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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 169 (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 42 (28 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 15 (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
"... ..."
Apptopinv  user’s guide
, 2003
"... The maximum planar subgraph, maximum outerplanar subgraph, the thickness and outerthickness of a graph are all NPcomplete optimization problems. Apptopinv is a program that contains different heuristic algorithms for these four problems: algorithms based on HopcroftTarjan planarity testing algorit ..."
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Cited by 1 (1 self)
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The maximum planar subgraph, maximum outerplanar subgraph, the thickness and outerthickness of a graph are all NPcomplete optimization problems. Apptopinv is a program that contains different heuristic algorithms for these four problems: algorithms based on HopcroftTarjan planarity testing algorithm, the spanningtree heuristic and various algorithms based on the cactustree heuristic. Apptopinv contains also a simulated annealing algorithm that can be used to improve the solutions obtained from other heuristics. Most of the heuristics have also a greedy version. We have implemented graph generators for complete graphs, complete kpartite graphs, complete hypercubes, random graphs, random maximum planar and outerplanar graphs and random regular graphs. Apptopinv supports three different graph file formats. Apptopinv is written in C++ programming language for Linuxplatform and GCC 2.95.3 compiler. To compile the program, a commercial LEDA algorithm
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.
GRASP: BASIC COMPONENTS AND ENHANCEMENTS
"... Abstract. GRASP (Greedy Randomized Adaptive Search Procedures) is a multistart metaheuristic for producing goodquality solutions of combinatorial optimization problems. Each GRASP iteration is usually made up of a construction phase, where a feasible solution is constructed, and a local search phas ..."
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Cited by 1 (0 self)
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Abstract. GRASP (Greedy Randomized Adaptive Search Procedures) is a multistart metaheuristic for producing goodquality solutions of combinatorial optimization problems. Each GRASP iteration is usually made up of a construction phase, where a feasible solution is constructed, and a local search phase which starts at the constructed solution and applies iterative improvement until a locally optimal solution is found. While, in general, the construction phase of GRASP is a randomized greedy algorithm, other types of construction procedures have been proposed. Repeated applications of a construction procedure yields diverse starting solutions for the local search. This chapter gives an overview of GRASP describing its basic components and enhancements to the basic procedure, including reactive GRASP and intensification strategies. 1.
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.
A GRASPBased Approach for Technicians and Interventions Scheduling for Telecommunications
, 2013
"... Abstract. The Technicians and Interventions Scheduling Problem for Telecommunications embeds the scheduling of interventions, the assignment of teams to interventions and the assignment of technicians to teams. Every intervention is characterized, among others attributes, by a priority. The objectiv ..."
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Abstract. The Technicians and Interventions Scheduling Problem for Telecommunications embeds the scheduling of interventions, the assignment of teams to interventions and the assignment of technicians to teams. Every intervention is characterized, among others attributes, by a priority. The objective of this problem is to schedule interventions such that the interventions with the highest priority are scheduled at the earliest time possible while satisfying a set of constraints like the precedence between some interventions and the number of technicians with the required skill level by domain. To solve this problem, we propose a GRASP algorithm based on the dynamic update of the weights assigned to the interventions combined with a local search procedure. We also compute lower bounds and present experimental results that validate the effectiveness of this approach.