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A genetic algorithm for the weight setting problem in OSPF routing
 Journal of Combinatorial Optimization
, 2002
"... Abstract. With the growth of the Internet, Internet Service Providers (ISPs) try to meet the increasing traffic demand with new technology and improved utilization of existing resources. Routing of data packets can affect network utilization. Packets are sent along network paths from source to desti ..."
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Cited by 111 (26 self)
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Abstract. With the growth of the Internet, Internet Service Providers (ISPs) try to meet the increasing traffic demand with new technology and improved utilization of existing resources. Routing of data packets can affect network utilization. Packets are sent along network paths from source to destination following a protocol. Open Shortest Path First (OSPF) is the most commonly used intradomain Internet routing protocol (IRP). Traffic flow is routed along shortest paths, splitting flow at nodes with several outgoing links on a shortest path to the destination IP address. Link weights are assigned by the network operator. A path length is the sum of the weights of the links in the path. The OSPF weight setting (OSPFWS) problem seeks a set of weights that optimizes network performance. We study the problem of optimizing OSPF weights, given a set of projected demands, with the objective of minimizing network congestion. The weight assignment problem is NPhard. We present a genetic algorithm (GA) to solve the OSPFWS problem. We compare our results with the best known and commonly used heuristics for OSPF weight setting, as well as with a lower bound of the optimal multicommodity flow routing, which is a linear programming relaxation of the OSPFWS problem. Computational experiments are made on the AT&T Worldnet backbone with projected demands, and on twelve instances of synthetic networks. 1.
GRASP with pathrelinking: Recent advances and applications
 Metaheuristics: Progress as Real Problem Solvers
, 2005
"... Abstract: Pathrelinking is a major enhancement to the basic greedy randomized adaptive search procedure (GRASP), leading to significant improvements in solution time and quality. Pathrelinking adds a memory mechanism to GRASP by providing an intensification strategy that explores trajectories conn ..."
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Cited by 44 (25 self)
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Abstract: Pathrelinking is a major enhancement to the basic greedy randomized adaptive search procedure (GRASP), leading to significant improvements in solution time and quality. Pathrelinking adds a memory mechanism to GRASP by providing an intensification strategy that explores trajectories connecting GRASP solutions and the best elite solutions previously produced during the search. This paper reviews recent advances and applications of GRASP with pathrelinking. A brief review of GRASP is given. This is followed by a description of pathrelinking and how it is incorporated into GRASP. Several recent applications of GRASP with pathrelinking are reviewed. The paper concludes with a discussion of extensions to this strategy, concerning in particular parallel implementations and applications of pathrelinking with other metaheuristics.
Randomized Heuristics for the MaxCut Problem
 Optimization Methods and Software
, 2002
"... Given an undirected graph with edge weights, the MAXCUT problem consists in finding a partition of the nodes into two subsets, such that the sum of the weights of the edges having endpoints in different subsets is maximized. ..."
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Cited by 41 (16 self)
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Given an undirected graph with edge weights, the MAXCUT problem consists in finding a partition of the nodes into two subsets, such that the sum of the weights of the edges having endpoints in different subsets is maximized.
Greedy randomized adaptive search procedures. In: Handbook of applied optimization (Eds.
 P.M. Pardalos, M.G.C. Resende). Oxford Univ.
, 2002
"... ..."
Parallel Grasp With PathRelinking For Job Shop Scheduling
 Parallel Computing
, 2002
"... In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines. Each job is required to complete a set of operations in a fixed order. Each operation is processed on a specific machine for a fixed duration. A machine can process no more than one job at a ..."
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Cited by 37 (18 self)
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In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines. Each job is required to complete a set of operations in a fixed order. Each operation is processed on a specific machine for a fixed duration. A machine can process no more than one job at a time and once a job initiates processing on a given machine it must complete processing without interruption. A schedule is an assignment of operations to time slots on the machines. The objective of the JSP is to find a schedule that minimizes the maximum completion time, or makespan, of the jobs. In this paper, we describe a parallel greedy randomized adaptive search procedure (GRASP) with pathrelinking for the JSP. A GRASP is a metaheuristic for combinatorial optimization. It usually consists of a construction procedure based on a greedy randomized algorithm and of a local search. Pathrelinking is an intensification strategy that explores trajectories that connect high quality solutions. Independent and cooperative parallelization strategies are described and implemented. Computational experience on a large set of standard test problems indicates that the parallel GRASP with pathrelinking finds goodquality approximate solutions of the job shop scheduling problem.
Strategies for the parallel implementation of metaheuristics
 Essays and Surveys in Metaheuristics
, 2002
"... Abstract. Parallel implementationsof metaheuristicsappear quite naturally asan effective alternative to speed up the search for approximate solutions of combinatorial optimization problems. They not only allow solving larger problems or finding improved solutions with respect to their sequential cou ..."
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Cited by 26 (5 self)
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Abstract. Parallel implementationsof metaheuristicsappear quite naturally asan effective alternative to speed up the search for approximate solutions of combinatorial optimization problems. They not only allow solving larger problems or finding improved solutions with respect to their sequential counterparts, but they also lead to more robust algorithms. We review some trends in parallel computing and report recent results about linear speedups that can be obtained with parallel implementations using multiple independent processors. Parallel implementations of tabu search, GRASP, genetic algorithms, simulated annealing, and ant colonies are reviewed and discussed to illustrate the main strategies used in the parallelization of different metaheuristics and their hybrids. 1. Introduction. Although
A greedy randomized adaptive search procedure for job shop scheduling
 IEEE Trans. on Power Systems
, 2001
"... Abstract. In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines. Each job is characterized by a fixed order of operations, each of which is to be processed on a specific machine for a specified duration. Each machine can process at most one job at a ..."
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Cited by 25 (2 self)
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Abstract. In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines. Each job is characterized by a fixed order of operations, each of which is to be processed on a specific machine for a specified duration. Each machine can process at most one job at a time and once a job initiates processing on a given machine it must complete processing uninterrupted. A schedule is an assignment of operations to time slots on the machines. The objective of the JSP is to find a schedule that minimizes the maximum completion time, or makespan, of the jobs. In this paper, we describe a greedy randomized adaptive search procedure (GRASP) for the JSP. A GRASP is a metaheuristic for combinatorial optimization. Although GRASP is a general procedure, its basic concepts are customized for the problem being solved. We describe in detail our implementation of GRASP for job shop scheduling. Further, we incorporate to the conventional GRASP two new concepts: an intensification strategy and POP (Proximate Optimality Principle) in the construction phase. These two concepts were first proposed by Fleurent & Glover (1999) in the context of the quadratic assignment problem. Computational experience on a large set of standard test problems indicates that GRASP is a competitive algorithm for finding approximate solutions of the job shop scheduling problem. 1.
GRASP with pathrelinking for the Quadratic Assignment Problem
 Proceedings of Third International Workshop on Experimental and Efficient Algorithms, Lect. Notes Comp. Sci
, 2004
"... This paper describes a GRASP with pathrelinking heuristic for the quadratic assignment problem. GRASP is a multistart procedure, where different points in the search space are probed with local search for highquality solutions. ..."
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Cited by 22 (6 self)
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This paper describes a GRASP with pathrelinking heuristic for the quadratic assignment problem. GRASP is a multistart procedure, where different points in the search space are probed with local search for highquality solutions.
A GRASP For Job Shop Scheduling
 Essays and Surveys on Metaheuristics
, 2000
"... In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines. Each job is characterized by a fixed order of operations, each of which is to be processed on a specific machine for a specified duration. Each machine can process at most one job at a time and o ..."
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Cited by 18 (8 self)
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In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines. Each job is characterized by a fixed order of operations, each of which is to be processed on a specific machine for a specified duration. Each machine can process at most one job at a time and once a job initiates processing on a given machine it must complete processing uninterrupted. A schedule is an assignment of operations to time slots on the machines. The objective of the JSP is to find a schedule that minimizes the maximum completion time, or makespan, of the jobs. In this paper, we describe a greedy randomized adaptive search procedure (GRASP) for the JSP. A GRASP is a metaheuristic for combinatorial optimization. Although GRASP is a general procedure, its basic concepts are customized for the problem being solved. We describe in detail our implementation of GRASP for job shop scheduling. Further, we incorporate to the conventional GRASP two new concepts: an ...