A Novel Genetic Algorithm-Based Methodology for Large-Scale Fixed Charge Plus Routing Network Design Problem With Efficient Operators
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...
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doaj-39f8c3dfa3ce425780929786d608fa7b2021-08-23T23:00:38ZengIEEEIEEE Access2169-35362021-01-01911483611485310.1109/ACCESS.2021.31047949515310A Novel Genetic Algorithm-Based Methodology for Large-Scale Fixed Charge Plus Routing Network Design Problem With Efficient OperatorsSamira Doostie0https://orcid.org/0000-0002-6723-6304Tetsuhei Nakashima-Paniagua1https://orcid.org/0000-0002-0091-2697John Doucette2https://orcid.org/0000-0003-0072-4546Department of Mechanical Engineering, Donadeo Innovation Centre for Engineering, University of Alberta, Edmonton, AB, CanadaDepartment of Mechanical Engineering, Donadeo Innovation Centre for Engineering, University of Alberta, Edmonton, AB, CanadaDepartment of Mechanical Engineering, Donadeo Innovation Centre for Engineering, University of Alberta, Edmonton, AB, CanadaIn 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.https://ieeexplore.ieee.org/document/9515310/Genetic algorithmlarge-scale networkoptimal topologyrouting |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Samira Doostie Tetsuhei Nakashima-Paniagua John Doucette |
spellingShingle |
Samira Doostie Tetsuhei Nakashima-Paniagua John Doucette A Novel Genetic Algorithm-Based Methodology for Large-Scale Fixed Charge Plus Routing Network Design Problem With Efficient Operators IEEE Access Genetic algorithm large-scale network optimal topology routing |
author_facet |
Samira Doostie Tetsuhei Nakashima-Paniagua John Doucette |
author_sort |
Samira Doostie |
title |
A Novel Genetic Algorithm-Based Methodology for Large-Scale Fixed Charge Plus Routing Network Design Problem With Efficient Operators |
title_short |
A Novel Genetic Algorithm-Based Methodology for Large-Scale Fixed Charge Plus Routing Network Design Problem With Efficient Operators |
title_full |
A Novel Genetic Algorithm-Based Methodology for Large-Scale Fixed Charge Plus Routing Network Design Problem With Efficient Operators |
title_fullStr |
A Novel Genetic Algorithm-Based Methodology for Large-Scale Fixed Charge Plus Routing Network Design Problem With Efficient Operators |
title_full_unstemmed |
A Novel Genetic Algorithm-Based Methodology for Large-Scale Fixed Charge Plus Routing Network Design Problem With Efficient Operators |
title_sort |
novel genetic algorithm-based methodology for large-scale fixed charge plus routing network design problem with efficient operators |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
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. |
topic |
Genetic algorithm large-scale network optimal topology routing |
url |
https://ieeexplore.ieee.org/document/9515310/ |
work_keys_str_mv |
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