Wavelength converter placement in least-load-routing-based optical networks using genetic algorithms

Abstract

In this paper we study the routing and wavelength converter placement problems in optical networks with sparse wavelength conversion. We propose a new dynamic routing algorithm with two new path cost functions based on the concept of the Least-Load Routing (LLR) with sparse converter placement. Moreover, we discuss the application of Genetic Algorithms (GA) to determine the optimal location of wavelength converters so that the call blocking probability is minimized. Simulation results show that the proposed dynamic routing algorithms performs significantly better than Shortest-Path (SP) routing and Fixed-Alternative-Routing (FAR), in terms of the call blocking probability. The GA model is able to obtain a nearly optimal solution of the wavelength converter placement problem within a reasonable time and its performance is better than that of two other popular heuristic placement algorithms. abstract

Routing and wavelength converter allocation in WDM networks: a multi-objective evolutionary optimization approach

“…In the post-simulated approach, the blocking probability of each possible solution is obtained by simulation; therefore, there are as many simulations as solutions to be evaluated [21,40,50,64]. On the other hand, in the pre-simulated approach, a statistic of utilization of converters is calculated at the beginning through simulations on a full-wavelength conversion network.…”

Multiobjective Optimization in Optical Networks

“…Recently, some works have proposed optical WCA (OWCA) approaches that adapt to any RWA algorithm (Hei et al, 2004;Roy and Naskar, 2008;Xiao and Leung, 1999). They use traffic direct or indirect simulations as a fundamental tool.…”

“…They use traffic direct or indirect simulations as a fundamental tool. On one hand, a direct-simulation approach evaluates each solution, applying an RWA algorithm under a dynamic traffic pattern (Hei et al, 2004). On the other hand, an indirect-simulation approach solves problems in two stages: simulation stage and optimization stage (Roy and Naskar, 2008;Xiao and Leung, 1999).…”

“…However, a direct simulation gives full information about the performance of the proposed solution (Hei et al, 2004). Other works reported heuristic approaches, which allocate a great number of wavelength converters at random (Lee et al, 1993;Subramaniam et al, 1996).…”

Computational intelligence in photonics technology and optical networks: A survey and future perspectives

“…In this direction, Hei et al studied in [52] a specific combined problem of dynamic routing together with sparse wavelength converter placement. For a given dynamic routing technique base on least load routing (LLR) a genetic algorithm was employed to determine the optimal placement in order to minimize call-blocking probability.…”