Design Analysis A connection is recognized in the optical layer regulates to carry the information among the clients in the network. The optical connection is preserved between a source node, s and destination node, d is known as an light path. The problem in finding a route for a light path with wavelength assigned to the light path is submitted as the routing and wavelength assignment problem (RWA).
The problem of RWA is divided into two parts : Routing and Wavelength Assignment. In the traffic model, the RWA problem is considered as either Static Light path Establishment (SLE) [14] where the idea is to minimize the number of wavelengths wanted to accommodate the given connection set or Dynamic Light path Establishment (DLE) idea is to decrease …show more content…
The mutation operator launches new genetic objects by arbitrarily selecting and changing simple genes. As the result of crossover and mutation a better population is generated and the process is repetitive till a fine solution is found or with predefined number of iteration. The key advantage of the GA approach does not rely upon precise knowledge of the problem definition. The success of the algorithm is accredited to various factors: powerful, global parallel search capability, computation simplicity, and robustness, ability to combine with other heuristic procedures and independence from solution traits such as linear or non-linear constraints either in discrete or continuous search …show more content…
The main idea is to simulate evolution. The vectors represent genotypes and the aim is to find an good individual as possible. The node coloring problem can be solved with GA [16]. Here the vectors define the order in which the nodes are colored. Basically find the best ordering to color the nodes with the greedy algorithm. The option of crossover operation for permutations is not straightforward and numerous diverse schemes is proposed.
At each generation it maintains a population of individuals where each individual is a coded from of a possible solution of the problem at hand and is called a chromosome. Each chromosome is evaluated by a function known as the fitness function which is usually called the cost function or the objective function of the corresponding optimization problem. Next new population is generated from the present one through selection crossover, repair and mutation operations. Purpose of selection mechanism is to select more fit individuals for crossover and mutation. A crossover causes the exchange of genetic materials between parents to form offspring, Where as mutation incorporates new genetic materials in the
The problem of RWA is divided into two parts : Routing and Wavelength Assignment. In the traffic model, the RWA problem is considered as either Static Light path Establishment (SLE) [14] where the idea is to minimize the number of wavelengths wanted to accommodate the given connection set or Dynamic Light path Establishment (DLE) idea is to decrease …show more content…
The mutation operator launches new genetic objects by arbitrarily selecting and changing simple genes. As the result of crossover and mutation a better population is generated and the process is repetitive till a fine solution is found or with predefined number of iteration. The key advantage of the GA approach does not rely upon precise knowledge of the problem definition. The success of the algorithm is accredited to various factors: powerful, global parallel search capability, computation simplicity, and robustness, ability to combine with other heuristic procedures and independence from solution traits such as linear or non-linear constraints either in discrete or continuous search …show more content…
The main idea is to simulate evolution. The vectors represent genotypes and the aim is to find an good individual as possible. The node coloring problem can be solved with GA [16]. Here the vectors define the order in which the nodes are colored. Basically find the best ordering to color the nodes with the greedy algorithm. The option of crossover operation for permutations is not straightforward and numerous diverse schemes is proposed.
At each generation it maintains a population of individuals where each individual is a coded from of a possible solution of the problem at hand and is called a chromosome. Each chromosome is evaluated by a function known as the fitness function which is usually called the cost function or the objective function of the corresponding optimization problem. Next new population is generated from the present one through selection crossover, repair and mutation operations. Purpose of selection mechanism is to select more fit individuals for crossover and mutation. A crossover causes the exchange of genetic materials between parents to form offspring, Where as mutation incorporates new genetic materials in the