fouad_kharroubi_PhD_Defense_05_06_2014.ppt

School of Information Science and Engineering
Hunan University, Changsha 410082, China
2013-2014
Random Search Algorithms for Solving the Routing
and Wavelength Assignment
in WDM Networks
Presented by the PhD Candidate :
Fouad KHARROUBI (方达)
Supervisors:
Professor Chen Lin and Assistant Prof Jing He
June 2014
PhD Defense

• Introduction
• Thesis Contributions
• Published Papers
• Conclusion and Future Work
Outline
Fouad KHARROUBI June 2014 2

Background (1)
Introduction
Thesis Contributions
Published Papers
Conclusion and Future Work
The huge demand for higher
bandwidth speeds has
transformed the cabling
technology

Background (2)
Introduction
Published Papers
WDM

Background(3)
Introduction
Published Papers
WDM
RWA
New Technology
New Problem
The Wavelength
Clash Constraint
The Wavelength
Continuity
Constraint

Background (3)
Routing
Wavelength Assignment
K-shortest Path
shortest Path
Backtracking
Random methods
Exact methods
Hybrid
methods
Background
Published Papers
RWA Subproblems

Background
Published Papers
Therefore, we investigated four random search algorithms combined with the
Backtracking algorithm to solve the Max-RWA problem in WDM networks. The
contributions of our conducted research are as follows:
1. A new mathematical formulation for the Max-RWA problem in WDM optical
networks is proposed and its constraints are analyzed
2. Four efficient random search algorithms are proposed and experimentally
demonstrated to solve the problem of Max-RWA in WDM optical networks. Namely:
ROA, GA, TSA and EP;
3. To the best of our knowledge, a novel efficient Backtracking algorithm is proposed
and investigated in WDM optical network for the first time.
4. A huge number of experiments that consist of 1080 extensive tests are conducted in
the first part of our experiments and 480 tests in the second part by running ROA,
GA, TSA and EP simultaneously under different circumstances.

1. Mathematical Formulation
Problem
. To simplify the problem
. To formulate and solve the problem
Our Aim
. To maximize the number of optical connection requests that can be established
for a given number of wavelengths on a given physical topology.
Proposed Solution
. A simple mathematical formulation that can be used to solve the problem
efficiently.
Background
Thesis Contributions (1)
Published Papers

. The Max-RWA problem can be mathematically formulated as follows:
Background
Published Papers

Background
Published Papers

2. Random Search Algorithms
Problem
. To solve approximately the RWA problem using several metheuristics.
Our Aim
. To maximize the number of optical connection requests that can be established
for a given number of wavelengths on a given physical topology.
Proposed Solution
. four random search algorithms have been proposed to solve the RWA problem.
Namely: ROA, GA, TSA and EP.
Background
Published Papers

Part I: Local search algorithms
1. Random Optimization
2. Simple Descent
3. Deepest Descent
4. Multi-start Descent
5. Variable Neighborhood Search (VNS)
6. Tabu Search (TS)
Part II: Stochastic search algorithms
1. Simulated annealing
2. Threshold Accept
Part II: Evolutionary algorithms
1. Evolutionary programming
2. Evolution Strategy
3. Genetic algorithms
4. Genetic programming
5. Learning classifier system
Part III: Inspired algorithms
1. Stochastic Diffusion Search
2. Generalized external optimization
3. Harmony search
4. Ant colony optimization
5. Differential evolution
6. Particle swarm optimization
7. Invasive weed optimization algorithm
8. Gaussian adaptation
Background
Published Papers

Part I: Local search algorithms
1. Random Optimization
2. Simple Descent
3. Deepest Descent
4. Multi-start Descent
5. Variable Neighborhood Search (VNS)
6. Tabu Search (TS)
Part II: Stochastic search algorithms
1. Simulated annealing
2. Threshold Accept
Part II: Evolutionary algorithms
1. Evolutionary programming
2. Evolution Strategy
3. Genetic algorithm
4. Genetic programming
5. Learning classifier system
Part III: Inspired algorithms
1. Stochastic Diffusion Search
2. Generalized external optimization
3. Harmony search
4. Ant colony optimization
5. Differential evolution
6. Particle swarm optimization
7. Invasive weed optimization algorithm
8. Gaussian adaptation
Background
Published Papers

Our Proposed Random Optimization Algorithm
The Random Optimization Algorithm (ROA) is a local search method which is known for
its simplicity, speed in terms of execution time as well as its effectiveness in terms of
quality of solutions found.
Step (1): Initialization
Step (2): Wavelength Assignment
Step (3): Update of the found solution
Step (4): Copy back of the last found solution
Background
Published Papers

Background
Published Papers
Our Proposed Genetic Algorithm
The Genetic Algorithm (GA), proposed in the 1970s by John Holland at University of
Michigan, is a particular class of evolutionary algorithms and it is considered as a random
search algorithm that imitates the process of biological evolution in order to solve
combinatorial optimization problems.
Step (2): Crossover
Step (3): Mutation

Our Proposed Genetic Algorithm (Crossover Operator)
Background
Published Papers

Our Proposed Genetic Algorithm (Mutation Operator)
Background
Published Papers

Tabu Search Algorithm (TSA) is an iterative random search algorithm that can be used for
solving combinatorial optimization problems. This metaheuristic developed by Glover uses a
local search procedure in which we will move iteratively from an initial random solution to
another better solution in the neighborhood of the former one. TSA is useful to help the search
move away from previously visited portions of the search space and thus perform more
extensive exploration.
Step (2): Primary Solutions
Step (3): Wavelength Assignment
Step (4): The main loop
Step (5): Choose the best solution non-tabu list
Background
Published Papers
Our Proposed Tabu Search Algorithm

Our Proposed Evolutionary Programming
This random optimization metaheuristic is relatively similar to Genetic algorithm (GA) since
both of them imitate the process of biological evolution in order to solve combinatorial
optimization problems. For this purpose EP exploits a myriad of techniques (also known as
operators) inspired by natural evolution, such as selection, mutation, replacement so that it
can generate the best approximate solutions to optimization problems.
Step (2): Mutation
Step (3): Update of the found solution (Evaluation)
Step (4): Copy back of the last found solution (Replacement)
Background
Published Papers

3. New Backtracking Algorithm
Background
Published Papers
Problem
. To solve the routing subproblem by generating all the possible lightpaths.
Our Aim
. To give more chance for each connection request to be satisfied by generating
more lightpaths.
Proposed Solution
. A new backtracking is proposed for the first time to the best of our knowledge
in combination with ROA, GA, TSA and EP.

Background
Published Papers

Background
Published Papers
Step (1): The Stopping Condition
Step (2): We keep the new lightpath into the list of all lightpaths
Step (3): Backtrack

Problem
. To evaluate the performance of our proposed algorithms.
Our Aim
. To carry out an experiment that consist of 1080 extensive tests by running
ROA, GA and TSA simultaneously on randomly-generated topology networks
(case 1) as well as a on fixed-generated topology networks (case 2) which are
composed of 4, 8, 14, 19, 25 and 38 nodes for each of the cases.
Proposed Solution
. We run ROA, GA and TSA simultaneously under different conditions.
Background
Published Papers
4. Experiments (Part I)

The hardware used for our experiments is an Intel(R) Core(TM) i5-3470 CPU
3.20 GHZ within a 16 GB for the RAM, running under Kernel Ubuntu Linux
3.5.0-17-generic operating system. All algorithms were compiled by GCC
compiler of Qt Creator 2.6.0 (based on Qt 5.0.0 “64Bit”).
Algorithms characteristics
Experimental Setup (1)
Background
Published Papers

Experimental Setup(2)
Background
Published Papers

1 2
3
4
Background
Published Papers

Background
Published Papers

Performance Analysis
0
100
200
300
400
500
600
700
800
N=4 N=8 N=14 N=19 N=25 N=38 N=4 N=8 N=14 N=19 N=25 N=38 N=4 N=8 N=14 N=19 N=25 N=38
Number of Nodes
Satisfied
Connection
Requests
RO_fixed GA_fixed TS_fixed RO_rand GA_rand TS_rand
B
ons
MaxIterati
N
mult
N sd
sd 

 ;
1000
)
(
;
1
30

wav
N
90

wav
N
270

wav
N
Background
Published Papers
4. Experiment Results (Part I)

B
ons
MaxIterati
N
mult
N sd
sd 

 ;
1000
)
(
;
1
Time Analysis
0
10000
20000
30000
40000
50000
60000
70000
80000
N=4 N=8 N=14 N=19 N=25 N=38 N=4 N=8 N=14 N=19 N=25 N=38 N=4 N=8 N=14 N=19 N=25 N=38
Number of Nodes
Time(sec.)
RO_fixed GA_fixed TS_fixed RO_Rand GA_Rand TS_Rand
30

wav
N 90

wav
N
270

wav
N
Background
Published Papers

B
ons
MaxIterati
N
mult
N sd
sd 

 ;
20
)
(
;
5
Performance Analysis
0
10
20
30
40
50
60
70
80
N=4 N=8 N=14 N=19 N=38 N=4 N=8 N=14 N=19 N=38 N=4 N=8 N=14 N=19 N=38
Number of Nodes
Satisfied
Connection
Requests
RO_fixed GA_fixed TS_fixed RO_rand GA_rand TS_rand
30

wav
N
90

wav
N
270

wav
N
Background
Published Papers

B
ons
MaxIterati
N
mult
N sd
sd 

 ;
20
)
(
;
5
Time Analysis
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
N=4 N=8 N=14 N=19 N=38 N=4 N=8 N=14 N=19 N=38 N=4 N=8 N=14 N=19 N=38
Number of Nodes
Time(sec.)
RO_fixed GA_fixed TS_fixed RO_fixed GA_fixed TS_fixed
30

wav
N 90

wav
N
270

wav
N
Background
Published Papers

•The performance using ROA, GA or TSA to solve the Max-RWA increases rapidly as
the number of wavelength increases.
•The performance of TSA is the best when the total number of all connection-requests
desired to satisfy is equal to 5, 20 or 100. Thus, in terms of accepted connection-
requests TSA achieves up to a 23% improvement over GA and a 10% improvement
over ROA.
•ROA have also shown a good performance which is better than GA and close to TSA
when the number of connection requests is equal to 5 or 20. However, the gap is
widening in favor of TSA when is equal to 100. Indeed, in terms of accepted
connection-requests ROA achieves up to a 14% improvement over GA.
Background
Published Papers

• When we increase the number exponentially till it is equal to 1000, the surprise is only
GA which will perform well with a better results than ROA or TSA. This can be
explained by the crossover (or recombination) operator used by GA which maintains
diversity in the solution space. In fact, in terms of accepted connection-requests we
found that GA achieves up to a 6% improvement over TSA and a 7% improvement
over ROA.
Algorithms characteristics
Background
Published Papers

•The fixed-generated topology networks (case 2) consume more time to be solved with
whatever ROA, GA and TSA than the time taken in the randomly-generated topology
networks (case 1). This is due to the number of edges. In fact, in case 2 the number of
edges is relatively larger than those in case 1 especially when the number of nodes is
between 14 and 38. Edge Analysis
0
10
20
30
40
50
60
70
N=4 N=8 N=14 N=19 N=25 N=38
Nodes
Number
of
Edges
Random_Topology Fixed_Topology
Background
Published Papers

• The time spent by ROA, GA or TSA to solve the Max-RWA increases rapidly
depending on the number of nodes as well as that of the wavelengths.
•In general and comparing to TSA, we found that GA and more especially ROA
perform very efficiently in terms of speed with an edge for ROA compared to GA. In
fact, the time spent by GA, on average, is 5 times higher than ROA.
•The time spent by TSA, on average, is 5 times higher than GA and 29 times higher
than ROA.
•The time spent by TSA is significantly higher, but since we are dealing with the static
case, computations are performed offline, so the runtimes of TSA are reasonable given
this constraint.
Background
Published Papers

4. Experiments (Part II)
Background
Published Papers

iterations
ons
MaxIterati
N
mult
N
C
Case sd
sd 5000
;
20
)
(
;
5
: 


Fouad KHARROUBI May 2014 40
4. Experiment Results (Part II)
Background
Published Papers

iterations
n
MaxIteatio
N
mult
N
C
Case sd
sd 15000
;
20
)
(
;
5
: 


Background
Published Papers

Background
Published Papers

Algorithms Characteristics
Background
Published Papers

Background
Published Papers
4. Performance Comparison

Background
Published Papers

• We successfully covered 100% of all the generated paths.
• We find that, in terms of accepted connection-requests, on average, TSA achieves up
to a 11% improvement over GA, a 5% improvement over ROA and only a 1%
improvement over EP.
• Time spent by TSA, on average, is 10 times higher than EP, 2 times higher than ROA
and 3 times higher than GA.
Background
Published Papers

Published Papers
Fouad Kharroubi, Jing He, and Lin Chen, (2014) "Performance Analysis of GA, ROA, and TSA for
Solving the Max-RWA Problem in Optical Networks," in Optical Fiber Communication Conference,
OSA Technical Digest (online) (Optical Society of America, 2014), paper W2A.48. (EI) (Accepted and
already published Online via OSA).
Fouad Kharroubi, Jing He, Tang Jin, Chen Ming, Chen Lin (2013) “Evaluation performance of genetic
algorithm and tabu search algorithm for solving the Max-RWA problem in all-optical networks”. Journal
of Combinatorial Optimization (JOCO). Springer New York. DOI 10.1007/s10878-013-9676-y. (SCI,
Level 3, IF: 0.59), (Accepted and already published Online via Springer).
Fouad Kharroubi, Lin Chen, and Jianjun. Yu (2012), “Approaches and controllers to solving the contention
problem for packet switching networks: A survey,” in Internet of Things, ser. Communications in
Computer and Information Science (CCIS). 2012, vol. 312, pp. 172–182. Springer Berlin Heidelberg.
(EI), (Accepted and already published Online via Springer).
Fouad KHARROUBI May 2014 49
Background
Published Papers

• In this research work, we have implemented and compared four metaheuristics to
deal with the Max-RWA problem in all optical networks.
•The objective was to maximize the number established connections depending on
the set of wavelength at stake. (static-case).
•The RWA problem was mathematically formulated and solved approximately by
three efficient random search algorithms namely; ROA, GA, TSA and EP.
•The routing subproblem was insured exactly by the backtracking algorithm while
the wavelength assignment subproblem was solved randomly.
•A relevant comparison, including the performance and the time involved, was made
between the three algorithms, making a total of 1560 experiments in different
circumstances and variations.
Background
Published Papers

•One of the most significant results to be noted is that TSA performed very well
and showed very good results in comparison to EP, GA and ROA. However, its
drawback is in the fact that it is the slowest one in terms of the consumed running
time to solve Max-RWA problem
•For future work, we would like to implement more metaheurisics but this time
on networks with full, sparse and limited-range wavelength conversion capability.
A comparison between the backtracking method and the k-shortest path method
would also need to be explored further. We will also consider Max-RWA problem
for the multicast advance reservation case. The mathematical formulation and
heuristics can be adapted for this case.
Background
Published Papers

Any Question?
Thank You 
Background
Contributions
Published Papers

fouad_kharroubi_PhD_Defense_05_06_2014.ppt

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