Abstract
In this paper, we present a novel approach that addresses the problem of large-scale network topology design and routing. There are research works that used exact methodologies based on Integer Linear Programming (ILP) models to develop potential solutions for this problem. However, this problem is computationally NP-hard, thus solving it is hugely demanding on computational power for large-scale networks, and in many cases, it is not even possible to generate a solution with a reasonable optimality gap. This paper presents a hybrid algorithm based on the Genetic Algorithm with efficiently designed genetic operators. This algorithm aims to design the topology of large-scale networks and generate a routing configuration for a set of predefined traffic demands on the networks while keeping the total cost of design and routing at a minimum. The results have been compared to an exact ILP model, a relaxed ILP model, and a customized GA as benchmarks for validation purposes. These comparisons showed that the proposed algorithm significantly outperforms the ILP solutions in all of the large-scale network configurations that were used as case studies.
Highlights
Optical transport networks have become widespread, and they have a pervasive role in most typical interactions of modern society
Given a fixed set of nodes, the objective of the fixed charge plus routing (FCR) problem is to minimize the total cost of the network topology design, demand routing, and working capacity placement
RESEARCH GAPS AND CONTRIBUTIONS We have identified several shortcomings that commonly arise in the existing literature and developed an Improved Genetic Algorithm (IGA) in a manner that seeks to overcome the following common weaknesses: 1) Weakness: Generating a high-merit initial population is time-consuming, due to the stochastic nature of the process and subsequent repair actions that are often required to transform infeasible solutions into feasible solutions
Summary
Optical transport networks have become widespread, and they have a pervasive role in most typical interactions of modern society. Given a fixed set of nodes, the objective of the FCR problem is to minimize the total cost of the network topology design, demand routing, and working capacity placement.
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