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54
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.
VLSI cell placement techniques
 ACM Computing Surveys
, 1991
"... VLSI cell placement problem is known to be NP complete. A wide repertoire of heuristic algorithms exists in the literature for efficiently arranging the logic cells on a VLSI chip. The objective of this paper is to present a comprehensive survey of the various cell placement techniques, with emphasi ..."
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Cited by 75 (0 self)
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VLSI cell placement problem is known to be NP complete. A wide repertoire of heuristic algorithms exists in the literature for efficiently arranging the logic cells on a VLSI chip. The objective of this paper is to present a comprehensive survey of the various cell placement techniques, with emphasis on standard ce11and macro
Network Correlated Data Gathering with Explicit Communication: NPCompleteness and Algorithms
"... We consider the problem of correlated data gathering by a network with a sink node and a tree based communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we ..."
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Cited by 39 (8 self)
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We consider the problem of correlated data gathering by a network with a sink node and a tree based communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we consider a joint entropy based coding model with explicit communication where coding is simple and the transmission structure optimization is di#cult. We first formulate the optimization problem definition in the general case and then we study further a network setting where the entropy conditioning at nodes does not depend on the amount of side information, but only on its availability. We prove that even in this simple case, the optimization problem is NPhard. We propose some e#cient, scalable, and distributed heuristic approximation algorithms for solving this problem and show by numerical simulations that the total transmission cost can be significantly improved over direct transmission or the shortest path tree. We also present an approximation algorithm that provides a tree transmission structure with total cost within a constant factor from the optimal.
Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems
 SIAM Journal on Optimization
, 2004
"... A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. This class combines and extends the AudetDennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPSfilter algorithms for gene ..."
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Cited by 37 (8 self)
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A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. This class combines and extends the AudetDennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPSfilter algorithms for general nonlinear constraints. In generalizing existing algorithms, new theoretical convergence results are presented that reduce seamlessly to existing results for more specific classes of problems. While no local continuity or smoothness assumptions are required to apply the algorithm, a hierarchy of theoretical convergence results based on the Clarke calculus is given, in which local smoothness dictate what can be proved about certain limit points generated by the algorithm. To demonstrate the usefulness of the algorithm, the algorithm is applied to the design of a loadbearing thermal insulation system. We believe this is the first algorithm with provable convergence results to directly target this class of problems.
A SearchBased Automated TestData Generation Framework for Safety Critical Software
, 2000
"... Software ..."
Triangulation of Graphs  Algorithms Giving Small Total State Space
, 1990
"... The problem of achieving small total state space for triangulated belief graphs (networks) is considered. It is an NPcomplete problem to find a triangulation with minimum state space. Our interest ..."
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Cited by 35 (0 self)
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The problem of achieving small total state space for triangulated belief graphs (networks) is considered. It is an NPcomplete problem to find a triangulation with minimum state space. Our interest
Hybrid Genetic Algorithm, Simulated Annealing and Tabu Search Methods for Vehicle Routing Problems with Time Windows. Working paper
, 1993
"... The Vehicle Routing Problem with Time Windows (VRPTW) involves servicing a set of customers, with earliest and latest time deadlines, with varying demands using capacitated vehicles with limited travel times. The objective of the problem is to service all customers while minimizing the number of veh ..."
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Cited by 32 (1 self)
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The Vehicle Routing Problem with Time Windows (VRPTW) involves servicing a set of customers, with earliest and latest time deadlines, with varying demands using capacitated vehicles with limited travel times. The objective of the problem is to service all customers while minimizing the number of vehicles and travel distance without violating the capacity and travel time of the vehicles and customer time constraints. In this paper we describe a λinterchange mechanism that moves customers between routes to generate neighborhood solutions for the VRPTW. The λinterchange neighborhood is searched using Simulated Annealing and Tabu Search strategies. The initial solutions to the VRPTW are obtained using the PushForward Insertion heuristic and a Genetic Algorithm based sectoring heuristic. The hybrid combination of the implemented heuristics, collectively known as the GenSAT system, were used to solve 60 problems from the literature with customer sizes varying from 100 to 417 customers. The computational results of GenSAT obtained new best solutions for 40 test problems. For the remaining 20 test problems, 11 solutions obtained by the GenSAT system equal previously known best solutions. The average performance of GenSAT is significantly better than known competing heuristics. For known optimal solutions to the VRPTW problems, the GenSAT system obtained the optimal number of vehicles. Keywords:
Applications to timetabling
 Handbook of Graph Theory, chapter 5.6
, 2004
"... Abstract Automating the neighbourhood selection process in an iterative approach that uses multiple heuristics is not a trivial task. Hyperheuristics are search methodologies that not only aim to provide a general framework for solving problem instances at different difficulty levels in a given dom ..."
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Cited by 29 (17 self)
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Abstract Automating the neighbourhood selection process in an iterative approach that uses multiple heuristics is not a trivial task. Hyperheuristics are search methodologies that not only aim to provide a general framework for solving problem instances at different difficulty levels in a given domain, but a key goal is also to extend the level of generality so that different problems from different domains can also be solved. Indeed, a major challenge is to explore how the heuristic design process might be automated. Almost all existing iterative selection hyperheuristics performing single point search contain two successive stages; heuristic selection and move acceptance. Different operators can be used in either of the stages. Recent studies explore ways of introducing learning mechanisms into the search process for improving the performance of hyperheuristics. In this study, a broad empirical analysis is performed comparing Monte Carlo based hyperheuristics for solving capacitated examination timetabling problems. One of these hyperheuristics is an approach that overlaps two stages and presents them in a single algorithmic body. A learning heuristic selection method (L) operates in harmony with a simulated annealing move acceptance method using reheating (SA) based on some shared variables. Yet, the heuristic selection and move
An investigation of automated planograms using a simulated annealing based hyperheuristics
 Progress as Real Problem Solver  (Operations Research/Computer Science Interface Serices, Vol.32
, 2005
"... This paper formulates the shelf space allocation problem as a nonlinear function of the product net profit and storeinventory. We show that this model is an extension of multiknapsack problem, which is itself an NPhard problem. A twostage relaxation is carried out to get an upper bound of the m ..."
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Cited by 16 (9 self)
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This paper formulates the shelf space allocation problem as a nonlinear function of the product net profit and storeinventory. We show that this model is an extension of multiknapsack problem, which is itself an NPhard problem. A twostage relaxation is carried out to get an upper bound of the model. A simulated annealing based hyperheuristic algorithm is proposed to solve several problem instances with different problem sizes and space ratios. The results show that the simulated annealing hyperheuristic significantly outperforms two conventional simulated annealing algorithms and other hyperheuristics for all problem instances. The experimental results show that our approach is a robust and efficient approach for the shelf space allocation problem. hyperheuristics, simulated annealing, shelf space allocation, planograms 1.
Genetic Algorithms for Combinatorial Optimization: The Assembly Line Balancing Problem
 ORSA Journal on Computing
, 1994
"... Genetic algorithms are one example of the use of a random element within an algorithm for combinatorial optimization. We consider the application of the genetic algorithm to a particular problem, the Assembly Line Balancing Problem. A general description of genetic algorithms is given, and their spe ..."
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Cited by 15 (0 self)
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Genetic algorithms are one example of the use of a random element within an algorithm for combinatorial optimization. We consider the application of the genetic algorithm to a particular problem, the Assembly Line Balancing Problem. A general description of genetic algorithms is given, and their specialized use on our testbed problems is discussed. We carry out extensive computational testing to find appropriate values for the various parameters associated with this genetic algorithm. These experiments underscore the importance of the correct choice of a scaling parameter and mutation rate to ensure the good performance of a genetic algorithm. We also describe a parallel implementation of the genetic algorithm and give some comparisons between the parallel and serial implementations. Both versions of the algorithm are shown to be effective in producing good solutions for problems of this type (with appropriately chosen parameters). Subject classifications: Programming: combinatorial ...