A Comparative Evaluation of Nature Inspired Algorithms for Telecommunication Network Design

• Published in: Applied sciences Vol. 10; no. 19; p. 6840
• Main Authors: Kozdrowski, Stanisław, Żotkiewicz, Mateusz, Wnuk, Kacper, Sikorski, Arkadiusz, Sujecki, Sławomir
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2020
• Subjects: Algorithms; Authorship; bees algorithm; Capital costs; Comparative analysis; Data transmission; Design and construction; evolutionary algorithm; harmony search algorithm; Heuristic; Influence; Integer programming; Linear programming; metaheuristics; Nature; Network management systems; Optimization; Random variables; Spectrum allocation; swarm intelligence; swarm-based optimization; Technology application; Telecommunication systems; Transponders; Wave division multiplexing; Poland
• DOI: 10.3390/app10196840
• ISSN: 2076-3417

The subject of the study was an application of nature-inspired metaheuristic algorithms to node configuration optimization in optical networks. The main objective of the optimization was to minimize capital expenditure, which includes the costs of optical node resources, such as transponders and amplifiers used in a new generation of optical networks. For this purpose a model that takes into account the physical phenomena in the optical network is proposed. Selected nature-inspired metaheuristic algorithms were implemented and compared with a reference, deterministic algorithm, based on linear integer programming. For the cases studied the obtained results show that there is a large advantage in using metaheuristic algorithms. In particular, the evolutionary algorithm, the bees algorithm and the harmony search algorithm showed superior performance for the considered data-sets corresponding to large optical networks; the integer programming-based algorithm failed to find an acceptable sub-optimal solution within the assumed maximum computational time. All optimization methods were compared for selected instances of realistic teletransmission networks of different dimensions subject to traffic demand sets extracted from real traffic data.