1. Professor Chen Lin and Assistant Prof Jing He
School of Information Science and Engineering
Hunan University, Changsha , China
PhD Defense
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

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

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

4. Background (2) WDM Introduction Thesis Contributions Published Papers
Conclusion and Future Work
Background (2)
WDM
Fouad KHARROUBI June

5. The Wavelength Clash Constraint The Wavelength Continuity Constraint
Introduction
Thesis Contributions
Published Papers
Conclusion and Future Work
Background(3)
WDM
New Technology
The Wavelength Clash Constraint
RWA
New Problem
The Wavelength Continuity Constraint
Fouad KHARROUBI June

6. Wavelength Assignment
Background
Thesis Contributions
Published Papers
Conclusion and Future Work
Background (3)
RWA Subproblems
Wavelength Assignment
Routing
K-shortest Path
Backtracking
Random methods
Hybrid methods
shortest Path
Exact methods
Fouad KHARROUBI June

7. Wavelength Assignment
Background
Thesis Contributions
Published Papers
Conclusion and Future Work
Background (3)
RWA Subproblems
Wavelength Assignment
Routing
K-shortest Path
Backtracking
Random methods
Hybrid methods
shortest Path
Exact methods
Fouad KHARROUBI June

8. Background
Thesis Contributions
Published Papers
Conclusion and Future Work
Thesis Contributions
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:
A new mathematical formulation for the Max-RWA problem in WDM optical networks is proposed and its constraints are analyzed
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;
To the best of our knowledge, a novel efficient Backtracking algorithm is proposed and investigated in WDM optical network for the first time.
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.
Fouad KHARROUBI June

9. 1. Mathematical Formulation
Background
Thesis Contributions (1)
Published Papers
Conclusion and Future Work
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.
Fouad KHARROUBI June

10. 1. Mathematical Formulation
Background
Thesis Contributions (1)
Published Papers
Conclusion and Future Work
1. Mathematical Formulation
. The Max-RWA problem can be mathematically formulated as follows:
Fouad KHARROUBI June

11. 1. Mathematical Formulation
Background
Thesis Contributions (1)
Published Papers
Conclusion and Future Work
1. Mathematical Formulation
Fouad KHARROUBI June

12. 1. Mathematical Formulation
Background
Thesis Contributions (1)
Published Papers
Conclusion and Future Work
1. Mathematical Formulation
Fouad KHARROUBI June

13. 2. Random Search Algorithms
Background
Thesis Contributions (2)
Published Papers
Conclusion and Future Work
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.
Fouad KHARROUBI June

14. 2. Random Search Algorithms
Background
Thesis Contributions (2)
Published Papers
Conclusion and Future Work
2. Random Search Algorithms
Part II: Evolutionary algorithms
Evolutionary programming
Evolution Strategy
Genetic algorithms
Genetic programming
Learning classifier system
Part III: Inspired algorithms
Stochastic Diffusion Search
Generalized external optimization
Harmony search
Ant colony optimization
Differential evolution
Particle swarm optimization
Invasive weed optimization algorithm
Gaussian adaptation
Part I: Local search algorithms
Random Optimization
Simple Descent
Deepest Descent
Multi-start Descent
Variable Neighborhood Search (VNS)
Tabu Search (TS)
Part II: Stochastic search algorithms
Simulated annealing
Threshold Accept
Fouad KHARROUBI June

15. 2. Random Search Algorithms
Background
Thesis Contributions (2)
Published Papers
Conclusion and Future Work
2. Random Search Algorithms
Part II: Evolutionary algorithms
Evolutionary programming
Evolution Strategy
Genetic algorithm
Genetic programming
Learning classifier system
Part III: Inspired algorithms
Stochastic Diffusion Search
Generalized external optimization
Harmony search
Ant colony optimization
Differential evolution
Particle swarm optimization
Invasive weed optimization algorithm
Gaussian adaptation
Part I: Local search algorithms
Random Optimization
Simple Descent
Deepest Descent
Multi-start Descent
Variable Neighborhood Search (VNS)
Tabu Search (TS)
Part II: Stochastic search algorithms
Simulated annealing
Threshold Accept
Fouad KHARROUBI June

16. 2. Random Search Algorithms
Background
Thesis Contributions (2)
Published Papers
Conclusion and Future Work
2. Random Search Algorithms
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
Fouad KHARROUBI June

17. 2. Random Search Algorithms
Background
Thesis Contributions (2)
Published Papers
Conclusion and Future Work
2. Random Search Algorithms
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 (1): Initialization
Step (2): Crossover
Step (3): Mutation
Step (4): Update of the found solution
Step (5): Copy back of the last found solution
Fouad KHARROUBI June

18. 2. Random Search Algorithms
Background
Thesis Contributions (2)
Published Papers
Conclusion and Future Work
2. Random Search Algorithms
Our Proposed Genetic Algorithm (Crossover Operator)
Fouad KHARROUBI June

19. 2. Random Search Algorithms
Background
Thesis Contributions (2)
Published Papers
Conclusion and Future Work
2. Random Search Algorithms
Our Proposed Genetic Algorithm (Mutation Operator)
Fouad KHARROUBI June

20. 2. Random Search Algorithms
Background
Thesis Contributions (2)
Published Papers
Conclusion and Future Work
2. Random Search Algorithms
Our Proposed Tabu Search Algorithm
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 (1): Initialization
Step (2): Primary Solutions
Step (3): Wavelength Assignment
Step (4): The main loop
Step (5): Choose the best solution non-tabu list
Step (6): Update of the found solution
Step (7): Copy back of the last found solution
Fouad KHARROUBI June

21. 2. Random Search Algorithms Our Proposed Evolutionary Programming
Background
Thesis Contributions (2)
Published Papers
Conclusion and Future Work
2. Random Search Algorithms
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 (1): Initialization
Step (2): Mutation
Step (3): Update of the found solution (Evaluation)
Step (4): Copy back of the last found solution (Replacement)
Fouad KHARROUBI June

22. 3. New Backtracking Algorithm
Background
Thesis Contributions (3)
Published Papers
Conclusion and Future Work
3. New Backtracking Algorithm
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.
Fouad KHARROUBI June

23. 3. New Backtracking Algorithm
Background
Thesis Contributions (3)
Published Papers
Conclusion and Future Work
3. New Backtracking Algorithm
Fouad KHARROUBI June

24. 3. New Backtracking Algorithm
Background
Thesis Contributions (3)
Published Papers
Conclusion and Future Work
3. New Backtracking Algorithm
Step (1): The Stopping Condition
Step (2): We keep the new lightpath into the list of all lightpaths
Step (3): Backtrack
Fouad KHARROUBI June

25. 4. Experiments (Part I) Problem Our Aim Proposed Solution
Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Experiments (Part I)
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.
Fouad KHARROUBI June

26. Algorithms characteristics
Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Experiments (Part I)
Experimental Setup (1)
The hardware used for our experiments is an Intel(R) Core(TM) i CPU 3.20 GHZ within a 16 GB for the RAM, running under Kernel Ubuntu Linux generic operating system. All algorithms were compiled by GCC compiler of Qt Creator (based on Qt “64Bit”).
Algorithms characteristics
Fouad KHARROUBI June

27. 4. Experiments (Part I) Experimental Setup(2) Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Experiments (Part I)
Experimental Setup(2)
Fouad KHARROUBI June

28. 4. Experiments (Part I) Background Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Experiments (Part I) 1 2 3 4
Fouad KHARROUBI June

29. 4. Experiments (Part I) Experimental Setup(3) Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Experiments (Part I)
Experimental Setup(3)
Fouad KHARROUBI June

30. 4. Experiments (Part I) Experimental Setup(4) Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Experiments (Part I)
Experimental Setup(4)
Fouad KHARROUBI June

31. 4. Experiment Results (Part I)
Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Experiment Results (Part I)
Fouad KHARROUBI June

32. 4. Experiment Results (Part I)
Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Experiment Results (Part I)
Fouad KHARROUBI June

33. 4. Experiment Results (Part I)
Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Experiment Results (Part I)
Fouad KHARROUBI June

34. 4. Experiment Results (Part I)
Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Experiment Results (Part I)
Fouad KHARROUBI June

35. 4. Experiment Results (Part I)
Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Experiment Results (Part I)
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.
Fouad KHARROUBI June

36. 4. Experiment Results (Part I) Algorithms characteristics
Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Experiment Results (Part I)
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
Fouad KHARROUBI June

37. 4. Experiment Results (Part I)
Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Experiment Results (Part I)
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.
Fouad KHARROUBI June

38. 4. Experiment Results (Part I)
Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Experiment Results (Part I)
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.
Fouad KHARROUBI June

39. 4. Experiments (Part II) Background Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Experiments (Part II)
Fouad KHARROUBI June

40. 4. Experiment Results (Part II)
Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Experiment Results (Part II)
Fouad KHARROUBI May

41. 4. Experiment Results (Part II)
Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Experiment Results (Part II)
Fouad KHARROUBI June

42. 4. Experiment Results (Part II)
Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Experiment Results (Part II)
Fouad KHARROUBI June

43. 4. Experiment Results (Part II) Algorithms Characteristics
Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Experiment Results (Part II)
Algorithms Characteristics
Fouad KHARROUBI June

44. 4. Performance Comparison
Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Performance Comparison
Fouad KHARROUBI June

45. 4. Performance Comparison
Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Performance Comparison
Fouad KHARROUBI June

46. 4. Performance Comparison
Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Performance Comparison
Fouad KHARROUBI June

47. 4. Performance Comparison
Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Performance Comparison
Fouad KHARROUBI June

48. 4. Performance Comparison
Background
Thesis Contributions (4)
Published Papers
Conclusion and Future Work
4. Performance Comparison
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.
Fouad KHARROUBI June

49. Background
Thesis Contributions
Published Papers
Conclusion and Future Work
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 /s 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

50. Conclusion and Future Work
Background
Thesis Contributions
Published Papers
Conclusion and Future Work
Conclusion and Future Work
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.
Fouad KHARROUBI June

51. Conclusion and Future Work
Background
Thesis Contributions
Published Papers
Conclusion and Future Work
Conclusion and Future Work
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.
Fouad KHARROUBI June

52. Any Question? Thank You 
Background
Contributions
Published Papers
Conclusion and Future Work
Any Question?
Thank You 
Fouad KHARROUBI June