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Metaheuristics in combinatorial optimization: Overview and conceptual comparison
 ACM COMPUTING SURVEYS
, 2003
"... The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important meta ..."
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Cited by 194 (14 self)
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The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behaviour of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.
Probability distribution of solution time in GRASP: An experimental investigation
 J Heuristic
"... Abstract. A GRASP (greedy randomized adaptive search procedure) is a multistart metaheuristic for combinatorial optimization. We study the probability distributions of solution time to a suboptimal target value in five GRASPs that have appeared in the literature and for which source code is availa ..."
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Cited by 49 (31 self)
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Abstract. A GRASP (greedy randomized adaptive search procedure) is a multistart metaheuristic for combinatorial optimization. We study the probability distributions of solution time to a suboptimal target value in five GRASPs that have appeared in the literature and for which source code is available. The distributions are estimated by running 12,000 independent runs of the heuristic. Standard methodology for graphical analysis is used to compare the empirical and theoretical distributions and estimate the parameters of the distributions. We conclude that the solution time to a suboptimal target value fits a twoparameter exponential distribution. Hence, it is possible to approximately achieve linear speedup by implementing GRASP in parallel. 1.
An Approach for Mixed Upward Planarization
 In Proc. 7th International Workshop on Algorithms and Data Structures (WADS’01
, 2003
"... In this paper, we consider the problem of finding a mixed upward planarization of a mixed graph, i.e., a graph with directed and undirected edges. The problem is a generalization of the planarization problem for undirected graphs and is motivated by several applications in graph drawing. ..."
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Cited by 13 (2 self)
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In this paper, we consider the problem of finding a mixed upward planarization of a mixed graph, i.e., a graph with directed and undirected edges. The problem is a generalization of the planarization problem for undirected graphs and is motivated by several applications in graph drawing.
Algorithm 797: Fortran Subroutines for Approximate Solution Of . . .
 ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE
, 1999
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Two New Approximation Algorithms for the Maximum Planar Subgraph Problem
, 2006
"... The maximum planar subgraph problem (MPS) is defined as follows: given a graph G, find a largest planar subgraph of G. The problem is NPhard and it has applications in graph drawing and resource location optimization. Călinescu et al. [J. Alg. 27, 269302 (1998)] presented the first approximation a ..."
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Cited by 1 (0 self)
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The maximum planar subgraph problem (MPS) is defined as follows: given a graph G, find a largest planar subgraph of G. The problem is NPhard and it has applications in graph drawing and resource location optimization. Călinescu et al. [J. Alg. 27, 269302 (1998)] presented the first approximation algorithms for MPS with nontrivial performance ratios. Two algorithms were given, a simple algorithm which runs in linear time for boundeddegree graphs with a ratio 7/18 and a more complicated algorithm with a ratio 4/9. Both algorithms produce outerplanar subgraphs. In this article we present two new versions of the simpler algorithm. The first new algorithm still runs in the same time, produces outerplanar subgraphs, has at least the same performance ratio as the original algorithm, but in practice it finds larger planar subgraphs than the original algorithm. The second new algorithm has similar properties to the first algorithm, but it produces only planar subgraphs. We conjecture that the performance ratios of our algorithms are at least 4/9 for MPS. We experimentally compare the new algorithms against the original simple algorithm. We also apply the new algorithms for approximating the thickness and outerthickness of a graph. Experiments show that the new algorithms produce clearly better approximations than the original simple algorithm by Călinescu et al.
Solving the Graph Planarization Problem Using an Improved Genetic Algorithm
, 2006
"... An improved genetic algorithm for solving the graph planarization problem is presented. The improved genetic algorithm which is designed to embed a graph on a plane, performs crossover and mutation conditionally instead of probability. The improved genetic algorithm is verified by a large number of ..."
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An improved genetic algorithm for solving the graph planarization problem is presented. The improved genetic algorithm which is designed to embed a graph on a plane, performs crossover and mutation conditionally instead of probability. The improved genetic algorithm is verified by a large number of simulation runs and compared with other algorithms. The experimental results show that the improved genetic algorithm performs remarkably well and outperforms its competitors.
GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURES
"... Abstract. 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 se ..."
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Abstract. 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. 1.
GRASP with PathRelinking: Recent Advances and Applications
, 2005
"... 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 ..."
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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.