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31
A comparison of annealing techniques for academic course scheduling
 Lecture Notes in Computer Science
, 1998
"... Abstract. In this study we have tackled the NPhard problem of academic class scheduling (or timetabling) at the university level. We have investigated a variety of approaches based on simulated annealing, including meanfield annealing, simulated annealing with three different cooling schedules, an ..."
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Cited by 39 (0 self)
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Abstract. In this study we have tackled the NPhard problem of academic class scheduling (or timetabling) at the university level. We have investigated a variety of approaches based on simulated annealing, including meanfield annealing, simulated annealing with three different cooling schedules, and the use of a rulebased preprocessor to provide a good initial solution for annealing. The best results were obtained using simulated annealing with adaptive cooling and reheating as a function of cost, and a rulebased preprocessor. This approach enabled us to obtain valid schedules for the timetabling problem for a large university, using a complex cost function that includes student preferences. None of the other methods were able to provide a complete valid schedule. 1
Using Helpful Sets to Improve Graph Bisections
 Univ. of Paderborn
, 1995
"... We describe a new, linear time heuristic for the improvement of graph bisections. The method is a variant of local search with sophisticated neighborhood relations. It is based on graphtheoretic observations that were used to find upper bounds for the bisection width of regular graphs. Efficiently ..."
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Cited by 36 (20 self)
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We describe a new, linear time heuristic for the improvement of graph bisections. The method is a variant of local search with sophisticated neighborhood relations. It is based on graphtheoretic observations that were used to find upper bounds for the bisection width of regular graphs. Efficiently implemented, the new method can serve as an alternative to the commonly used local heuristics, not only in terms of the quality of attained solutions, but also in terms of space and time requirements. We compare our heuristic with a number of well known bisection algorithms. Extensive measurements show that the new method is a real improvement for graphs of certain types. Keywords: Graph Partitioning, Graph Bisection, Recursive Bisection, Edge Separators, Mapping, Local Search, Parallel Processing. This work was partly supported by the German Research Foundation (DFG Forschergruppe "Effiziente Nutzung massiv paralleler Systeme") and by the ESPRIT Basic Research Action No. 7141 (ALCOM II)....
Distributed Control by Lagrangian Steepest Descent
"... Often adaptive, distributed control can be viewed as an iterated game between independent players. The coupling between the players’ mixed strategies, arising as the system evolves from one instant to the next, is determined by the system designer. Information theory tells us that the most likely j ..."
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Cited by 22 (13 self)
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Often adaptive, distributed control can be viewed as an iterated game between independent players. The coupling between the players’ mixed strategies, arising as the system evolves from one instant to the next, is determined by the system designer. Information theory tells us that the most likely joint strategy of the players, given a value of the expectation of the overall control objective function, is the minimizer of a Lagrangian function of the joint strategy. So the goal of the system designer is to speed evolution of the joint strategy to that Lagrangian minimizing point, lower the expectated value of the control objective function, and repeat. Here we elaborate the theory of algorithms that do this using local descent procedures, and that thereby achieve efficient, adaptive, distributed control.
A Parallel Simulated Annealing Algorithm for Generating 3D Layouts of Undirected Graphs
, 1995
"... In this paper, we introduce a parallel simulated annealing algorithm for generating aesthetically pleasing straightline drawings. The proposed algorithm calculates high quality 3D layouts of arbitrary undirected graphs. Due to the 3D layouts, structure information is presented to the human viewer a ..."
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Cited by 15 (1 self)
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In this paper, we introduce a parallel simulated annealing algorithm for generating aesthetically pleasing straightline drawings. The proposed algorithm calculates high quality 3D layouts of arbitrary undirected graphs. Due to the 3D layouts, structure information is presented to the human viewer at a glance. The computing time of the algorithm is reduced by a new parallel method for exploiting promising intermediate configurations. As the algorithm avoids running into a local minimum of the cost function, it is applicable for the animation of graphs of reasonably larger size than it was possible before. Subsequent to the discussion of the algorithm, empirical data for the performance of the algorithm and the quality of the generated layouts are presented.
Combining Helpful Sets and Parallel Simulated Annealing for the GraphPartitioning Problem
 INT. J. PARALLEL ALGORITHMS AND APPLICATIONS
, 1996
"... In this paper we present a new algorithm for the kpartitioning problem which achieves an improved solution quality compared to known heuristics. We apply the principle of so called "helpful sets", which has shown to be very efficient for graph bisection, to the direct kpartitioning prob ..."
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Cited by 14 (4 self)
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In this paper we present a new algorithm for the kpartitioning problem which achieves an improved solution quality compared to known heuristics. We apply the principle of so called "helpful sets", which has shown to be very efficient for graph bisection, to the direct kpartitioning problem. The principle is extended in several ways. We introduce a new abstraction technique which shrinks the graph during runtime in a dynamic way leading to shorter computation times and improved solutions qualities. The use of stochastic methods provides further improvements in terms of solution quality. Additionally we present a parallel implementation of the new heuristic. The parallel algorithm delivers the same solution quality as the sequential one while providing reasonable parallel efficiency on MIMDsystems of moderate size. All results are verified by experiments for various graphs and processor numbers.
Communication Throughput of Interconnection Networks
 Proc. 19th Int. Symp. on Mathematical Foundations of Computer Science (MFCS '94), Lecture Notes in Computer Science No. 841
, 1994
"... . Modern flow control techniques used for massively parallel computers have made network capacity a more important parameter for the application performance than network latency. Network latency is usually rather low as long as the injection rate is below a specific value. Nowadays the maximal injec ..."
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Cited by 10 (6 self)
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. Modern flow control techniques used for massively parallel computers have made network capacity a more important parameter for the application performance than network latency. Network latency is usually rather low as long as the injection rate is below a specific value. Nowadays the maximal injection rate is usually approximated by the bisection bandwith of the network. We will describe the state of the art in determining the bisection bandwith of interconnection systems. Unfortunately the bisection bandwith leads only to very vague approximations of the communication capacity of a network. We will describe some methods aiming at modeling the maximal network capacity by using probabilistic models. Especially we will present results for the multistage interconnection network which is often used in parallel computing and more general communication applications. The presented results show a rather close relation to results gained by simulations and therefore have the potential to repla...
A General Parallel Simulated Annealing Library and its Application in Airline Industry
 PAREO 1998: First meeting of the PAREO working group on Parallel Processing in Operations Research
, 2000
"... To solve realworld discrete optimization problems approximately metaheuristics such as simulated annealing and other local search methods are commonly used. For large instances of these problems or those with a lot of hard constraints even fast heuristics require a considerable amount of computatio ..."
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Cited by 10 (0 self)
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To solve realworld discrete optimization problems approximately metaheuristics such as simulated annealing and other local search methods are commonly used. For large instances of these problems or those with a lot of hard constraints even fast heuristics require a considerable amount of computational time. At the same time, especially for sensitivity analyses, fast response times are necessary in realworld applications. Therefore, to speed up the computation a parallelization of metaheuristics is very desirable. We present parSA, an objectoriented simulated annealing library based on C++ and using the MPI message passing interface. It provides an automatic, transparent way of parallelizing simulated annealing. The efficient communication in parSA is the main reason for its success in several realworld applications. To demonstrate performance of parSA we address the weekly fleet assignment problem (FAP) as a realworld application. It is one of the optimization problems, which occur in the process of operating an airline. Given a flight schedule and aircraft of different types (subfleets), to each flight leg a subfleet has to be assigned. Large realworld instances have been provided by internationally operating airlines. We show that our heuristic approach using our library parSA is very competitive to the commonly used integer program (IP) approach.
Distributed Combinatorial Optimization
 PROC. OF SOFSEM'93, CZECH REPUBLIK
, 1993
"... This paper reports about research projects of the University of Paderborn in the field of distributed combinatorial optimization. We give an introduction into combinatorial optimization and a brief definition of some important applications. As a first exact solution method we describe branch & ..."
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Cited by 10 (6 self)
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This paper reports about research projects of the University of Paderborn in the field of distributed combinatorial optimization. We give an introduction into combinatorial optimization and a brief definition of some important applications. As a first exact solution method we describe branch & bound and present the results of our work on its distributed implementation. Results of our distributed implementation of iterative deepening conclude the first part about exact methods. In the second part we give an introduction into simulated annealing as a heuristic method and present results of its parallel implementation. This part is concluded with a brief description of genetic algorithms and some other heuristic methods together with some results of their distributed implementation.
Reinforcement learning in distributed domains: Beyond team games
 in Proceedings of the Seventeenth IJCAI
, 2001
"... Using a distributed algorithm rather than a centralized one can be extremely beneficial in large search problems. In addition, the incorporation of machine learning techniques like Reinforcement Learning (RL) into search algorithms has often been found to improve their performance. In this article w ..."
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Cited by 7 (4 self)
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Using a distributed algorithm rather than a centralized one can be extremely beneficial in large search problems. In addition, the incorporation of machine learning techniques like Reinforcement Learning (RL) into search algorithms has often been found to improve their performance. In this article we investigate a search algorithm that combines these properties by employing RL in a distributed manner, essentially using the team game approach. We then present biutility search, which interleaves our distributed algorithm with (centralized) simulated annealing, by using the distributed algorithm to guide the exploration step of the simulated annealing. We investigate using these algorithms in the domain of minimizing the loss of importanceweighted communication data traversing a constellations of communication satellites. To do this we introduce the idea of running these algorithms “on top ” of an underlying, learningfree routing algorithm. They do this by having the actions of the distributed learners be the introduction of virtual “ghost ” traffic into the decisionmaking of the underlying routing algorithm, traffic that “misleads ” the routing algorithm in a way that actually improves performance. We find that using our original distributed RL algorithm to set ghost traffic improves performance, and that biutility search — a semidistributed search algorithm that is widely applicable — substantially outperforms both that distributed RL algorithm and (centralized) simulated annealing in our problem domain. 1
Simulated Annealing and Genetic Algorithms for Shape Detection
, 1996
"... this paper we consider the problem of recognizing simple geometric shapes in a picture corrupted by noise. The algorithmic techniques we use for its solution are simulated annealing, genetic algorithms and a constructive method based on noise filtering. Simulated annealing is a powerful stochastic t ..."
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Cited by 7 (0 self)
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this paper we consider the problem of recognizing simple geometric shapes in a picture corrupted by noise. The algorithmic techniques we use for its solution are simulated annealing, genetic algorithms and a constructive method based on noise filtering. Simulated annealing is a powerful stochastic technique for solving combinatorial optimization problems. One of the main drawbacks of simulated annealing is its high computational requirements. Because of this, a number of parallel implementations have been proposed [1, 5, 8, 10, 17, 23, 30]. In particular, in [10] some problem independent parallel implementations of simulated annealing have been described. Simulated annealing has been proposed to solve image recognition problems [6, 7, 28]. In particular, in [6] a parallel implementation of simulated annealing for the shape detection problem has been proposed. In this paper we present the results obtained using the farming implementation of simulated annealing as it was proposed in [10] for other applications. In Section 2 of this paper, the shape detection problem is formally defined and its representation in terms of a combinatorial optimization problem is described. In Section 3 the general simulated annealing algorithm is described together with some of the parallel implementations proposed for it. In Section 4 we describe a genetic algorithm for the shape detection problem. This algorithm is inherently parallel. In Section 5 we present a constructive heuristic for the shape detection problem which is based on a noise filter. Performance measurements presented in Section 6 for the different algorithms finish the paper. 2 The Shape Detection Problem