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GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURES
, 2002
"... GRASP is a multistart metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phas ..."
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Cited by 637 (79 self)
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GRASP is a multistart metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phase. The best overall solution is kept as the result. In this chapter, we first describe the basic components of GRASP. Successful implementation techniques and parameter tuning strategies are discussed and illustrated by numerical results obtained for different applications. Enhanced or alternative solution construction mechanisms and techniques to speed up the search are also described: Reactive GRASP, cost perturbations, bias functions, memory and learning, local search on partially constructed solutions, hashing, and filtering. We also discuss in detail implementation strategies of memorybased intensification and postoptimization techniques using pathrelinking. Hybridizations with other metaheuristics, parallelization strategies, and applications are also reviewed.
Internet traffic engineering by optimizing OSPF weights
 in Proc. IEEE INFOCOM
, 2000
"... Abstract—Open Shortest Path First (OSPF) is the most commonly used intradomain internet routing protocol. Traffic flow is routed along shortest paths, splitting flow at nodes where several outgoing links are on shortest paths to the destination. The weights of the links, and thereby the shortest pa ..."
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Cited by 405 (13 self)
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Abstract—Open Shortest Path First (OSPF) is the most commonly used intradomain internet routing protocol. Traffic flow is routed along shortest paths, splitting flow at nodes where several outgoing links are on shortest paths to the destination. The weights of the links, and thereby the shortest path routes, can be changed by the network operator. The weights could be set proportional to their physical distances, but often the main goal is to avoid congestion, i.e. overloading of links, and the standard heuristic recommended by Cisco is to make the weight of a link inversely proportional to its capacity. Our starting point was a proposed AT&T WorldNet backbone with demands projected from previous measurements. The desire was to optimize the weight setting based on the projected demands. We showed that optimizing the weight settings for a given set of demands is NPhard, so we resorted to a local search heuristic. Surprisingly it turned out that for the proposed AT&T WorldNet backbone, we found weight settings that performed
Increasing internet capacity using local search
 Computational Optimization and Applications
, 2004
"... but often the main goal is to avoid congestion, i.e. overloading of links, and the standard heuristic recommended by Cisco (a major router vendor) is to make the weight of a link inversely proportional to its capacity. We study the problem of optimizing OSPF weights for a given a set of projected de ..."
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Cited by 95 (8 self)
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but often the main goal is to avoid congestion, i.e. overloading of links, and the standard heuristic recommended by Cisco (a major router vendor) is to make the weight of a link inversely proportional to its capacity. We study the problem of optimizing OSPF weights for a given a set of projected demands so as to avoid congestion. We show this problem is NPhard and propose a local search heuristic to solve it. We also provide worstcase results about the performance of OSPF routing vs. an optimal multicommodity flow routing. Our numerical experiments compare the results obtained with our local search heuristic to the optimal multicommodity flow routing, as well as simple and commonly used heuristics for setting the weights. Experiments were done with a proposed nextgeneration AT&T WorldNet backbone as well as synthetic internetworks.
Genetic algorithms and tabu search: hybrids for optimization
 Comput. Oper. Res
, 1995
"... Scope and Purpo~The development of hybrid procedures for optimization focuses on enhancing the strengths and compensating for the weaknesses of two or more complementary approaches. The goal is to intelligently combine the key elements of competing methodologies to create a superior solution proce ..."
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Cited by 41 (1 self)
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Scope and Purpo~The development of hybrid procedures for optimization focuses on enhancing the strengths and compensating for the weaknesses of two or more complementary approaches. The goal is to intelligently combine the key elements of competing methodologies to create a superior solution procedure. Our paper explores the marriage between tabu search and genetic algorithms in the context of solving difficult optimization problems. Among other ideas, the procedure known as scatter search is revisited to create a unifying environment where tabu search and genetic algorithms can coexist. Overall, our objective is to demonstrate that it is possible to establish useful connections between methods whose search principles may superficially appear unrelated. AbstractGenetic algorithms and tabu search have a number of significant differences. They also have some common bonds, often unrecognized. We explore the nature of the connections between the methods, and show that a variety of opportunities exist for creating hybrid approaches to take advantage of their complementary features. Tabu search has pioneered the systematic exploration of memory functions in search processes, while genetic algorithms have pioneered the implementation of methods that exploit the idea of combining solutions. There is also another approach, related to both of these, that is frequently overlooked. The procedure called scatter search, whose origins overlap with those of tabu search (and
Tabu search fundamentals and uses
, 1995
"... Tabu search has achieved widespread successes in solving practical optimization problems. Applications are rapidly growing in areas such as resource management, process design, logistics, technology planning, and general combinatorial optimization. Hybrids with other procedures, both heuristic and a ..."
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Cited by 24 (0 self)
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Tabu search has achieved widespread successes in solving practical optimization problems. Applications are rapidly growing in areas such as resource management, process design, logistics, technology planning, and general combinatorial optimization. Hybrids with other procedures, both heuristic and algorithmic, have also produced productive results. We examine some of the principal features of tabu search that are most responsible for its successes, and that offer a basis for improved solution methods in the future. Note: This expanded version contains additional illustrations and information on candidate list strategies, probabilistic tabu search, strategic oscillation and parallel processing options. Sections have also been added on principles of intelligent search.
Applications of Modern Heuristic Search Methods to Pattern Sequencing Problems
 COMPUTERS & OPERATIONS RESEARCH
, 1999
"... This article describes applications of modern heuristic search methods to pattern sequencing problems, i.e., problems seeking for a permutation of the rows of a given matrix with respect to some given objective function. We consider two di#erent objectives: Minimization of the number of simultane ..."
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Cited by 24 (6 self)
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This article describes applications of modern heuristic search methods to pattern sequencing problems, i.e., problems seeking for a permutation of the rows of a given matrix with respect to some given objective function. We consider two di#erent objectives: Minimization of the number of simultaneously open stacks and minimization of the average order spread. Both objectives require the adaptive evaluation of changed solutions to allow an e#cient application of neighbourhood search techniques.
Local Search with Memory: Benchmarking RTS
, 1994
"... this paper is dedicated to benchmarking RTS, we completely describe the algorithm but omit the detailed analysis given in the cited papers because it is not necessary for the scope of this work. The Tabu Search technique (TS) represents a metastrategy that permits to complement a local search heuri ..."
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Cited by 19 (6 self)
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this paper is dedicated to benchmarking RTS, we completely describe the algorithm but omit the detailed analysis given in the cited papers because it is not necessary for the scope of this work. The Tabu Search technique (TS) represents a metastrategy that permits to complement a local search heuristic based on a specific neighborhood with memorybased mechanisms designed to avoid cycles, see [13] and [14] for two seminal papers and [34] for a tutorial
Guided Local Search  An Illustrative Example in Function Optimisation
 In BT Technology Journal, Vol.16, No.3
, 1998
"... The Guided Local Search method has been successfully applied to a number of hard combinatorial optimisation problems from the wellknown TSP and QAP to real world problems such as Frequency Assignment and Workforce Scheduling. In this paper, we are demonstrating that the potential applications of GL ..."
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Cited by 18 (5 self)
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The Guided Local Search method has been successfully applied to a number of hard combinatorial optimisation problems from the wellknown TSP and QAP to real world problems such as Frequency Assignment and Workforce Scheduling. In this paper, we are demonstrating that the potential applications of GLS are not limited to optimisation problems of discrete nature but also to difficult continuous optimisation problems. Continuous optimisation problems arise in many engineering disciplines (such as electrical and mechanical engineering) in the context of analysis, design or simulation tasks. The problem examined gives an illustrative example of the behaviour of GLS, providing insights on the mechanisms of the algorithm. 1.
Function Optimization using Guided Local Search
, 1995
"... In this report, we examine the potential use of Guided Local Search (GLS) for function optimization. In order to apply GLS, the function to be minimized is augmented with a set of penalty terms that enable local search to escape from local minima. The function F6 is used to demonstrate the proposed ..."
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Cited by 14 (3 self)
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In this report, we examine the potential use of Guided Local Search (GLS) for function optimization. In order to apply GLS, the function to be minimized is augmented with a set of penalty terms that enable local search to escape from local minima. The function F6 is used to demonstrate the proposed technique. 1. Introduction In this report, we present preliminary findings on the potential use of Guided Local Search (GLS) for function optimization. GLS is a metaheuristic for guiding local search [3] to escape local minima and visit promising solutions. GLS has been used to tackle difficult combinatorial optimization problems [7,5] and derives itself from the GENET network for constraint satisfaction problems [6]. Function optimization can be seen as a combinatorial problem by encoding real variables as binary strings [2]. In the simple case of binary encoding, binary string values are converted to integers which then are scaled by the appropriate coefficient to give real values in the...