Simulated evolution and simulated annealing algorithms for solving multi-objective open shortest path first weight setting problem

Optimal utilization of resources in present-day communication networks is a challenging task. Routing plays an important role in achieving optimal resource utilization. The open shortest path first (OSPF) routing protocol is widely used for routing packets from a source node to a destination node. T...

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Veröffentlicht in:	Applied intelligence (Dordrecht, Netherlands) Jg. 41; H. 2; S. 348 - 365
Hauptverfasser:	Mohiuddin, Mohammed A., Khan, Salman A., Engelbrecht, Andries P.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Boston Springer US 01.09.2014 Kluwer Springer Nature B.V
Schlagworte:	Access methods and protocols, osi model Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Artificial Intelligence Communications networks Computer engineering Computer networks Computer Science Computer science; control theory; systems Computer systems and distributed systems. User interface Cost function Exact sciences and technology Fuzzy Fuzzy logic Fuzzy set theory Information retrieval. Graph Internet Links Load Machines Manufacturing Mechanical Engineering Objectives Optimization Processes Routing (telecommunications) Software Telecommunications Telecommunications and information theory Teleprocessing networks. Isdn Theoretical computing Fuzzy logic Routing Open shortest path first algorithm Optimization Simulated evolution Simulated annealing Internet protocol Multiobjective programming Shortest path Iterative method Network management Communication network Genetic algorithm Heuristic method NP hard problem Routing protocols Mathematical programming
ISSN:	0924-669X, 1573-7497
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Abstract	Optimal utilization of resources in present-day communication networks is a challenging task. Routing plays an important role in achieving optimal resource utilization. The open shortest path first (OSPF) routing protocol is widely used for routing packets from a source node to a destination node. This protocol assigns weights (or costs) to the links of a network. These weights are used to determine the shortest path between all sources to all destination nodes. Assignment of these weights to the links is classified as an NP-hard problem. This paper formulates the OSPF weight setting problem as a multi-objective optimization problem, with maximum utilization, number of congested links, and number of unused links as the optimization objectives. Since the objectives are conflicting in nature, an efficient approach is needed to balance the trade-off between these objectives. Fuzzy logic has been shown to efficiently solve multi-objective optimization problems. A fuzzy cost function for the OSPF weight setting problem is developed in this paper based on the Unified And-OR (UAO) operator. Two iterative heuristics, namely, simulated annealing (SA) and simulated evolution (SimE) have been implemented to solve the multi-objective OSPF weight setting problem using a fuzzy cost function. Results are compared with that found using other cost functions proposed in the literature (Sqalli et al. in Network Operations and Management Symposium, NOMS, 2006 ). Results suggest that, overall, the fuzzy cost function performs better than existing cost functions, with respect to both SA and SimE. Furthermore, SimE shows superior performance compared to SA. In addition, a comparison of SimE with NSGA-II shows that, overall, SimE demonstrates slightly better performance in terms of quality of solutions.
AbstractList	Optimal utilization of resources in present-day communication networks is a challenging task. Routing plays an important role in achieving optimal resource utilization. The open shortest path first (OSPF) routing protocol is widely used for routing packets from a source node to a destination node. This protocol assigns weights (or costs) to the links of a network. These weights are used to determine the shortest path between all sources to all destination nodes. Assignment of these weights to the links is classified as an NP-hard problem. This paper formulates the OSPF weight setting problem as a multi-objective optimization problem, with maximum utilization, number of congested links, and number of unused links as the optimization objectives. Since the objectives are conflicting in nature, an efficient approach is needed to balance the trade-off between these objectives. Fuzzy logic has been shown to efficiently solve multi-objective optimization problems. A fuzzy cost function for the OSPF weight setting problem is developed in this paper based on the Unified And-OR (UAO) operator. Two iterative heuristics, namely, simulated annealing (SA) and simulated evolution (SimE) have been implemented to solve the multi-objective OSPF weight setting problem using a fuzzy cost function. Results are compared with that found using other cost functions proposed in the literature (Sqalli et al. in Network Operations and Management Symposium, NOMS, 2006 ). Results suggest that, overall, the fuzzy cost function performs better than existing cost functions, with respect to both SA and SimE. Furthermore, SimE shows superior performance compared to SA. In addition, a comparison of SimE with NSGA-II shows that, overall, SimE demonstrates slightly better performance in terms of quality of solutions.[PUBLICATION ABSTRACT] Optimal utilization of resources in present-day communication networks is a challenging task. Routing plays an important role in achieving optimal resource utilization. The open shortest path first (OSPF) routing protocol is widely used for routing packets from a source node to a destination node. This protocol assigns weights (or costs) to the links of a network. These weights are used to determine the shortest path between all sources to all destination nodes. Assignment of these weights to the links is classified as an NP-hard problem. This paper formulates the OSPF weight setting problem as a multi-objective optimization problem, with maximum utilization, number of congested links, and number of unused links as the optimization objectives. Since the objectives are conflicting in nature, an efficient approach is needed to balance the trade-off between these objectives. Fuzzy logic has been shown to efficiently solve multi-objective optimization problems. A fuzzy cost function for the OSPF weight setting problem is developed in this paper based on the Unified And-OR (UAO) operator. Two iterative heuristics, namely, simulated annealing (SA) and simulated evolution (SimE) have been implemented to solve the multi-objective OSPF weight setting problem using a fuzzy cost function. Results are compared with that found using other cost functions proposed in the literature (Sqalli et al. in Network Operations and Management Symposium, NOMS, 2006). Results suggest that, overall, the fuzzy cost function performs better than existing cost functions, with respect to both SA and SimE. Furthermore, SimE shows superior performance compared to SA. In addition, a comparison of SimE with NSGA-II shows that, overall, SimE demonstrates slightly better performance in terms of quality of solutions. Optimal utilization of resources in present-day communication networks is a challenging task. Routing plays an important role in achieving optimal resource utilization. The open shortest path first (OSPF) routing protocol is widely used for routing packets from a source node to a destination node. This protocol assigns weights (or costs) to the links of a network. These weights are used to determine the shortest path between all sources to all destination nodes. Assignment of these weights to the links is classified as an NP-hard problem. This paper formulates the OSPF weight setting problem as a multi-objective optimization problem, with maximum utilization, number of congested links, and number of unused links as the optimization objectives. Since the objectives are conflicting in nature, an efficient approach is needed to balance the trade-off between these objectives. Fuzzy logic has been shown to efficiently solve multi-objective optimization problems. A fuzzy cost function for the OSPF weight setting problem is developed in this paper based on the Unified And-OR (UAO) operator. Two iterative heuristics, namely, simulated annealing (SA) and simulated evolution (SimE) have been implemented to solve the multi-objective OSPF weight setting problem using a fuzzy cost function. Results are compared with that found using other cost functions proposed in the literature (Sqalli et al. in Network Operations and Management Symposium, NOMS, 2006 ). Results suggest that, overall, the fuzzy cost function performs better than existing cost functions, with respect to both SA and SimE. Furthermore, SimE shows superior performance compared to SA. In addition, a comparison of SimE with NSGA-II shows that, overall, SimE demonstrates slightly better performance in terms of quality of solutions.
Author	Mohiuddin, Mohammed A. Khan, Salman A. Engelbrecht, Andries P.
Author_xml	– sequence: 1 givenname: Mohammed A. surname: Mohiuddin fullname: Mohiuddin, Mohammed A. email: waseem_aijaz@yahoo.com organization: Department of Computer Science, University of Pretoria – sequence: 2 givenname: Salman A. surname: Khan fullname: Khan, Salman A. organization: Department of Computer Science, University of Pretoria, Department of Computer Engineering, College of Information Technology, University of Bahrain – sequence: 3 givenname: Andries P. surname: Engelbrecht fullname: Engelbrecht, Andries P. organization: Department of Computer Science, University of Pretoria
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Keywords	Fuzzy logic Routing Open shortest path first algorithm Optimization Simulated evolution Simulated annealing Internet protocol Multiobjective programming Shortest path Iterative method Network management Communication network Genetic algorithm Heuristic method NP hard problem Routing protocols Mathematical programming
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Snippet	Optimal utilization of resources in present-day communication networks is a challenging task. Routing plays an important role in achieving optimal resource...
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SubjectTerms	Access methods and protocols, osi model Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Artificial Intelligence Communications networks Computer engineering Computer networks Computer Science Computer science; control theory; systems Computer systems and distributed systems. User interface Cost function Exact sciences and technology Fuzzy Fuzzy logic Fuzzy set theory Information retrieval. Graph Internet Links Load Machines Manufacturing Mechanical Engineering Objectives Optimization Processes Routing (telecommunications) Software Telecommunications Telecommunications and information theory Teleprocessing networks. Isdn Theoretical computing
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Title	Simulated evolution and simulated annealing algorithms for solving multi-objective open shortest path first weight setting problem
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