In general, inter-stage permutations πis are fixed by the
network topology. However, stage permutation σis are not
fixed since each switch can be set to either parallel (for switch
setting value 0) or crossing (for switch setting value 1) as
shown in Figure 2. Since, in a unique-path multistage network,
routing can be determined solely by the source and destination
addresses [6] we also denote the switch setting for a switch in
stage i on the routing path as σi(s,d).
For input 1, we can obtain the following transformation to
reach output 1 as shown in Eq. (4).
1
σ0
0
π0
0
σ1
0
π1
0
σ2
1
(4)
Figure 3 illustrate the permutation routing from source 1 to
destination 1 in 8 x 8 baseline multistage interconnection
networks. Similarly, we can obtain transformation from 2 to 4,
4 to 2 and 6 to 6 applying composition operations on stage
permutation (σi) and inter-stage permutation (πi).
The algorithm for self routing and composition
operation of stage permutation (σi) and inter-stage permutation
(πi) for realization of admissible permutation is presented in
Table 1.
B. Latin Square
In this paper we use Latin squares to achieve conflict free
access to various subsets of data. Latin squares are well known
combinatorial objects for centuries [16]. Parallel
communication in baseline multistage network is realized by
routing a set of N permutations forming a Latin square [10].
The Latin square is generated by means of an off-line
algorithm run at the time the baseline network is built. The key
idea of the method in [9] is to build a Latin square matrix for a
given multistage network i.e. baseline network and it can be
routed using the tag information (self routing) represented by
the elements in the Latin square matrix.
A Latin square of order n is an n x n square composed with
symbols from 0 to n - 1 such that no symbol appears more than
once in any rows or in any columns [16] as given in Eq. (5).
0 1 2 3 
1 0 3 2 

A
(5)
2 3 0 1 


3 2 1 0  2 2
We assume that for a self-routable multistage network
under consideration, there exists a Latin square such that any
permutation formed by each row of the matrix is admissible to
the network; that is, this permutation is self-routable from the
inputs to the outputs of the network [11].
Although in general there are many ways to construct a
Latin square [11][14][16], in this section we describe one of
the method of constructing the Latin square of an array 2nx 2n
which will be suitable to the class of multistage networks we
consider in this paper.
Theorem: If we take Latin square matrix A of order n x n
and add n in each number row wise and column wise we get
Latin square matrix B.
Proof: Take the matrix of order n x n as show in Eq. (6).
a0,1 ... a0, n 1 
 a0 , 0
 a
a1,1 ... a1, n 1 
1,0
(6)
A
 :
:
:
: 


an 1,0 an 1,1 ... an 1, n 1  n n
Add n in each row we get a new matrix B of order n x n and
repeat this process. Latin square matrix B is given in Eq. (7).
n  a0, n 
 n  a0,1 n  a0, 2 ...
n  a
n  a1, 2 ...
n  a1, n 
1,1
B
 :

:
:
:


n  an,1 n  an, 2 ... n  an 1, n 1  n n
(7)
It is easy to construct Latin square matrix B from A. We give
an algorithm to generate Latin square matrix B from A in Table
2. Its correctness can be seen from (9). Notice that we only
need to build B once (as for A) for a given multistage network
and both A and B can be considered as system parameters used
in our permutation algorithm. The algorithm to generate Latin
square is given in Table 2.
TABLE II. THE ALGORITHM TO GENERATE LATIN SQUARE B FROM A
To construct Latin square C of order 2n×2n place the matrix A
and B in such a way that A and B will appear more than once in
any row or in any column. It is given in Eq. (8).
 A B
C
(8)

 B A 2 n  2 n
From above described method we can construct Latin square C
for 8x8 baseline network given in (9).
0 1 2 3 4
1 0 3 2 5

2 3 0 1 6

3 2 1 0 7
C
4 5 6 7 0
5 4 7 6 1

6 7 4 5 2

7 6 5 4 3
Latin square C is generated only
network, and can be viewed as a
network.
5 6 7
4 7 6
7 4 5

6 5 4
(9)
1 2 3
0 3 2

3 0 1

2 1 0
once for a given multistage
system parameter [9] of the
We can design baseline network under the assumption that for
a self routable multistage networks, there exists a Latin square
such that any permutation formed by each row of the matrix is
admissible to the network; that is, this permutation is self
routable from the inputs to the outputs of the network.
However, baseline MIN is not rearrange-able [10] that is it
cannot realize the entire N! possible permutations, but only
few of them.
C. Firefly Algorithm
Firefly algorithm is efficient and an easy to implement
algorithm. It is also suitable for parallel implementation with
few modifications [1]. However, original firefly algorithm is
slow in convergence and easily gets trapped in local optimum
for multimodal problems [26]. In addition, since the parameters
are fixed, the search behavior remains to be the same for any
condition in all iterations. Hence modifying the standard firefly
algorithm to boost its performance has been one of the research
issues. In this paper we are modifying two parameters one is
parallelization of firefly algorithm and second is modifying the
Euclidean distance to squared Euclidean distance. The detailed
procedure of the proposed algorithm is as follow and it is
described in Table 3.
 All fireflies are unisex.
 All fireflies are of same size.
 Their attractiveness is proportional to their light
intensity.
 The light intensity of a firefly is affected or
determined by the landscape of the fitness function.
1) Objective function
Each solution in the search space is associated with a
numeric objective values. So, the quality of a solution is
proportional to the value of the objective function. In the case
of the permutation routing in baseline network, the quality of
solution is related to the Latin square. The best solution is the
one that replicate the Latin square.
2) Attractiveness
The attractiveness of a firefly is determined by its light
intensity. Light intensity is a value that represents the objective
function of a problem. The objective function of permutation
routing is Latin square and admissible permutation.
3) Distance
There are two possible ways to measure the distance
between two permutations fireflies:
a) Squared Euclidean Distance
The Square
Euclidean distance
between
two
points, a and b, with k order matrix is given in Eq. (10).
k
ri j 
 a b
i
i
TABLE III. THE ALGORITHM FOR PARALLEL FIREFLY ALGORITHM
2
(10)
i 1
b) Swap distance
It is number of the required swaps of the first solution in
order to get the second one.
The swap distance is an edit-distance, counting how
many edit operation (here: swaps, i.e., transposition of two
adjacent elements) have to be performed to transform
permutation a into permutation b.
We need such measurement of the distances; so that
the closer permutations/solutions are to one another, the
smaller difference of their objective functions is produced.
4) Movement
The movement of a firefly i attracted to another brighter
(more attractive) firefly j is determined by Eq. (11).
 =  (2, rij )
(11)
Where, xi is the step that must be taken by firefly i to move
towards j, and rij is distance between firefly i and j. The length
of movement (step) of a firefly will be randomly selected from
2 to rij using uniform distribution. When a firefly moves,
existing solutions in the firefly is changed. Since the
representation of firefly is a permutation representation, then
we use inversion mutation to represent the movement.
Mutation prevents the algorithm to be trapped in a local
minimum. With inversion mutation, the solution that has been
obtained can be maintained so the good solution formed
previously is not changed.
The objective function of each firefly population obtained
in the first generation replaces its previous value (P i.e.
permutation value), if its current solution is better than the
previously stored firefly solution. The best solution of the first
iteration replaces the global best firefly solution (Pbest i.e.
Latin square value) if it is better than the previously stored
global best solution. The procedure is repeated until the
termination criterion is satisfied. In this study, the termination
criterion is the total number of generations.
In this paper implementation of improved firefly algorithm
in baseline interconnection network for permutation routing is
based on the static information associated with Latin square
matrices and self routing property of MIN. The performance of
improved firefly algorithm is compared with conventional
sequential and parallel algorithm.
IV.
PERFORMANCE EVALUATION AND RESULTS
The performance of MIN is usually determined by
modeling, simulation [3], mathematical [8], [15], [20] and
graphical methods [18]. The graph models are very helpful in
comparing the permutation capabilities of various MIN's. They
provide an easy way of resolving the conflicts and for
designing a suitable MIN [18]. The graph model is also useful
in testing of MIN, and the network partitioning could be easily
done for the multiple SIMD(single-instruction stream, multiple
data stream)/MIMD(multiple instruction stream, multiple data
stream) [18] computation modes. However, if the network size
in MIN increases it becomes difficult to represent it
graphically.
In mathematical modeling exact performance results can be
obtained from it if the models are simple enough. As the
network model becomes more complex and large the
performance results obtained by those models may become
inaccurate [3]. Additionally, we cannot investigate many
network parameters because they have to be neglected and they
are not included into the model.
In consequence, simulation gives an alternative to graphical
and mathematical methods. Its advantages are a more detailed
network description. Nevertheless, simulations of very large
networks suffer from long simulation run times. However, for
reasonable network sizes and in all cases, where mathematical
and graphical methods cannot be applied, simulation is a good
option [3]. Still, to our knowledge, no one has presented
simulation result on permutation routing in baseline MINs
using multi-core environment, which is the main focus of this
paper.
In the proposed work, we improved the distance formula
from Euclidean to Square Euclidean distance for a nature –
inspired firefly algorithm so that in large network search space,
distance between two fireflies can be calculated. The
simulation environment and the performance results are
described in next sub section.
A. Simulation Result
The Simulation results obtained based on effectiveness of
an algorithm and speedup of an algorithm. Effectiveness is
defined as a high probability of finding a high quality solution
[5][13]. Here, the quality of a solution (Q) is measured by how
close the solution is to the known global solution as shown in
Eq. (12).
Solution  KnownSolui on
%
(12)
KnowSoluti on
In parallel processing speedup emphasizes time reduction
of a given problem and it is defined as sequential execution
time over parallel execution time [2]. It is given in Eq. (13).
Sequential Time of Solving Scaled Workload
(13)
Speedup (S ) 
Parallel Time of Solving Scaled Workload
The Simulation is carried out on Intel(R) Core(TM) i3 CPU
with 2.53 GHz, 3.00 GB RAM. We have implemented
improved firefly algorithm using Java 7 language under 32 bit
Windows 7 operating system, and Eclipse 3.7 as an integrated
development environment (IDE). As Java support
multithreading, here we considered one thread as one core.
Q
TABLE V. RESULT OF COMPUTATION EXPERIMENTS OF FIREFLY ALGORITHM
Simulations of very large networks suffer from long
simulation run times, particularly if stochastic events are
involved [3] and confidence levels must be fulfilled. The
simulation results, as presented in Table 5, shows that as the
network size increases above 7x7 the speedup decreases but
quality of solution gets improved.
In Figure 5, the horizontal axis represents the number of
cores, scaled from 2 to 8. The vertical axis represents the
speedup value. As shown clearly from Figure 4, the speedup
illustrates a very limited scalability of a multi-core architecture,
and the speedup is quickly restricted by the sequential portion
of a problem under study i.e. parallelization of firefly algorithm
for permutation routing in baseline multistage interconnection
network.
TABLE IV. RESULT OF COMPUTATION EXPERIMENTS OF PARALLEL
ALGORITHM
FIG. 5. ILLUSTRATES THE SPEEDUP OF MULTI-CORE ARCHITECTURES FOR
PARALLEL AND PARALLEL FIREFLY ALGORITHM.
V.
As we can see from Table 4, result obtained by parallel
approach is better than results generated by traditional
serial approach.
CONCLUSION
In this paper, we have presented an improved parallel
firefly algorithm for permutation routing in baseline multistage
interconnection network. Self-routing is used to route the
permutations. The performance of improved parallel firefly
algorithm is measured on the basis of speedup and quality of
solution. We examined speed improvement, as well as the
quality of solution improvement.
Comparisons with sequential results has demonstrated that
routing with parallel firefly algorithm could be a better choice
for routing permutations in baseline network due to its better
quality solution and it achieve speedup up to 4 times with
reasonable size network 7 X 7.
A recent trend in computer architecture is the move from
traditional, single-core processors to multi-core processors and
further to many-core or massively multi-core processors. The
result of this trend is that research community can take
advantage of this technology by modifying sequential
algorithm to parallel one and performing parallel computation
on GPUs that has traditionally been done on CPUs.
As a plan for further research, we will try to enhance the
original Genetic algorithm and parallelize it in the same way
we did with the original firefly algorithm. Also, to get better
result we will test our algorithms on more number of cores.
ACKNOWLEDGMENT
The authors would like to thank the anonymous reviewers of
this article for their comments and suggestions.
REFERENCES
[1] I. Fister, I. Fister Jr., X. Yang, and J. Brest, "A comprehensive
review of firefly algorithms," Swarm and Evolutionary
Computation 13, pp. 34-46, June 2013.
[2] X. Sun and Y. Chen, "Reevaluating Amdahl’s law in the
multicore era," Journal of Parallel and Distributed Computing,
pp.183-188, May 2009.
[3] D.Lüdtke and D. Tutsch, "The modeling power of CINSim:
Performance evaluation of interconnection networks," Computer
Networks, pp.1274-1288, March 2009.
[4] P. Vidal and A. C. Olivera, "A Parallel Discrete Firefly
Algorithm on GPU for Permutation Combinatorial Optimization
Problems," Latin American High Performance Computing
Conference Springer Berlin Heidelberg, pp.191-205, 2014.
[5] X. Yang, “Nature-inspired metaheuristic algorithms,” Luniver
press, 2010.
[6] Y. Yuanyuan, and J. Wang, "Routing permutations on baseline
networks with node-disjoint paths," IEEE Transactions on
Parallel and Distributed Systems, vol. 16, no. 8, pp.737-746,
August 2005.
[7] E. Lu, and S. Q. Zheng, "Parallel routing algorithms for
nonblocking electronic and photonic switching networks," IEEE
Transactions on Parallel and Distributed Systems, vol. 16, no.
8, pp.702-713, August 2005.
[8] Y. Yuanyuan, and J. Wang,"A class of multistage conference
switching networks for group communication," IEEE
Transactions on Parallel and Distributed Systems, vol. 15, no. 3,
pp.228-243, March 2004.
[9] Y. Yuanyuan, and J. Wang,"Routing permutations with linkdisjoint and node-disjoint paths in a class of self-routable
interconnects," IEEE Transactions on Parallel and Distributed
Systems , vol. 14, no. 4, pp. 383-393, April 2003.
[10] A. Massini, "All-to-all personalized communication on
multistage interconnection networks," Discrete applied
mathematics, pp.435-446, June 2002.
[11] Y. Yuanyuan, and J. Wang,"Optimal all-to-all personalized
exchange in self-routable multistage networks," IEEE
Transactions on Parallel and Distributed Systems, vol. 11, no.
3, pp.261-274, March 2000.
[12] B. Parhami, “Introduction to parallel processing: algorithms
and architectures,” Springer Science & Business Media, 2006.
[13] R.Hassan, B. Cohanim, O. de Weck, and G. Venter, "A
comparison of particle swarm optimization and the genetic
algorithm," In Proceedings of the 1st AIAA multidisciplinary
design optimization specialist conference, pp.18-21, 2005.
[14] K. Heinrich, K. Kim, and V.K. Prasanna Kumar, "Perfect Latin
squares," Discrete applied mathematics, pp.281-286, April
1990.
[15] C. S.Raghavendra, and R. V. Boppana. "On self-routing in
Benes and Shuffle-exchange networks," IEEE Transactions on
Computers vol.40, no.9, pp.1057-1064, September1991.
[16] K. Kim, and V. K. Prasanna-Kumar, "Perfect Latin squares and
parallel array access," ACM SIGARCH Computer Architecture
pp.372-379,1989.
[17] Ted H. Szymanski, and V. Carl Hamacher, "On the permutation
capability of multistage interconnection networks," IEEE
Transactions on Computers, vol. C-36, no. 7, pp. 810-822, July
1987.
[18] D. P. Agrawal, “Graph Theoretical Analysis and Design of
Multistage Interconnection Networks,” IEEE Transaction on
Computers, vol. c-32, no. 7,pp. 637-648, July 1983.
[19] D. Nassimi and S. Sahni, "A self-routing Benes network and
parallel permutation algorithms," IEEE Transactions on
Computers, vol. c-30, no. 5, pp.332-340, May 1981.
[20] Y. Yuanyuan, and J. Wang,"Routing permutations on optical
baseline networks with node-disjoint paths," Tenth International
Conference on Parallel and Distributed Systems,2004.
[21] S. Arora, and S. Singh, "The firefly optimization algorithm:
convergence analysis and parameter selection," International
Journal of Computer Applications, vol.69, no.3, pp.48-52, May
2013.
[22] K. Day, N. Alzeidi, B. Arafeh, and A. Touzene, “A Parallel
Routing Algorithm for Torus NoCs” 2012 International
Conference on Computer Networks and Communication
Systems, vol.35 IACSIT Press, Singapore, 2012.
[23] F. Bistouni1, and M. Jahanshahi, “ Pars Network: A Multistage
Interconnection Network with Fault-Tolerance Capability”, J.
Parallel Distrib. Comput., in press.
[24] M. Borkar and Nitin, “Realizing frequently used permutations
on gamma interconnection network’s family networks with the
help of alternate source”, Springer Science Business Media New
York, 2015.
[25] T. Jain, K. Schneider, and A. Bhagyanath, “The Selector-Tree
Network: A New Self-Routing and Non-Blocking
Interconnection Network”, IEEE, 2016.
[26] W. A. Khan, N. N. Hamadneh, S. L. Tilahun and J. M. T.
Ngnotchouye, “A Review and Comparative Study of Firefly
Algorithm and its Modified Versions”, in Optimization
Algorithms- Methods and Applications, Intech, 2016, pp. 282313.
Документ
Категория
Без категории
Просмотров
3
Размер файла
3 153 Кб
Теги
2017, parcomptech, 8068332
1/--страниц
Пожаловаться на содержимое документа