345 research outputs found
Fast Ant Colony Optimization on Runtime Reconfigurable Processor Arrays
Ant Colony Optimization (ACO) is a metaheuristic used to solve combinatorial optimization problems. As with other metaheuristics, like evolutionary methods, ACO algorithms often show good optimization behavior but are slow when compared to classical heuristics. Hence, there is a need to find fast implementations for ACO algorithms. In order to allow a fast parallel implementation, we propose several changes to a standard form of ACO algorithms. The main new features are the non-generational approach and the use of a threshold based decision function for the ants. We show that the new algorithm has a good optimization behavior and also allows a fast implementation on reconfigurable processor arrays. This is the first implementation of the ACO approach on a reconfigurable architecture. The running time of the algorithm is quasi-linear in the problem size n and the number of ants on a reconfigurable mesh with n2 processors, each provided with only a constant number of memory words
Hyperreconfigurable architectures as flexible control systems
Dynamically reconfigurable architectures or systems are able to reconfigure their function and/or structure to suit changing needs of a computation during run time. The increasing flexibility of modern dynamically reconfigurable systems improves their adaptability but also makes fast reconfiguration difficult because of the large amount of necessary reconfiguration information. However, even when a computation uses this flexibility it will not use it all the time. Therefore, we propose to make the potential for reconfigurationitself reconfigurable. This allows for speeding up reconfiguration operations during phases where only parts of the total flexibility are required. Such architectures are called hyperreconfigurable and use two types of reconfiguration operations: hyperreconfigurations for changing the reconfiguration potential and ordinary reconfigurations for actually configuring a new context for a computation
On solving permutation scheduling problems with ant colony optimization
A new approach for solving permutation scheduling problems with ant colony optimization (ACO) is proposed in this paper. The approach assumes that no precedence constraints between the jobs have to be fulfilled. It is tested with an ACO algorithm for the single-machine total weighted deviation problem. In the new approach the ants allocate the places in the schedule not sequentially, as in the standard approach, but in random order. This leads to a better utilization of the pheromone information. It is shown by experiments that adequate combinations between the standard approach which can profit from list scheduling heuristics and the new approach perform particularly well
Hyperreconfigurable architectures and the partition into hypercontexts problem
Dynamically reconfigurable architectures or systems are able to reconfigure their function and/or structure to suit the changing needs of a computation during run time. The increasing flexibility of modern dynamically reconfigurable systems improves their adaptability to computational needs but also makes fast reconfiguration difficult because of the large amount of reconfiguration information which has to be transferred. However, even when a computation uses this flexibility it will not use it all the time. Therefore, we propose to make the potential for reconfiguration itself reconfigurable. Such architectures are called hyperreconfigurable. Different models of hyperreconfigurable architectures are proposed in this paper. We also study a fundamental problem that emerges on such architectures, namely, to determine for a given computation when and how the potential for reconfiguration should be changed during run time so that the reconfiguration overhead is minimal. It is shown that the general problem is NP-hard but fast polynomial time algorithms are given to solve this problem for special types of hyperreconfigurable architectures. We define two example hyperreconfigurable architectures and illustrate the introduced concepts for corresponding application problems
Hyperreconfigurable architectures for fast run time reconfiguration
Dynamically reconfigurable architectures or systems are able to reconfigure their function and/or structure to suit changing needs of a computation during run time. The increasing flexibility of modern dynamically reconfigurable systems improves their adaptability but also makes fast reconfiguration difficult because of the large amount of necessary reconfiguration information. However, even when a computation uses this flexibility it is not use it all the time. Therefore, we propose to make the potential for reconfiguration itself reconfigurable. This allows for speeding up reconfiguration operations during phases where only parts of the total flexibility are required. Such architectures are called hyperreconfigurable and uses two types of reconfiguration operations: hyperreconfigurations for changing the reconfiguration potential and ordinary reconfigurations for actually configuring a new context for a computation
The Partition into Hypercontexts Problem for Hyperreconfigurable Architectures
Hyperreconfigurable architectures adapt their reconfiguration abilities during run time in order to achieve fast dynamic reconfiguration. Models for such architectures have been proposed that change their ability for reconfiguration during hyperreconfiguration steps and in ordinary reconfiguration steps reconfigure the actual contexts for a computation within the limits that have been set by the last hyperreconfiguration step. In this paper we study algorithmic aspects of how to optimally decide what hyperreconfiguration steps should be done during a computation in order to minimize the total time necessary for hyperreconfiguration and ordinary reconfiguration. It is shown that the general problem is NP-hard but fast polynomial time algorithms are given to solve this problem on different types of hyperreconfigurable architectures. These include newly introduced architectures that use a cache to store hypercontexts. We define an example hyperreconfigurable architecture and illustrate the introduced concepts for three application problems
Combined super-/substring and super-/subsequence problems
Super-/substring problems and super-/subsequence problems are well-known problems in stringology that have applications in a variety of areas, such as manufacturing systems design and molecular biology. Here we investigate the complexity of a new type of such problem that forms a combination of a super-/substring and a super-/subsequence problem. Moreover we introduce different types of minimal superstring and maximal substring problems. In particular, we consider the following problems: given a set L of strings and a string S, (i) find a minimal superstring (or maximal substring) of L that is also a supersequence (or a subsequence) of S, (ii) find a minimal supersequence (or maximal subsequence) of L that is also a superstring (or a substring) of S. In addition some non-super-/non-substring and non-super-/non-subsequence variants are studied. We obtain several NP-hardness or even MAX SNP-hardness results and also identify types of âweak minimalâ superstrings and âweak maximalâ substrings for which (i) is polynomial-time solvable
On Weighting Schemes for Gene Order Analysis
Gene order analysis aims at extracting phylogenetic information from the comparison of the order and orientation of the genes on the genomes of different species. This can be achieved by computing parsimonious rearrangement scenarios, i.e. to determine a sequence of rearrangements events that transforms one given gene order into another such that the sum of weights of the included rearrangement events is minimal. In this sequence only certain types of rearrangements, given by the rearrangement model, are admissible and weights are assigned with respect to the rearrangement type. The choice of a suitable rearrangement model and corresponding weights for the included rearrangement types is important for the meaningful reconstruction. So far the analysis of weighting schemes for gene order analysis has not been considered sufficiently. In this paper weighting schemes for gene order analysis are considered for two
rearrangement models: 1) inversions, transpositions, and inverse
transpositions; 2) inversions, block interchanges, and inverse transpositions. For both rearrangement models we determined properties of the weighting functions that exclude certain types of rearrangements from parsimonious rearrangement scenarios
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