Reader’s Guide

The remainder of this dissertation is organized as follows. In Chapter 2, the characteristics of optical networks, multiplexing transmission systems, optical equipment, evolution of optical networks, and multicast will be introduced. The introduction not only gives an overview of what is the optical network but also helps us to understand that the studied problems in this dissertation are reasonable. The preliminaries of routing and wavelength assignment problem (RWAP) are given in Chapter 3. According to the number of destinations required to be routed, the RWAP is divided into two types, unicast RWAP (URWAP) and multicast RWAP (MRWAP). Previous research on the URWAP and MRWAP will be surveyed. In the end of this chapter, a model of the RWAP will be proposed such that the complexity and relationship among previous studies can be compared. The problems

discussed in this dissertation can be represented by this model.

In Chapter 4, the MRWAP-DC will be formulated to define as a general problem.

Nevertheless, due to this generalization, the MRWAP-DC is hard to solve in an affordable execution time. The variants, including the MRWAP-DC-WWC, URWAP-DC-SR, MRP-DC-WWC-SR, and MRWAP-DC-MRP-DC-WWC-SR, are special cases of the MRWAP-DC by setting the request set to only contain single request, setting the multicast request to be a unicast request, and setting the network without wavelength conversion. These variants will be explored by different methods introduced in following chapters.

In Chapter 5, an ILP (Integer Linear Programming) formulation will be proposed to solve the MRWAP-DC which is more difficult than the RWAP-DC due to the multicasting feature.

The tool CPLEX will be used to implement the ILP formulation. The simulation results obtained by the ILP method will be viewed as a baseline for the comparison with meta-heuristics. Although the ILP model can be deployed to find optimal solutions, the execution time is not affordable for large-scale networks. Moreover, the MRWAP-DC exhibits much more complicated structures; it is unlikely to follow the ILP approach to produce optimal solutions in an acceptable time.

Chapter 6 will address a design of ant colony optimization (ACO), which is a meta-heuristic developed in early 1990s. The ACO uses natural metaphor inspired by the behavior of ant colonies to solve complex combinatorial optimization problems for finding near-optimal solutions. It has demonstrated significant strengths in many application areas, such as the traveling salesman problem, graph coloring problem, quadratic assignment problem, generalized minimum spanning tree problem, scheduling problems, and minimum weight vertex cover problem. An ACO algorithm will be designed for the URWAP-DC-SR and comparisons between ACO and ILP will be made. Therefore, our study will not only extend the application areas of the ACO approach but also suggest a new viable method for coping

with the complex optimization problems arising from the WDM domain

In Chapter 7, a genetic algorithm (GA) will be introduced for the MRP-DC-WWC-SR.

The set of possible solutions of the problem is the search space in GA. A solution in the search space is called an individual whose genotype is composed of a set of chromosomes represented by sequences of 0’s and 1’s. These chromosomes of individuals could dominate phenotypes of individuals. Each individual is associated with an objective function value called fitness. A good individual is the one that has a high or low fitness value depending upon the problem’s goal as maximization or minimization. The strength of a chromosome in the individual is represented by its fitness value and the chromosomes of the individuals are to be carried to the next generation. A set of individuals with associated fitness values is called the population. This population at a given stage of GA is called a generation. The best individual was found in each generation at which the individual with that best fitness value was discovered. The general GA proceeds to include five basic operations, individual coding, selection/reproduction, crossover, mutation, and replacement. A GA algorithm will be developed to solve the MRP-DC-WWC-SR. We will compare its performance with the ILP model.

For routing a set of requests in a large-scale network, where the network provides more wavelengths, more requests are issued and the requests have enormous destinations, the ILP, ACO and GA are all time–demanding in solving the MRWAP-DC or the UEWAP-DC. In Chapter 8, two efficient heuristics, Near-k-Shortest-Path-based Heuristic (NKSPH) and Iterative Solution Model (ISM), will be proposed to find feasible approximate solutions.

Based on the k-shortest light-paths between the source and each destination, NKSPH can find near optimal solutions and reduce the failure opportunity by using the adjustment in the value of k according to the execution time, where increasing the value of k will enlarge the searching space but provide a better opportunity of reaching the optimal solution. Conclusion

and future work will be given in Chapter 9 to make a summary and review about these proposed methods. In this chapter, we will outline several works worthy to research further including extending the proposed methods to solve the other variants, relaxing these integral variables in the ILP formulation to be real variables to become a relaxed-ILP formulation, and introducing simulated annealing (SA) algorithm.