15,759 research outputs found
Restricted Dynamic Programming Heuristic for Precedence Constrained Bottleneck Generalized TSP
We develop a restricted dynamical programming heuristic for a complicated traveling salesman problem: a) cities are grouped into clusters, resp. Generalized TSP; b) precedence constraints are imposed on the order of visiting the clusters, resp. Precedence Constrained TSP; c) the costs of moving to the next cluster and doing the required job inside one are aggregated in a minimax manner, resp. Bottleneck TSP; d) all the costs may depend on the sequence of previously visited clusters, resp. Sequence-Dependent TSP or Time Dependent TSP. Such multiplicity of constraints complicates the use of mixed integer-linear programming, while dynamic programming (DP) benefits from them; the latter may be supplemented with a branch-and-bound strategy, which necessitates a “DP-compliant” heuristic. The proposed heuristic always yields a feasible solution, which is not always the case with heuristics, and its precision may be tuned until it becomes the exact DP
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
A Parallel Tree code for large Nbody simulation: dynamic load balance and data distribution on CRAY T3D system
N-body algorithms for long-range unscreened interactions like gravity belong
to a class of highly irregular problems whose optimal solution is a challenging
task for present-day massively parallel computers. In this paper we describe a
strategy for optimal memory and work distribution which we have applied to our
parallel implementation of the Barnes & Hut (1986) recursive tree scheme on a
Cray T3D using the CRAFT programming environment. We have performed a series of
tests to find an " optimal data distribution " in the T3D memory, and to
identify a strategy for the " Dynamic Load Balance " in order to obtain good
performances when running large simulations (more than 10 million particles).
The results of tests show that the step duration depends on two main factors:
the data locality and the T3D network contention. Increasing data locality we
are able to minimize the step duration if the closest bodies (direct
interaction) tend to be located in the same PE local memory (contiguous block
subdivison, high granularity), whereas the tree properties have a fine grain
distribution. In a very large simulation, due to network contention, an
unbalanced load arises. To remedy this we have devised an automatic work
redistribution mechanism which provided a good Dynamic Load Balance at the
price of an insignificant overhead.Comment: 16 pages with 11 figures included, (Latex, elsart.style). Accepted by
Computer Physics Communication
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