Performance analysis of genetic algorithm (GA)-based multi-constrained path routing algorithm

To support networked multimedia applications, it is important for the network to provide guaranteed quality-of-service (QoS). One way to provide such services is for the network to perform QoS routing, where the path taken must fulfill a given set of constraints. Multi-constrained path (MCP) problem...

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Main Author: Yussof S.
Other Authors: 16023225600
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Published: 2023
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Institution: Universiti Tenaga Nasional
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spelling my.uniten.dspace-304062023-12-29T15:47:28Z Performance analysis of genetic algorithm (GA)-based multi-constrained path routing algorithm Yussof S. 16023225600 Genetic algorithm Multi-constrained path problem Quality-of-service (qos) Routing To support networked multimedia applications, it is important for the network to provide guaranteed quality-of-service (QoS). One way to provide such services is for the network to perform QoS routing, where the path taken must fulfill a given set of constraints. Multi-constrained path (MCP) problem refers to the problem of finding a path through a network subject to multiple additive constraints. It has been proven that this problem is Non-deterministic Polynomial time (NP)-complete and therefore no exact algorithm can be found. As such, various heuristics and approximation algorithms have been proposed to solve the MCP problem. This paper proposed a solution to the MCP problem using genetic algorithm (GA). The effectiveness of the proposed algorithm is evaluated through simulation. The performance of the algorithm is then compared with an exact algorithm called the depth first search and a common shortest path algorithm called the Dijkstra's algorithm. The result of the simulation shows that the performance of the proposed algorithm is almost comparable to an exact algorithm, while at the same time can execute much faster. The proposed algorithm has also been shown to have good network link utilization and is able to scale well with network size. � 2011 Academic Journals. Final 2023-12-29T07:47:28Z 2023-12-29T07:47:28Z 2011 Article 10.5897/IJPS11.613 2-s2.0-83655172492 https://www.scopus.com/inward/record.uri?eid=2-s2.0-83655172492&doi=10.5897%2fIJPS11.613&partnerID=40&md5=1cfcfb5dc4b9782b2422681561c4a6fd https://irepository.uniten.edu.my/handle/123456789/30406 6 33 7524 7539 Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Genetic algorithm
Multi-constrained path problem
Quality-of-service (qos)
Routing
spellingShingle Genetic algorithm
Multi-constrained path problem
Quality-of-service (qos)
Routing
Yussof S.
Performance analysis of genetic algorithm (GA)-based multi-constrained path routing algorithm
description To support networked multimedia applications, it is important for the network to provide guaranteed quality-of-service (QoS). One way to provide such services is for the network to perform QoS routing, where the path taken must fulfill a given set of constraints. Multi-constrained path (MCP) problem refers to the problem of finding a path through a network subject to multiple additive constraints. It has been proven that this problem is Non-deterministic Polynomial time (NP)-complete and therefore no exact algorithm can be found. As such, various heuristics and approximation algorithms have been proposed to solve the MCP problem. This paper proposed a solution to the MCP problem using genetic algorithm (GA). The effectiveness of the proposed algorithm is evaluated through simulation. The performance of the algorithm is then compared with an exact algorithm called the depth first search and a common shortest path algorithm called the Dijkstra's algorithm. The result of the simulation shows that the performance of the proposed algorithm is almost comparable to an exact algorithm, while at the same time can execute much faster. The proposed algorithm has also been shown to have good network link utilization and is able to scale well with network size. � 2011 Academic Journals.
author2 16023225600
author_facet 16023225600
Yussof S.
format Article
author Yussof S.
author_sort Yussof S.
title Performance analysis of genetic algorithm (GA)-based multi-constrained path routing algorithm
title_short Performance analysis of genetic algorithm (GA)-based multi-constrained path routing algorithm
title_full Performance analysis of genetic algorithm (GA)-based multi-constrained path routing algorithm
title_fullStr Performance analysis of genetic algorithm (GA)-based multi-constrained path routing algorithm
title_full_unstemmed Performance analysis of genetic algorithm (GA)-based multi-constrained path routing algorithm
title_sort performance analysis of genetic algorithm (ga)-based multi-constrained path routing algorithm
publishDate 2023
_version_ 1806425867034820608