Routing and spectrum allocation in elastic optical networks using bee colony optimization

• Published in: Photonic network communications Vol. 34; no. 3; pp. 356 - 374
• Main Author: Marković, Goran Z.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-12-2017; Springer Nature B.V
• Subjects: Algorithms; Characterization and Evaluation of Materials; Computer Communication Networks; Computer Science; Computer simulation; Data encryption; Electrical Engineering; Evolutionary algorithms; Heuristic methods; Optical communication; Optimization; Original Paper; Spectrum allocation; Bee colony optimization (BCO); Flexible lightpaths; Elastic optical networks (EON); Frequency slot (FS); Routing and spectrum allocation (RSA)
• ISSN: 1387-974X; 1572-8188
• DOI: 10.1007/s11107-017-0706-z

Elastic optical network (EON) technology is considered as a very promising candidate for future high-speed networks due to its intrinsic flexibility and high efficiency in allocating the optical spectrum resources. The key issue that has to be addressed in EON is the routing and spectrum allocation (RSA) problem. RSA is NP-hard problem that has to be solved in an efficient manner. It is a highly challenging task particularly in the case of large problem instances. In this paper, we applied the bee colony optimization (BCO) metaheuristic approach to solve the RSA problem in EON with static traffic demands. The objective of the proposed BCO–RSA algorithm is to minimize both the network spectrum utilization and the average path length criterions. The results of numerous experimental studies show that our BCO–RSA algorithm performs superior compared to some benchmark greedy heuristics as well as to differential evolution (DE) metaheuristic algorithm recently proposed in the literature. The algorithm is evaluated in different realistic size optical networks, such as the NSFnet, two European optical networks (EON-19 and EON-28) and the USA network topology. Simulation results demonstrate that considerable spectrum savings could be achieved with our BCO–RSA algorithm compared to other considered approaches. In addition, we analyzed the efficiency of the BCO–RSA algorithm and compare it with the competitive DE approach according to the required CPU time and the convergence speed.