## Abstract

The physical resource assignment problem in dynamic optical networks, often referred to as the routing and wavelength assignment problem, is very important for the development of optical transport networks. Research has been done to optimize this operation so that the overall connection blocking can be minimized. Traffic grooming adds another dimension to this problem by introducing opportunities for multiplexing low-bit-rate traffic into a high-bit-rate stream. The ant colony optimization (ACO) algorithm is a metaheuristic method that is inspired by the foraging behavior of ants and has been widely implemented in solving discrete optimization problems. This paper proposes an ACO to solve the grooming, routing, and wavelength assignment problem. Unlike previous work, our work includes considerations of mixed line rate, physical impairments, and traffic grooming functionality. Comprehensive simulation tests show how variations on the ACO algorithms’ implementation affect performance. A comparison is made between this distributed algorithm and a centralized algorithm that we propose, a grooming adaptive shortest path algorithm (GASP). Although GASP shows better efficiency in terms of blocking probability, ACO shows great robustness and adaptivity to varying network and traffic conditions.

© 2014 Optical Society of America

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