An evolutionary approach with surrogate models and network science concepts to design optical networks. (August 2015)

• Record Type: Journal Article
• Authors: de Araújo, Danilo R.B.; Bastos-Filho, Carmelo J.A.; Martins-Filho, Joaquim F.
• Is Part Of: Engineering applications of artificial intelligence. Volume 43(2015:Jul.)
• Page Start: 67
• Page End: 80
• Publication Date: 2015-08
• Subjects: Multiobjective evolutionary algorithms -- Artificial neural networks -- Optical networks -- Physical topology design -- Network science -- Surrogate models; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2015.04.004
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Physical topology design of optical networks is frequently accomplished by using evolutionary approaches. However, fitness evaluation for this type of problems is time consuming and the overall optimization process presents a huge execution time. In this paper we propose a new method that uses a multi-objective evolutionary approach to handle the design of all-optical networks. We focused on the simultaneous optimization of the network topology and the device specifications in order to minimize both the capital expenditure of the network and the network performance. Our method uses surrogate models to accelerate the fitness evaluation and a novel network generative model based on preferential attachment to generate the seeds for the evolutionary process. Our approach can provide high quality solutions with a very small execution time when compared to the previous approaches. In order to assess our proposal we performed a set of simulations aiming to analyze the convergence ability and the diversity of the generated solutions for scenarios considering uniform and non-uniform traffic matrices. From our results, we obtained an evolutionary approach that presents better solutions than previous proposals for all analyzed scenarios. Our proposal presents an execution time that is up to 84% and 88% lower than the execution time needed by the previous approaches for uniform and non-uniform traffic, respectively.